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Article

Long-Term Heavy Metal Pollution Induces Complex Differences in Farmland Topsoil and Rhizosphere Microbial Communities

by
Jing Guo
1,2,
Weili Dou
2,
Zhiwen Liu
2,
Jiaxuan Sun
2,
Duanping Xu
1,2,
Qili Yang
2,*,
Gang Lv
2 and
Dongli Wang
2
1
College of Mining, Liaoning Technical University, 47 Zhonghua Road, Fuxin 123000, China
2
College of Environmental Science and Engineering, Liaoning Technical University, 47 Zhonghua Road, Fuxin 123000, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(24), 16598; https://doi.org/10.3390/su152416598
Submission received: 19 October 2023 / Revised: 30 November 2023 / Accepted: 30 November 2023 / Published: 6 December 2023
(This article belongs to the Special Issue Environmental Microbiology and Biotechnology)
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Abstract

:
The microbial effect of long-term heavy metal pollution on farmland remains unclear. Here, we investigated microbial (bacterial and fungal) communities in topsoil and rhizosphere samples with heavy metal (Cd, Cu, Pb, and Zn) pollution from four different types of tillage plots around an abandoned zinc smelter set up 85 years ago and analyzed the complex relationship between microorganisms, plants, and heavy metals (HMs) in soil to guide strategies for further soil remediation measures. The abundance and diversity index results showed that the bacterial and fungal diversities of the four plots were significantly different. Meanwhile, correlation analysis of the microbial communities and HMs showed that bacteria Pseudomonas and fungi Chaetothyriales and Fusarium had a good tolerance for HM pollution, but bacteria Vicinamibacteraceae, JG30_KF_CM45, RB41, Gaiella, MB-A2-108, 67-14, and Microvirga, and fungi Glomerellales, Hypocreales, Chaetomium, and Mortierella all showed indications of being sensitive to HM toxicity. Our structural equation model (SEM) attributed the inhibition of Zn and the promotion of Cd to bacterial diversity, attributed a weak inhibition of Cd to fungal diversity, and revealed the effects of the tillage type on these diversities.

1. Introduction

Heavy metals (HMs) have long been the main pollutants investigated in environmental research because of their serious toxicity. They are inevitably discharged into the environment during the processes of human resource development and utilization, resulting in short-term and long-term pollution and health-related impacts [1,2]. The high toxicity, long-lasting persistence, and non-biodegradability of HM contamination in soils, especially agricultural soils [3,4,5], could impact the surface water, groundwater, and eventually enter the food chain, risking human health [1,6,7]. Heavy metals in soil are difficult for microorganisms to utilize [8,9]. Meanwhile, they can be concentrated and even converted into more toxic forms by organisms in the soil. This could change the soil’s physicochemical properties, pollute the soil’s water system through rainwater leaching, and lead to soil degradation [10,11]. Meanwhile, it affects the abundance and diversity of microorganisms and the structure of the microbial community in the soil [12,13] and causes HM bioaccumulation and biomagnification in living systems [14,15].
Microorganisms play a vital role in the migration [16], transformation [17], and cycling of metals in soil. Soil microorganisms may mobilize metals to drive their migration or immobilize them via adsorption, complexation, and precipitation to slow their migration and reduce their toxicity. Microorganism-assisted remediation has been proven to be an efficient strategy for heavy metal decontamination due to its economic feasibility, eco-friendly effects, and sustainability; however, this remediation process can be very slow [18]. Li et al. [19] demonstrated that microbial community compositions varied among depth layers in a typical Pb/Zn smelting area, and Proteobacteria was the most dominant phylum across the different depth layers. Meanwhile, recent studies have also been conducted to investigate the interrelationship between heavy metals and soil microbial communities in smelting sites, which provide scientific guidance for developing an innovative bioremediation method to reduce heavy metal contamination. Disi et al. [20] reported that four Bacillus and three Pseudomonas strains exhibited the capability to remove 70–80% of the heavy metals in soil because the adaptation of these bacterial strains was at the same level as the cells’ structure and/or their exopolymeric substances, which immobilizes the heavy metals and reduces their toxicity, allowing the growth of the Bacillus and Pseudomonas. Kou et al. [21] suggested that Actinobacteriota, Proteobacteria, Chloroflexi, Acidobacteriota, and Gemmatimonadota were the bacterial taxa most resistant to heavy metal stress at gangue sites; in addition to heavy metals, the soil’s nitrogen (N), phosphorus (P), and total organic carbon (TOC) contents also affected the composition of the bacterial communities, with TOC having the strongest effect, followed by N, and then the soil organic matter (SOM). Xie et al. [22] identified a Cd-resistant fungal strain, Penicillium janthinellum ZZ-2, and assessed its potential to improve plant growth by reducing Cd-toxicity. Therefore, improving the interactions between resistant and beneficial rhizosphere soil microorganisms and heavy metal-tolerant plants can enhance plant biomass and tolerance against heavy metals for the eco-friendly remediation of HM-contaminated farmland.
This work was conducted in a typical agricultural region severely impacted by a sizeable zinc smelter in the city of Huludao in the western Liaoning province. Huludao City, belonging to the Liaoning Coastal Economic Zone, is one of the most important lead–zinc mineral habitats in China [23]. The Huludao Zinc Plant (HZP), which is the largest Zn smeltery in Asia, was constructed in 1937 [24]. Related studies have focused on the bioavailability and horizontal and vertical distribution of heavy metals close to this smelter [25,26].
The overall objectives of this study are (1) to investigate the variation of bacteria and fungus taxa in farmlands utilized for different kinds of cultivation with heavy metal pollution and (2) to elucidate the potential of these microorganisms to adapt to the toxicity of heavy metals. This could provide a deeper understanding of the roles of microorganisms in the HZP-polluted farmland and guide a strategy for the microbial detoxification of HM-contaminated soil.

2. Materials and Methods

2.1. Sampling Site and Sample Collection

The HZP is a non-ferrous metal smelter state-owned enterprise and is the largest Zn smeltery in Asia. It was constructed in 1937 and is located at 120°56′18.2″ E, 40°43′0.8″ N. In this research, we set up four groups of plots based on their different cultivation/utilization characteristics. Three plots were situated at a farming area in the downwind direction of the abandoned original HZP sites in the northeast of China (Huludao, Liaoning province; 120°54′6.9″–120°55′58.7″ E, 40°43′2.6″–40°44′15.7″ N), which were selected for soil sampling as a potentially toxic HM pollution hotspot. A chimney of the abandoned zinc smelting plant stands at a distance of about 100 m southwest of the research area, and a small river separates them from each other. Soil samples were collected from three types of farmlands, including a cornfield (with five years of planting history), a sunflower oil field (newly assarted), and an uncultivated area of land, marked as C, SF, and U, respectively (Figure 1). Specifically, the top 20 cm soil samples were collected from the abovementioned farmlands and labeled as C_S, SF_S, and U_S, respectively. At the same time, rhizosphere soils were collected from 0–15 cm soil adhering to plant roots, C_R was corn rhizosphere soil from Corn plot, SF_1_R and SF_2_R were oil sunflower rhizosphere soil from Oil-SF plots (marked with red spot). Plants in SF_2 were obviously larger than those in the SF_1 plot. Additionally, the fourth plot was selected as a reference place (CK group), which was a cornfield ten km away from the research area westward. Its surface soil was marked as CK_S, and CK_R represents rhizosphere soil. All soil samples were collected randomly, homogenized, sealed with plastic bags, and transported to a laboratory with an ice tank. After that, each sample was divided into two parts; one part for the microorganism experiment was stored in a freezer, and the other part was air-dried for chemical and metal analyses.

Determination of Soil Fertility Parameters

The soil samples were air-dried, ground, and passed through a 0.15 mm sieve in the laboratory to remove stones and roots. K2Cr2O7 oxidation-spectrophotometry was used to determine soil total organic carbon (TOC) [27]. Total P (TP) was evaluated via the ammonium molybdate stannous chloride method in a colorimetric assay [28], and the Kjedahl digestion approach was employed to measure total N (TN) [29].

2.2. Metal(loid)s Contents Analysis

The air-dried soil samples were ground and passed through a 0.85 mm sieve to remove stones and roots and then further ground to <0.15 mm. Microwave digestion with HNO3-HCl-HF, which lasted 55 min at 180 °C, was used to extract metal(loid)s. The digested soil and water liquid were subjected to inductively coupled plasma mass spectrometry (ICP-MS) after filtering through a 0.45 μm membrane. All the measurements, including TOC, TN, TP, and metal(loid) contents, were conducted in triplicates, and the results were expressed as mean ± S.D.

2.3. Microbial Communities Analysis of Topsoil and Rhizosphere

DNA extraction, qPCR, and high-throughput sequencing were processed by Majorbio BioPharm Technology Co., Ltd. (Shanghai, China). More details are shown in Supplementary Material (File S1). For alpha diversity analysis, the Sobs index and Shannon index were calculated to estimate community richness and diversity. The Sobs index was the observed richness (Operational Taxonomic Units, OTU). The Shannon index, which could assess the richness and evenness of species composition in the sample, was calculated using the vegan package. The raw 16S rRNA and ITS gene sequences were deposited in the NCBI GenBank Sequence Read Archive (SRA) with the accession number PRJNA972448.

2.4. Statistical Analysis

All data are presented as mean ± standard error (n = 3). A correlation analysis and one-way ANOVA were conducted using SPSS 25.0 (SPSS Inc., Chicago, IL, USA). The distribution maps were calculated using the ordinary kriging interpolation in ArcGIS version 10.2, and other figures were prepared using Origin 2017 software (Origin Laboratories Inc., San Francisco, CA, USA). A Redundancy Analysis and Heatmap were implied with R (version 4.2.3; vegan, ggrepe1, ggplot2, ggpubr, and eoffice packet) to display the relationships among soil physiochemical properties, microbial abundance, and heavy metal contents. The structural equation model (SEM) was applied to depict the direct and indirect influences of HMs, soil fertility elements, and microbial community characteristics in different plots. Eleven observable variables (TOC, TN, TP, Cd, Cu, Pb, Zn, bacterial diversity, fungi diversity, location, and farming) were considered independent variables, whereas the effects of soil fertility, HMs, and microbial were treated as the dependent variable. The SEM was conducted using R software (version 4.2.3; lavaan and semPlot packet).

3. Results and Discussion

3.1. Soil Properties and HM Contents

The soil TP, TN, and TOC contents ranged from 242.00 ± 2.41 to 430.71 ± 3.27 mg/kg, 0.91 ± 0.01 to 2.05 ± 0.05 g/kg, and 0.58 ± 0.06% to 2.39 ± 0.08%, respectively. SF_S soil showed the highest content of TP and TOC, but CK_R had the lowest content of TOC, and SF_2_R had the lowest content of TP. For TN, the highest value was in SF_2_R, but the lowest value was in U_S.
Total Cd, Cu, Pb, and Zn contents of the soils in contaminated and uncontaminated areas were different (Table 1), and significantly higher values appeared in the front area. The total Cd, Cu, Pb, and Zn contents of the soils also changed with different plot types and settings.

3.2. Soil Microbial Community Structure and Distribution

A total of 6634 OTUs of bacteria and 3244 OTUs of fungus were acquired from the 1,248,879 high-quality bacteria reads, and the 1,251,358 high-quality fungus reads of 24 samples.
As shown in the biodiversity index (Table 2) and Venn plot of soil microbial composition (Figure 2a), there were significant differences in microbial bacteria and fungus community richness among farming modes and HM contaminations. The lowest richness (bacterial obs and Shannon = 1864 and 5.89; fungi sobs = 401.67) and genus number (bacteria = 572 and fungi = 215) were observed in the U group (U_S) bacteria and fungus, while the highest Shannon index was found in the CK group (average value 6.34 for bacteria and 4.72 for fungus). The fungi genus number was also the highest (449) in the CK group, but its bacteria genus number (844) was at a high level with the SF group (846). Besides these, in the SF group, the SF_2_R sample had obviously (p < 0.01) lower values in sobs and Shannon than others for both bacteria and fungus. An obvious difference (p < 0.05) of unique genus between bacteria and fungus could be found. By comparing the alpha diversity of the surface soil and the rhizosphere soil microbe in each group (different farming plots), little difference between surface soil and rhizosphere in the CK and the C group was viewed. Whereas, in the SF group, surface soil showed the highest sobs and Shannon value in both bacteria and fungus than rhizosphere soil, and SF_2_R (rhizosphere soil from 2# oil sunflower) suggested the lowest sobs and Shannon value.
The results of microbial α diversity analysis (Table 2 and Figure 2a) also showed that the microbial community diversity index of agricultural land (C and SF group) around the abandoned zinc smelter was significantly higher (p < 0.01) than wasteland (U group). At the same time, there was no significant difference between the soil microbial community in the polluted area and the non-polluted area, but the microbial species richness (Figure 3) of the cornfield (C group) and the oil-sunflower field (SF group) in the polluted area was higher than that in the non-polluted area (CK group), and the number of species was greater. The low abundance of microbial species in oil-sunflower fields may be related to heavy metal pollution concentration in oil-sunflower fields. A large number of studies have shown that the toxic effect of high concentrations of heavy metals can strongly stress and inhibit the physiological and metabolic process of soil microorganisms, thus causing changes in the abundance and function of the soil microbial primary population [21,30].
There were abundant microbes in our soil samples. A total of 1011 bacteria and 594 fungus genera were classified using the Silva and Unite library. The major bacteria genera were Pseudomonas, KD4-96, Vicinamibacteraceae, JG30-KF-CM45, RB41, Gaiella, MB-A2-108, 67-14, Bacillus, Blastococcus, Microvirga, Nocardioides, TK10, and Rubrobacter, and the major fungi genera included Sordariaceae, Glomerellales, Chaetomium, Fusicolla, Chaetothyriales, Fusarium, Solicoccozyma, Mortierella, and Hypocreales (Figure 2b).
In the CK group soil, the community abundances of only the bacterial genus Pseudomonas (3.37%) and the fungus genus Chaetothyriales (0.10%) were the lowest. But, the community’s abundance of bacterial genera (JG30-KF-CM45 (4.17%), Gaiella (2.29%), RB41 (1.88%), Blastococcus (2.32%), and Rubrobacter (1.98%)) and fungi genera (Glomerellales (4.71%) and Hypocreales (3.52%)) were in the highest level than other tillage type plots (C, SF, and U) abounding zinc smelting plants. In contrast, from the U group plot (uncultured plot), the microbial communities abundance of JG30-KF-CM45 (1.05%), Gaiella (0.86%), RB41 (0.14%), Blastococcus (0.73%), Rubrobacter (0.25%), Glomerellales (0.02%), and Hypocreales (0.04%) were the lowest. As the maximum HMs contents were in the U group soil and the minimum contents were in the CK group soil, those microbial genera abundance may have some negative relationships with HMs concentration. Besides these, the abundance of Vicinamibacteraceae, Microvirga, and Sordariaceae from U also showed an obviously lower level (p < 0.05) than those from CK, C, and SF group soil; it seemed to have a correlation with HMs. In contrast, bacterial genera Pseudomonas (8.79%) and KD4-96 (5.97%), and fungi genera Fusarium (8.14%), Solicoccozyma (7.72%), Fusicolla (4.22%), and Chaetothyriales (2.90%) performed the highest abundance in the U group and the lowest proportion in the CK group. This reflected that these microbial genera had better survival status in the plots with a high occurrence of heavy metals in the study area. In other words, those microbial genera had some quite good resistance to heavy metal toxicity. In addition, the high abundance of MB-A2-108, Bacillus, and Mortierella (4.06%, 1.84%, and 19.74%, p < 0.05) and the low proportion of TK10 (0.70%, p < 0.05) from the C group soil both seemed to suggest that their microbial composition was significantly affected by corn tillage. In the SF group, Blastococcus, Microvirga, Rubrobacter, and Sordariaceae’s high proportions (1.22%, 1.73%, 0.98%, and 4.39%) may imply that the oil-sunflower planting process could raise the tolerance or resistance of HMs to promote these communities’ developing.

3.3. Relationship between Microbial Communities and Plot Tillage Type

Relationships between microbial communities and plot tillage type were analyzed according to the Hierarchical clustering tree of bacteria and fungus community composition at the genus level, the classification of microbial community highly corresponding to that of soil type group. The soil microorganisms of the C group and the CK group were both under a corn cultivation environment, which shows the closest clustering characteristics in bacterial communities. The closing clustering relationship is also presented in fungi communities. The other two groups, SF and U, showed a relatively farther clustering with the corn cultivation environment. Among the subgroups, the SF_2 group had the farthest relationship in both bacteria and fungi communities.
As the soil is under a corn cultivation environment, the microbial communities of these two groups are at a higher level than that of the SF group under an oil-sunflower cultivation environment, and they constitute analogical characteristics. At the higher level, the difference in a microbial cluster between the U_S group and the other groups obviously reflected the obvious difference in the microbial community between cultivated soil and non-cultivated soil. At the same time, the composition richness and uniformity of microbial community in the SF_2_R soil of the SF group are higher. This is considered to be more conducive to the stability and sustainable functioning of the microbial community. Fungus genus-level clustering results also show good correspondence with sample grouping. Groups C_S and C_R were clustered to the C group, groups CK_R and CK_S to the CK group, and group U_S to the U group. In the SF group, SF_S and SF_1_R samples showed a closer clustering relationship between groups, while the SF_2_R group showed significantly different fungal community composition from other groups.
In general, the characteristics of the microbial community were in good correspondence with the tillage types of the plots in the sampling sites. Except for the SF_2_R sample in the Oil-SF group, the composition of bacteria and fungi in the samples in the plots of each group showed obvious clustering similarity. The microbial community in the rhizosphere soil of oil sunflower cultivated land represented by the SF_2_R group was significantly different from that in the topsoil of this type of land SF_S group and another nearby oil sunflower crop. Field investigation (site photos in Figure 1) also showed that the growth status of oil sunflower plants in plot SF_2_R was significantly better than that in plot SF_1_R, and the two plots were adjacent to each other. Heavy metals, C, and N levels were similar, and P in plot SF_2_R was about 40% lower than that in the other two plots. These results suggested that the particularity of microbial community composition in the SF_2_R group was either related to plant type or P content. In general, there was nearly no difference in microbial community composition between surface soil and rhizosphere soil in each type of tillage plot.

3.4. Microbial Composition Difference between Uncontaminated Plot (UCT) and Contaminated Plot by Smelter (CT)

In order to compare the composition characteristics of soil microorganisms in the plots affected by heavy metal pollution from HZP and the reference plots that were not polluted, C, SF, and U were combined into the CT group of polluted plots, and the CK group was corresponding to the UCT group of unpolluted plots. By comparing the microbial population composition of the CT group and UCT group, the bacterial community composition of different groups is significantly different. Among the four phyla with the highest abundance, CT group Proteobacteria is higher than Actinobacteriota, and Chloroflexi is higher than Acidobacteriota. At the genus level, Vicinamibacterales with the highest abundance showed little difference between the two groups, while the phyla Pseudomonas and Arthrobacter showed that their relative abundance in the CT group was significantly higher than that in the UCT group (the red segment was larger than the blue segment, Figure S1). In addition, the “others” bacteria community, which represented the total all-community abundance of less than one percent, showed a slightly higher relative abundance in CT than in UCT.
Similarly, after comparing CT and UCT fungal community composition (Figure S1), it was found that, on the level of phylum, the abundance proportions of the top four fungus categories (Ascomycota, Mortierellomycota, Basidiomycota, and unclassified_k__Fungi) were fundamental equilibrium in both two groups. All of them accounted for more than 90% of the total community abundance, indicating that the major fungal phyla were not sensitive to the environmental differences between the HMs polluted and unpolluted areas in the study area. In other words, heavy metal pollution had little effect on most fungal communities in the study area. Comparing CT/UCT ratio in the rest main phyla (Chytridiomycota, Glomeromycota, Olpidiomycota, and others, abundance over 1%), it showed the absolute dominance of UCT in Chytridiomycota and Olpidiomycota, but CT priority in the Glomeromycota (77%) and the proportion of CT group (61%) was higher than that of UCT group (39%). It can be inferred that heavy metal pollution resistance is obvious for Chytridiomycota and Olpidiomycota at the phylum level, but Glomeromycota was more susceptible to heavy metal pollution resistance. At the same time, the comparison between CT and UCT fungal community composition of genera showed that most of the top ten genera had the same proportion of CT and UCT abundance. There were clear abundance differences of four fungi genera (Chaetomium, Fusarium, Hypocreales, and Apiotrichum) only during fungi genera composition comparing between CT and UCT. The proportion of UCT in Chaetomium and Fusarium (81% and 64%) was significantly higher than that of CT, but the proportions of Hypocreales and Apiotrichum in CT were absolute advantage (99%), which indicated the fungus of the genus group had good environmental adaptability of heavy metals or resistance, while Chaetomium and Fusarium with dominant UCT components were more sensitive to heavy metal environmental conditions and susceptible to HMs stress. On the whole, the bacteria genus Pseudomonas and some low abundance genera (<1%) and fungi genus Hypocreales showed abundance proportion is CT > UCT, and most other genera were UCT > CT oppositely. This might be relative to the influence of HMs from the abandoned smelter.

3.5. Correlations between Chemical Parameters and Microbial Community

The physiochemical properties factors on microbes were evaluated using the Redundancy Analysis (Figure 4a), and the relationships among soil physiochemical properties, microbial abundance, and heavy metal contents were analyzed using R software and displayed in a heatmap of Pearson correlation analysis (Figure 4b). On the genus level, axis 1 of the RDA plot explained nearly 52.34% of the variation; Axis 2 explained a further 23.55%. Different group soils were dispersed across RDA 1 and RDA 2, which indicated that the microbial community and chemical properties (HMs, TN, TP, and TOC) of soils varied with different soil types.
As can be seen from the heatmap (Figure 4b), bacteria Pseudomonas and fungi Chaetothyriales and Fusarium showed red in the heavy metal block, indicating a positive correlation between microbial community abundance and soil heavy metal content. Chaetothyriales showed a significantly positive correlation with Cd, Pb, and Zn (R = 0.864, 0.766, and 0.830, p < 0.001). Pseudomonas had a significantly positive correlation with Cd (R = 0.434, p < 0.05). Fusarium was significantly positively correlated with Cd (R = 0.574, p < 0.01) and Pb and Zn (R = 0.469, 0.497, p < 0.05). In addition, bacteria KD4-96, fungi Sordariaceae, and Solicoccozyma showed a weak positive correlation with Cu (R = 0.290, 0.145, 0.137). In contrast, the abundance of many bacterial and fungal genera showed negative correlations with heavy metals. For example, JG30-KF-CM45 and Hypocreales showed significantly negative correlations with all four heavy metals concerned in this research (p < 0.001). Vicinamibacteraceae, Gaiella, MB-A2-108 had a significantly negative correlation with Cd, Pb, and Zn (p < 0.001), and the former two also had a significant negative correlation with Cu (p < 0.01). Chaetomium had a significantly negative correlation with Zn (R = −0.635, p < 0.001). Bacteria 67-14 and Rubrobacter had a significantly negative correlation with Cu (R = −0.651 and −0.725, p < 0.001). At the same time, the microbial community was also significantly correlated with the basic soil elements such as P, C, and N. For example, bacteria KD4-96, Pseudomonas, and Bacillus all showed a significantly negative correlation with TN (R = −0.792, −0.745, and −0.651, p < 0.001). Microvirga, however, had a significantly positive correlation with microvirga (R = 0.762 and p < 0.001). Bacteria JG30-KF-CM45, Glomerellales, and fungi Hypocreales showed a significantly negative correlation with TOC (R = −0.638, −0.667, and −0.645, p < 0.001), while Chaetothyriales had a significantly positive correlation with TOC (R = 0.770 and p < 0.001). The community abundance of fungi Fusarium, Solicoccozyma, Fusicolla, and bacteria TK10 showed a significantly positive correlation with TP (R = 0.644, 0.701, and 0.804, p < 0.001), but fungi Sordariaceae showed a significantly negative correlation with TP (R = −0.699, p < 0.001).
The positive correlation generally reflects the tolerance and resistance of the microbial community to heavy metals, while the negative correlation reflects the inhibition of the microbial community growth after being contaminated by heavy metals. Some reports suggested that heavy metal-enrichment played a more important role in shaping bacterial diversity [31,32,33]. Pseudomonas was found to possess other plant growth-promoting properties, including P-solubilization, heavy metal detoxification, phosphatases, and siderophore production [34]. Poveda and Eugui [35] mentioned Bacillus or Pseudomonas had a positive effect on crops; their combination offers even greater potential as plant growth promoters and as biocontrol agents. Disi et al. [20] stated that Pseudomonas, as hydrocarbon-degrading bacterial strains, exhibited the highest tolerance to Cu, Cr, Zn, and Ni. In this study, Pseudomonas was the only bacteria genus demonstrating some tolerance to heavy metals, especially Cd. However, Bacillus nearly did not show any correlation with HMs but Zn. For other bacteria, their correlations with HMs were variable in earlier reports. Chun et al. [36] found that KD4-96, Vicinamibacteraceae, Vicinamibacterales, Gemmatimonadaceae, and Gaiella are generally resistant to heavy metal stress. Liu et al. [37] reported that KD4-96 on genus level was positively and significantly correlated with A-Cd of park soils in Shanghai, China. Tapase and Kodam [38] reported that Microvirga resisted much higher concentrations of As (V), Ni, and Cu. Khudur et al. [31] suggested that Rubrobacter was correlated with zinc. Qian et al. [39] found that JG30_KF_CM45 was identified as a major genus in the soil surrounding a smelter, but its negative response to Zn. They also mentioned that Rubrobacter and TK10 (both phylum Chloroflexi) showed a similar response of abundance decreasing as the high level of HMs decreased the pH and CEC of rhizosphere soil. There was few research about the correlation between functional bacteria genera MB-A2-108, 67-14, and HMs. Our analysis showed bacteria Vicinamibacteraceae, JG30_KF_CM45, RB41, Gaiella, MB-A2-108, 67-14, and Microvirga all represented they were inhibited by HMs. Fungus have been reported to be more tolerant of metal stress than bacteria because of their strategies and different mechanisms. Li et al. [40] and Hassan et al. [41] reported that Chaetomium showed strong tolerance to multiple heavy metals. Fusarium and Mortierella were reported as the metal-resistant fungal genera [36,42]. Yao et al. [43] reported that fusarium, hypocreales, and penicillium were positively correlated with the soil’s Cd, Pb, and Zn content. Zhang et al. [2] reported that Chaetothyriales and Glomerellales were enriched in Cu-contaminated soils. However, in a recent report, Yan [9] indicated that fungi Mortierella and Fusarium had negatively correlated with the heavy metals or positively correlated with organic pollution in soil. Our research indicated that Chaetothyriales and Fusicolla could tolerant HMs of soil around the smelter, which abundance also increased under high content of P and C. But, Glomerellales, Hypocreales, Chaetomium, and Mortierella showed HMs inhibited their developing. Otherwise, our results showed some microbes (such as bacteria Pseudomonas, Vicinamibacteraceae, JG30_KF_CM45, MB-A2-108, 67-14 and Microvirga and fungi Chaetothyriales, Fusicolla, Glomerellales, Hypocreales, Chaetomium, and Mortierella) tolerance or inhibitory to HMs took effect when soil fertility element P, C and/or N changed significantly. This indicated that HMs might affect soil fertility element’s variation by microbe metabolism. Qian et al. [39] reported that HMs accelerated the carbon cycle in phylloplane derived by JG30-KF-CM45 or fertility element (as an energy source for metabolic processes) supported microbe communities development to resist high HM stress.

3.6. Relationship of Soil Fertility Element, HMs and Microbial Community

A structural equation model (SEM) was applied to depict the direct and indirect influences of HMs, soil fertility elements, and microbial communities’ characteristics in the plots (Figure 5). The direct effects of HMs Zn and Cd on bacterial diversity were the most significant, and Cd promoted bacterial diversity, but Zn-induced bacterial diversity decreased. Meanwhile, SEM showed fungi diversity, in some ways, was mainly affected by the inhibitory effect of Cd. So, in the plots around the abandoned smelter we studied, bacterial communities were more sensitive than fungi communities. Fungi were resistant or less sensitive to heavy metal contamination than bacteria because they could generate mycorrhizal and interconnect with plant roots to enhance their resistance to heavy metals [44,45,46]. On the whole, there was a negative relationship between HMs and microbial community characteristics, which indicated a restriction of each other. Additionally, Farming (tillage type) also had a certain direct effect on both bacteria and fungi community diversity of plots soil. “Location” represented the different position relations of the CT area and UCT area, and SEM revealed Cd and Pb occurrence, to some extent, had a direct effect on the microbial community characteristics of areas with and without smelter HMs pollution. Among Soil fertility elements, TOC exerted a direct impact on the fertility-HMs-microbial compound system of plots of soil, while TP exerted a potential impact on the complex system. The soil system’s carbon cycle and transformation co-occur frequently with microbial metabolism, such as carbon source utilization or degradation of organic matter [2,39,47]. During microbial metabolism and community structure variation, the dissolution kinetics of inorganic phosphorus and alkaline phosphatase activity changed strongly as the phosphorus dissolution ability of some microorganisms was developed [40,48].
Soil microbial communities adapted to heavy metal contaminated sites normally had better tolerance to heavy metal pollution, among which there might be some microbial communities with soil environmental remediation potential. The main two processes were as follows: some microorganisms could achieve the attenuated transformation of heavy metals [49,50,51], and some could promote the expulsion of heavy metals from the soil system [15,52,53]. On the one hand, microorganisms could achieve detoxification of heavy metals by adsorption/solidification or changing the valence state of heavy metals [38,54]. On the other hand, microorganisms metabolization caused the transformation of heavy metals form, altered the heavy metals in soil into bioavailable to crops, and promoted their transfer to the above-ground tissue parts of crops (stems, leaves, fruits) so as to remove them from the soil system through the harvesting process [15,55]. In some cases, the heavy metal phytoremediation effect of enriched and hyper-enriched plants could be promoted by coexisting with beneficial microorganisms, such as Actinobacteria (Streptomyces pactum Act12) and Firmicutes (Bacillus subtilis and Bacillus licheniformis) enhanced phytoextraction of Cd, Cu, Pb, and Zn by Brassica juncea (L.) Czern. and endophyte PE31 Bacillus cereus inoculation enhanced Cd uptake of P. acinosa. In this study, the high diversity and abundance of microorganisms in the rhizosphere soil of oil sunflower may indicate the relatively rich and tolerant community assemblage range of soil microbial restoration in the study area and indicate the direction for further screening of attenuated microorganisms with high tolerance and efficiency. At the same time, the relationship between soil fertility factors and microorganisms also provides a theoretical basis for how to use the fertility factors effectively in the process of playing the role of potential heavy metal remediation microorganisms.

4. Conclusions

The long-term production process of the smelter caused serious HMs pollution to the surrounding soil, and it still had a great influence on the composition of the soil microbial community around the abandoned smelter by now. Bacterial and fungi diversity in the four types of soil plots showed significant differences; the abundance of microbial species in the soil of crop-tillage plots was significantly higher than that of untillage plots, and the corn plot soil in HMs contaminated areas had more abundant microbial species than uncontaminated area soil. Bacteria Pseudomonas and fungi Chaetothyriales and Fusarium showed good tolerance to HMs pollution, and bacteria Vicinamibacteraceae, JG30_KF_CM45, RB41, Gaiella, MB-A2-108, 67-14, and Microvirga, and fungi Glomerellales, Hypocreales, Chaetomium and Mortierella all had sensitive indication to HMs toxicity. SEM analysis revealed the inhibition of Zn and the promotion of Cd to bacterial diversity, a weak inhibition of Cd to fungi diversity, and the effect on diversity caused by tillage type. Considering the complex relationship between microorganisms, plants, and heavy metals, this study provides an important basis for further soil remediation measures, such as microbial remediation, phytoremediation, and microbial-phytoremediation.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su152416598/s1, File S1 (16S and ITS rRNA analysis) and Figure S1 (Circos). References [56,57,58,59,60] are cited in the Supplementary Materials.

Author Contributions

All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by J.G., W.D., Z.L., Q.Y. and D.X. The first draft of the manuscript was written by J.G. and amended by G.L. and D.W. All authors commented on previous versions of the manuscript. Text formatting was adjusted by J.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Key Research and Development Program of China: Project of Site Complex Pollution Control Technology and Integrated Demonstration in Heavy Industrial Zone, Northeast China [2019YFC180380103]; National Natural Science Foundation of China (Grant numbers [41501217]; and Double first-class Discipline innovation team Construction project of Liaoning Technical University [LNTU20TD-24].

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data included in this study are available upon request by contact with the corresponding author.

Acknowledgments

The authors would like to thank all the laboratory persons as well as colleagues for providing their help during sampling and analysis. The authors appreciate the reviewers and editors for their assistance in the development and improvement of this paper.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Soil sampling site location.
Figure 1. Soil sampling site location.
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Figure 2. Comparing of soil microbial composition from different tillage type plots at genus level: (a) Venn, (b) dominated communities. (1) bacteria and (2) fungi.
Figure 2. Comparing of soil microbial composition from different tillage type plots at genus level: (a) Venn, (b) dominated communities. (1) bacteria and (2) fungi.
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Figure 3. Composition of dominant genera of bacteria and fungi and Hierarchical clustering tree results: (a) bacteria, (b) fungi; relative abundance over 1%.
Figure 3. Composition of dominant genera of bacteria and fungi and Hierarchical clustering tree results: (a) bacteria, (b) fungi; relative abundance over 1%.
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Figure 4. Relationship of bacterial community distribution and dominant bacterial taxa (at the genus level) with the key environmental factors in the soils from the four types of plots in the research area by (a) RDA and (b) Pearson correlation analysis (*: p < 0.05; **: p < 0.01; ***: p < 0.001).
Figure 4. Relationship of bacterial community distribution and dominant bacterial taxa (at the genus level) with the key environmental factors in the soils from the four types of plots in the research area by (a) RDA and (b) Pearson correlation analysis (*: p < 0.05; **: p < 0.01; ***: p < 0.001).
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Figure 5. Structure equation model analysis of soil fertility, HMs, and microbial. Arrow widths correspond to standardized path coefficients, and arrow color represents correlation (red is negative; green is positive).
Figure 5. Structure equation model analysis of soil fertility, HMs, and microbial. Arrow widths correspond to standardized path coefficients, and arrow color represents correlation (red is negative; green is positive).
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Table 1. Soil properties and HMs contents of all samples. (HMs: mg/kg; TOC: %; TP: mg/kg; TN: g/kg. Different lowercase letters indicate statistically significant differences between sampling points as determined using a one-way ANOVA with a post hoc Tukey HSD test (p < 0.05)).
Table 1. Soil properties and HMs contents of all samples. (HMs: mg/kg; TOC: %; TP: mg/kg; TN: g/kg. Different lowercase letters indicate statistically significant differences between sampling points as determined using a one-way ANOVA with a post hoc Tukey HSD test (p < 0.05)).
SampleCdCuPbZnTPTOCTN
CK_R1.02 ± 0.12 g54.55 ± 1.57 e57.25 ± 0.82 g122.75 ± 1.17 d369.66 ± 0.02 d0.59 ± 0.08 f1.37 ± 0.11 c
CK_S0.75 ± 0.02 g47.82 ± 0.40 g52.89 ± 1.07 g110.04 ± 3.61 d328.95 ± 0.01 e1.37 ± 0.21 def1.38 ± 0.05 c
SF_1_R47.95 ± 0.44 b51.65 ± 0.90 f469.47 ± 9.67 b2011.53 ± 71.67 a404.81 ± 0.01 c2.4 ± 0.13 bcd1.57 ± 0.04 b
SF_2_R44.23 ± 0.61 d74.06 ± 0.91 b426.46 ± 4.13 c1371.07 ± 34.7 b242 ± 0.01 h2.05 ± 0.18 cde2.05 ± 0.05 a
SF_S46.14 ± 0.57 c64.74 ± 0.25 d364.48 ± 0.79 d1449.19 ± 178.84 b430.71 ± 0.02 a8.9 ± 1.63 a1.21 ± 0.14 d
U_S59.02 ± 1.60 a79.65 ± 0.21 a511.47 ± 9.93 a1902.88 ± 63.22 a406.66 ± 0.01 b3.36 ± 0.07 b0.9 ± 0.08 e
C_R9.13 ± 0.42 f70.88 ± 2.16 c232.55 ± 5.75 f339.37 ± 10.31 c293.8 ± 0.02 g2.8 ± 0.17 bc0.91 ± 0.01 e
C_S11.99 ± 0.07 e52.52 ± 1.26 f342.71 ± 10.84 e422.22 ± 4.13 c325.25 ± 0.01 f1.21 ± 0.13 ef1.08 ± 0.07 d
(Sample designations explanation: CK/SF/U/C corresponding to corn plot in reference area/oil_sunflower plot/uncultivated plot/corn plot, R representing rhizosphere soil and S representing surface soil). Note: Each value represents the mean ± standard (n = 3).
Table 2. Alpha diversity index of all samples.
Table 2. Alpha diversity index of all samples.
Sample/EstimatorsBacteriaFungus
SobsShannonSobsShannon
CKCK_R2438.67 ± 17.79 bc6.37 ± 0.01 a817 ± 31.32 b4.7 ± 0.15 ab
CK_S2305 ± 110.12 c6.31 ± 0.05 ab769.67 ± 22.68 bc4.73 ± 0.1 ab
SFSF_1_R2297 ± 93.74 c6.14 ± 0.03 b729 ± 5.57 cd4.35 ± 0.05 b
SF_2_R1979.67 ± 231.75 d5.71 ± 0.30 c441.67 ± 78.31 e2.78 ± 0.43 d
SF_S2484.33 ± 61.04 bc6.26 ± 0.03 ab647.67 ± 29.26 d4.41 ± 0.12 b
UU_S1864 ± 100.13 d5.89 ± 0.05 c401.67 ± 29.67 e3.94 ± 0.20 c
CC_R2623.67 ± 94.32 ab6.26 ± 0.07 ab945 ± 46.29 a4.83 ± 0.22 a
C_S2743.67 ± 40.2 a6.36 ± 0.02 ab823.33 ± 78.36 b4.54 ± 0.15 ab
Note: Each value represents the mean ± standard (n = 3). Different letters within the column indicate that values are significantly different at the p < 0.05 level.
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Guo, J.; Dou, W.; Liu, Z.; Sun, J.; Xu, D.; Yang, Q.; Lv, G.; Wang, D. Long-Term Heavy Metal Pollution Induces Complex Differences in Farmland Topsoil and Rhizosphere Microbial Communities. Sustainability 2023, 15, 16598. https://doi.org/10.3390/su152416598

AMA Style

Guo J, Dou W, Liu Z, Sun J, Xu D, Yang Q, Lv G, Wang D. Long-Term Heavy Metal Pollution Induces Complex Differences in Farmland Topsoil and Rhizosphere Microbial Communities. Sustainability. 2023; 15(24):16598. https://doi.org/10.3390/su152416598

Chicago/Turabian Style

Guo, Jing, Weili Dou, Zhiwen Liu, Jiaxuan Sun, Duanping Xu, Qili Yang, Gang Lv, and Dongli Wang. 2023. "Long-Term Heavy Metal Pollution Induces Complex Differences in Farmland Topsoil and Rhizosphere Microbial Communities" Sustainability 15, no. 24: 16598. https://doi.org/10.3390/su152416598

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