Comparative characterization of microbial communities that inhabit arsenic-rich and antimony-rich contaminated sites: Responses to two different contamination conditions

Environmental Pollution 260 (2020) 114052

Contents lists available at ScienceDirect

Environmental Pollution journal homepage: www.elsevier.com/locate/envpol

Comparative characterization of microbial communities that inhabit arsenic-rich and antimony-rich contaminated sites: Responses to two different contamination conditions* Xiaoxu Sun a, c, Tianle Kong a, b, c, Rui Xu a, c, Baoqin Li a, c, Weimin Sun a, c, * a

Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Guangdong Institute of Eco-environmental Science & Technology, Guangzhou, 510650, China School of Environmental Science and Engineering, Guangzhou University, Guangzhou, 510006, China c National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangzhou 510650, China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 12 November 2019 Received in revised form 2 January 2020 Accepted 22 January 2020 Available online 27 January 2020

Due to extensive mining and industrial activities, arsenic (As) and antimony (Sb) contaminations are becoming a global environmental concern. Both As and Sb are toxic and carcinogenic metalloids from the group 15 in the periodic table. Since As and Sb share many similar geochemical properties, it is often assumed that they exert similar environmental pressure on the native microbial communities. This hypothesis, however, still requires further confirmation. In the current study, a systematic comparison of microbial responses to As and Sb contamination were conducted. The results suggested that regular geochemical parameters, such as pH, nitrate, and TOC, were the driving forces for shaping the microbial community. In correspondence, two heavily contaminated groups showed similar microbial community compositions and the same microbial populations were enriched. The interactions between the contaminant fractions (As and Sb related fractions) and the individual OTUs, however, suggested the different and more diverse impacts of As comparing to Sb fractions, with more taxa significantly impacted by As species comparing to Sb species. The identification of the keystone taxa in the heavily contaminated samples revealed a group of microbial populations that could survive in both As and Sb heavily contaminated conditions and may providing critical environmental services to the community. Further investigation of these key microbial populations may provide valuable insights on employing these microorganisms for remediation applications. © 2020 Elsevier Ltd. All rights reserved.

Keywords: Arsenic Antimony Microbial community Bioremediation Co-occurrence network

1. Introduction Arsenic (As) and antimony (Sb) belong to the same group (group 15) in the periodic table and often coexist in the environment (Filella et al., 2007). Both of them are toxic and have been identified as potential carcinogenic substances (Gebel, 1997). It has been reported that both As and Sb share similar chemical properties (Wilson et al., 2004). Contamination by As and Sb has become a worldwide concern due to the potential exposure to populations. However, Sb is comparatively less studied in comparison to As (Frohne et al., 2011). Sb has attracted growing attention recently due to an increasing usage of Sb-related compounds and extensive

* This paper has been recommended for acceptance by Yong Sik Ok. * Corresponding author. 808 Tianyuan Road, Guangzhou, Guangdong, China. E-mail address: [email protected] (W. Sun).

https://doi.org/10.1016/j.envpol.2020.114052 0269-7491/© 2020 Elsevier Ltd. All rights reserved.

Sb mining activities. Among the major Sb producers, China accounts for more than 80% of world Sb production, making it the world’s largest Sb producer and also the victim of Sb contaminations (He et al., 2012). Microbial mediated As and Sb transformations have been investigated extensively and hold the potential to remediate As and Sb contaminations, since the biotransformation could reduce the mobility and toxicity of both As and Sb (Lehr et al., 2007; Li et al., 2016). Understanding the mechanisms of microbial-mediated Sb and As transformations is a prerequisite for effective bioremediation of contaminated sites. In addition, indigenous microbial communities that inhabit As and Sb contaminated sites may provide valuable information regarding the microbial responses to these metalloids. The biogeochemical cycling of As has been extensively studied, and many studies have reported microorganism could oxidize,

2

X. Sun et al. / Environmental Pollution 260 (2020) 114052

reduce, methylate, or demethylate As (Macy et al., 2000; Nguyen and Lee, 2015; Silver and Phung, 2005). Some contaminated sites, such as West Bengal and Mono lake, have been hotspots for As studies (Burgess et al., 2010; Oremland et al., 2004). Some critical biogeochemical As cycling processes were revealed from these sites. For instance, anaerobic metal-reducing bacteria played an important role in the mobilization of arsenic in sediments collected from a contaminated aquifer in West Bengal (Islam et al., 2004). In addition, anaerobic As(III) oxidation coupled with nitrate reduction has been observed in anoxic bottom water from Mono Lake (Oremland et al., 2002). Microbial community analyses have indicated the presence of diverse microbial communities in Sb contaminated habitats that include soil, tailings, and the sediments (Sun et al., 2019b; Wang et al., 2018). However, investigations of the mechanisms of Sb biotransformation are still in their infancy. It was frequently considered that As and Sb share similar chemical behaviors, although this has not always been verified (Kataoka et al., 2018). Therefore, it has been proposed that some microorganisms may also respond similarly to As and Sb. Indeed, arsenic resistance operon can be induced by both As and Sb (Li et al., 2013; Meng et al., 2004). Also, our previous study also indicated the presence of Asrelated genes in the metagenomes of Sb-rich soils (Sun et al., 2018). The metabolism of As and Sb, however, could also mediated by different genes. Although Sb compounds may stimulate genes that encode As resistance and reductions, it has also been reported that Agrobacterium tumefaciens oxidized Sb(III) without As(III) oxidase, suggesting that Agrobacterium spp. may use a different pathway than that used for As(III) oxidation (Li et al., 2015). Therefore, the microbial response to As and Sb may be complicated, and microorganisms may use both similar or different pathways to transform As and Sb. An investigation of the microbial communities that inhabit As and Sb contaminated environments may be an effective way to compare the microbial response to Sb and As. In this study, soil samples are collected from representative As and Sb highly contaminated regions. In addition, adjacent area with relatively lower As and Sb content samples are also collected for comparison purpose. Furthermore, a combination of analytical methods, Illumina sequencing, and statistical analyses are then performed to characterize the indigenous microbial communities that inhabit the As-rich and Sb-rich environments. The overall research goal is to (i) characterize and compare the microbial communities that inhabit the high As samples and the high Sb samples; (ii) elucidate the impact of As and Sb on the microbial community structures; and (iii) investigate the interactions between the microbial communities and the geochemical conditions, with a focus on the As and Sb extractable species. This information will provide critical insights into the microbial activities and their contaminant transformation mechanisms, which is necessary for remediation efforts. 2. Materials and methods 2.1. Sample collection The soil samples were collected in January 2018 in Hunan Province, China. The Xikuangshan (XK) site (27.70
N, 111.99
E) represents the largest antimony mine in the world; Shimen (SM) site (29.58
N, 111.38
E) is a major realgar (a-As₄S₄) mine that was relatively close to the XK site. In each site, the highly contaminated samples were collected within the mining area or near the smelting factories; the less contaminated samples were taken outside the mining area near agricultural fields. A total of 61 soil samples (approximately 100 g per sample) were collected for the four

groups (12 samples from highly contaminated SM samples [SMH], 12 samples from low contaminated SM samples [SML], 14 samples highly contaminated XK samples [XKH], and 15 samples from less contaminated XK samples [XKL]). To reduce the impact of anthropogenic activities, the top layers (~1 cm) were removed. Also, to avoid the bias induced by plants, the sample collected were away from plant root zone. The collected samples were immediately put on ice during transfer and stored at 20
C until further process. 2.2. Geochemical analysis For geochemical analysis, soil samples were air dried and grounded to pass a 200-mesh sieve. Soil pH was measured by mixing the samples with 25 ml distilled water, shaken for 5 min and equilibrium for 20 min. The supernatant was measured by a HACH HQ30d pH meter (Colorado, US). Total organic carbon (TOC) was measured on an elemental analyzer (Vario MACRO cube, Hanau, Germany) after digest with 5% HCl. Nitrate and sulfate were measured using an ion chromatography (DIONEX ICS-40, CA, USA). Iron concentrations were determined spectrophotometrically on a UV-9000s (METASH, Shanghai, China) with addition of 1,10phenanthroline following the published literature (Tamura et al., 1974; Yu et al., 2016). The total concentrations of the contaminants (Astot and Sbtot) were first digested with a mixed solution of 5:1 (v/v) HNO3 and HF. The products were then analyzed using an Atomic Fluorescence Spectrometer (AFS-920, Jitian, Beijing, China). The bioavailable species of the contaminant (Kim et al., 2014) were analyzed using a modified series extraction method adapted from Wenzel et al. (2001) on an Atomic Fluorescence Spectrometry (AFS920, Jitian, Beijing, China). Briefly, for the nonspecifically absorbed As and Sb (Asexe and Sbexe, which are fractions that could be easily exchanged with ions in the liquid phase), 1 g soil was mixed with 10 ml 0.05 M (NH4)SO4 at 20
C for 4 h. Then, the remaining filtrate was further extracted with 10 ml 0.05 M NH4H2PO4 after incubating at 20
C for 16 h for specifically absorbed As and Sb (Assrp and Sbsrp, which represent the fractions that are specifically absorbed to the mineral surfaces). The digested filtrate was then centrifuged and filtered before analyzation. The above four fractions (Asexe, Sbexe, Assrp, and Sbsrp) are considered as bioavailable fractions for their higher accessibility to humans and microorganisms (Rodriguez et al., 2003). 2.3. DNA extraction and sequence analysis The DNeasy Powersoil kit (Qiagen, Dresden, Germany) was used for the soil DNA extraction. For each sample, 0.25 g soil sample was used according to the manufacture’s protocol. The V4 hypervariable region of the 16S rRNA was amplified with the primer set 515F/ 806R (Caporaso et al., 2010). The amplicons were further processed and sequenced on an Illumina MiSeq at the Novogene Co. Ltd. (Beijing, China) following the protocols (Bybee et al., 2011; Green et al., 2015; Herbold et al., 2015). The bioinformatic analysis of the sequencing libraries were processed using QIIME (Caporaso et al., 2010) and UPARSE pipeline (Edgar, 2013). The paired-end sequences were first merged using PEAR. The merged sequences were further processed in QIIME for quality filtering and dereplication. The chimeras were removed in VSEARCH using both de novo and reference-based methods. The sequences were clustered into operational taxonomic units (OTUs) by 97% sequence similarity. The representative OTUs was assigned against Greengenes Database using RDP classifier (DeSantis et al., 2006). The sequences were submitted to the NCBI database under the project number PRJNA588346. Alpha diversity was calculated using sequencedepth rarified libraries in difference indices, including Observed Species, Shannon, Simpson, Chao1, and ACE. The statistical

X. Sun et al. / Environmental Pollution 260 (2020) 114052

comparison between different groups were conducted using student’s T-test. Community beta diversity was calculated in BrayCurtis distance and plotted in a NMDS plot using R package ‘vegan’ (Oksanen et al., 2018). The Canonical Correspondence Analysis (CCA) and multivariate analysis of variance (PERMANOVA) were also conducted using R package ‘vegan’. The Random Forest (RF) predictions on the impact of environmental parameters on the microbial alpha diversity was calculated using R package ‘randomForest’ (Liaw et al., 2018). The co-occurrence network was calculated in Cytoscape (Shannon et al., 2003). The cutoff used for environmental-biological interactions was Spearman correlation > |0.6| and p < 0.05, while the cutoff for biological interactions was Spearman correlation > |0.8| and p < 0.05. The results were visualized in Gephi (Newman, 2006).

3. Results 3.1. As- and Sb-related extractable fractions in the two sites Both the total As/Sb (Astot/Sbtot) and the bioavailable As/Sb fractions (Asexe, Assr/Sbexe, Sbsrp) were measured from the four groups (SMH was defined as the As highly contaminated zone; SML as the As less contaminated zone; XKH as the Sb highly contaminated zone, XKL as the Sb less contaminated zone) in this study (Fig. 1). All of the above-mentioned As and Sb related fractions were considered as contaminant fractions in this study (Sun et al., 2017). Astot was significantly (p < 0.05, hereafter) higher in the As-mining sites (SMH and SML) than those in the Sb-mining sites (XKL and XKH). Within the SM site, SMH demonstrated a significantly higher Astot concentration compared to SML. In the XK site, there were no differences observed in Astot. Similar patterns were also observed for Sbtot. A significantly higher Sbtot was observed in XKH compared to XKL, while no difference was observed between SMH and SML. The bioavailable fractions (Asexe, Assrp, Sbexe, and Sbsrp) only accounted for a minor portion of the total As and Sb contents. On

3

average, 13.6% of Astot was considered as bioavailable (Asexe þ Assrp), and 9% of Sbtot was considered as bioavailable (Sbexe þ Sbsrp). The distribution of the bioavailable As and Sb fractions was similar to the patterns observed for Astot and Sbtot, with significantly higher Asexe and Assrp found in the SM compared to the XK. However, there were significantly higher Sbexe and Sbsrp in the XK compared to the SM site. 3.2. Other geochemical parameters Other geochemical parameters were measured as well. pH was significantly higher in the less contaminated groups (SML and XKL). The overall pH in all the highly contaminated samples appeared to be acidic (pH < 5.5), while the pH in the uncontaminated samples was circumneutral. Total organic carbon (TOC) was also significantly higher in the less contaminated samples. For instance, the TOC of samples in the XKL was significantly higher than that in the XKH. In addition, the TOC concentrations in the SML samples were also significantly higher compared to those in the SMH. Nitrate also demonstrated significantly higher concentrations in the less contaminated samples than in the heavily contaminated samples. Namely, nitrate in the SML was significantly higher than that in the SMH, while nitrate in the XKL was relatively higher than that in the XKH. Overall, the more contaminated sites in the two areas can be categorized as acidic environments with less nutrients (TOC and Nrelated parameters). 3.3. Microbial community analysis Approximately 1.37 million high-quality sequences were obtained from 61 samples using Illumina Miseq sequencing. A total of 102,217 operational taxonomic units (OTUs) were identified with a 97% sequence similarity. Various alpha diversity indices were calculated from the samples. Microbial communities in the less contaminated groups (SML and XKL) were more diverse than those

Fig. 1. The box plots show the geochemical parameters among the four sample groups. The x-axis are concentration in mg/kg soil.

4

X. Sun et al. / Environmental Pollution 260 (2020) 114052

in the more contaminated groups (SMH and XKH). This was indicated by significantly higher values of all measured alpha diversity indices (Fig. 2). The microbial community composition analysis suggested a heterogeneous distribution of individual taxonomic groups at the phylum to genus levels. At the phylum level, Proteobacteria was the most abundant phylum followed by Acidobacteria and Chloroflexi (Fig. 3). No significant difference was observed in the abundances of Proteobacteria among the four groups, while both Acidobacteria and Chloroflexi were relatively enriched in the two more contaminated groups. Among the other abundant phyla, Gemmatimonadetes, Bacteroidetes, Planctomycetes, and Verrucomicrobia were more abundant in the less contaminated groups. Cyanobacteria and Actinobacteria showed their affinity to geological locations. Cyanobacteria was more abundant in samples taken from the SM, while Actinobacteria was more abundant in samples taken from the XKS. Among the abundant genera, several taxa demonstrated consistently significantly elevated abundances in the contaminated samples (SMH and XKH) compared to the less contaminated samples (SML and XKL) (Figs. S1e2). These taxa included Sphingomonas, Acidothermus, Bryobacter, Candidatus Solibacter, Bradyrhizobium, and Burkholderia-Paraburkholderia. In addition, there were other taxa that demonstrated significantly higher abundances in the less contaminated samples, such as Haliangium, Roseiflexus, and Gaiella. Only four genera, namely, Mucilaginibacter, RB41 of the Acidobacteria, Opitutus, and Cytophaga, demonstrated significant differences between the two heavily contaminated groups (SMH and XKH) (Fig. S3).

3.4. Environment-microbial networks Obvious clusters of the different groups were observed in the nonmetric multidimensional scaling (NMDS) plot of the Bray-Curtis distances (Fig. 4). The samples were clustered based on their contamination levels (SMH and XKH versus SML and XKL). Samples from the more contaminated sites tended to cluster, no matter whether they were contaminated by As or Sb. Indeed, the PERMANOVA analysis indicated that the contamination level was a major factor that shaped the microbial community structures

(p < 0.001). Among the individual groups, no significant difference was observed between SMH and XKH, while both of these highly contaminated groups were significantly different from the two less contaminated groups (SML and XKL). Such clustering might have been driven by the geochemical conditions. Therefore, interactions between the geochemical parameters and the microbial community and diversity were investigated using various statistical tools. According to the Canonical Correspondence Analysis (CCA), pH was the most important factor that shaped the indigenous microbial community compositions. This was followed by TOC, nitrate, Fetot, and Asexe (Fig. 5). The relative importance of the factors that affected the alpha diversity (indicated by observed species) was then calculated using RF (Fig. 6). pH and nitrate were the most important environmental factors that affected the alpha diversity, and both accounted for more than 10% of the variable importance of the observed species. In addition to pH and nitrate, Asexe, Fetot, and Sbtot were the other environmental parameters that influenced the observed species index. Both pH and nitrate were positively correlated with the observed species index, while Asexe, Sbexe, and Sbtot were negatively correlated with the observed species index. The effects of the other contaminant fractions (As- and Sb-related fractions) on observed species were less than 2.5%, and these are not discussed in this study. Further analysis was performed to investigate the specific interactions between the Sb and As extractable fractions and the microbial taxa (i.e., the top 1000 most abundant operation taxonomic units (OTUs) here) (Fig. 7 and Tables S1 and S2). The cooccurrence network, using the cutoff of the Spearman correlation > |0.6| and p < 0.05, indicated that all three As-related species showed substantial effects on the microbial taxa according to the larger sizes of their nodes than that of other nodes. This was followed by Sbsrp and Sbexe. A total of 66 OTUs showed strong and significant correlations with contaminant fractions. Taxonomic assignments of these OTUs suggested that Xanthomonadales (5 OTUs), Cytophagales (5 OTUs), Sphingobacteriales (5 OTUs), Family I of Cyanobacteria (4 OTUs), Gemmatimonadacea (4 OTUs), Hyphomonadaceae (4 OTUs), and Sulfurifustis (4 OTUs) were the microbial taxa that displayed the highest number of correlations with the contaminant fractions.

Fig. 2. The boxplots show the alpha diversity indices among the four sample groups.

X. Sun et al. / Environmental Pollution 260 (2020) 114052

5

Fig. 3. The boxplots are the relative abundances of the dominant microbial phyla among the different sample groups.

3.5. Keystone taxa in the contaminated sites

(Bryobacter), and Proteobacteria (Desulfurellaceae H16).

The identification of the keystone taxa in the highly contaminated samples was performed using the co-occurrence network of the microbial-microbial (biotic) interactions (Fig. 8 and Tables S3 and S4). The number of links/nodes, betweenness centralities were used to evaluate the interactions of OTUs within the cooccurrence network. The microbial populations with higher connections and lower betweenness are considered exerting more influences on the microbial community (Banerjee et al., 2018). In this study, the samples from the SMH and XKH were combined, and 758 nodes with 3873 edges were identified using the cutoff of the Spearman correlation of > |0.8| and p < 0.05. The mean degree was 15.18, the average path length was 4.34, and the average centralization betweenness was 0.398. The keystone taxa found in this study were considered the nodes with high degrees and low betweenness centralities (Roume et al., 2015). The identified keystone taxa members were nearly entirely from the Actinobacteria (two OTUs of Acidothermus, two OTUs within the order Solirubrobacterales, and one Catenulispora), along with one OTU from each of the phylums Bacteroidetes (Terrimonas), Acidobacteria

4. Discussion 4.1. Geochemical characterizations As and Sb belong to the same group in the periodic table and share similar chemical properties. It has been proposed that microorganisms may use similar mechanisms to transform both As and Sb (Kataoka et al., 2018; Wang et al., 2015). Microbial communities that inhabit only Sb and As mining contaminated sites have been extensively studied (Inskeep et al., 2007; Sun et al., 2019a; Wilson et al., 2004). However, a combined microbial community analysis of both As and Sb contaminated sites and a comparison between these two different contaminated sites will improve our knowledge of the environmental impacts of As- and Sb-related contamination on microbial communities. This information, however, is not documented yet. In this study, contaminated soil samples were sampled from the world’s largest Sb mine (Xikuangshan or XK) as the Sb contaminated site (Wu, 1993). For comparison, an As mining area, which is the largest realgar mine in

6

X. Sun et al. / Environmental Pollution 260 (2020) 114052

Fig. 4. The NMDS plot represents the microbial community beta diversity measured as Bray-Curtis distance.

Fig. 5. The Canonical Correspondence Analysis (CCA) demonstrating the impact of environmental factors on the indigenous microbial community compositions. The length of arrows indicates the relative importance of the parameter on the community compositions. The color of the dots is encoded for different contamination groups. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Asia (Tang et al., 2016), was selected as the As contaminated site (Shimen, or SM). From each contaminated site, heavily contaminated soil samples from the core mining areas and less contaminated samples from the adjacent soils were selected. Two more

contaminated groups contained the highest concentrations of either Astot (SMH) or Sbtot (XKH). Although high contamination of Sb is often associated with elevated As concentrations in sulfide ores (Hale, 1981), no significant differences in the As fractions

X. Sun et al. / Environmental Pollution 260 (2020) 114052

7

Fig. 6. Random forest prediction of the relative importance of the geochemical parameters on the community alpha diversity, measured as observed species (left panel) and the relationship between the geochemical parameters and the observed species.

Fig. 7. Co-occurrence network analysis showing the correlations between the contaminant fractions and the individual microbial OTUs (97% sequence similarity). Edges are shown only strong (Spearman correlation > |0.6|) and significant (p < 0.05) connections. The thickness of the edges is proportional to the strongness of the correlation. Size of the node is proportional to the number of connections. The color of the nodes represents the cluster of the interactions. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

8

X. Sun et al. / Environmental Pollution 260 (2020) 114052

Fig. 8. Co-occurrence network analysis showing the correlations of biological interactions in the heavily contaminated samples (97% sequence similarity). Edges are shown only strong (Spearman correlation > |0.8|) and significant (p < 0.05) connections. Size of the node is proportional to the number of connections to it. The thickness of the edges is proportional to the strongness of the correlation.

between the heavily and less contaminated Sb groups (XKH and XKL) were observed in this study. This made the site ideal for unveiling the microbial community responses to Sb contamination without the interference of As. The other geochemical parameters demonstrated patterns based on the sample contamination levels, rather than the sample locations. For instance, pH, which is considered a major parameter that drives terrestrial microbial community compositions (Fierer and Jackson, 2006), was more acidic in the heavily contaminated groups compared to the less contaminated ones. This suggested the impact of mining contamination. Both nitrate and TOC were significantly higher in the less contaminated groups compared to the highly contaminated zones, possibly because they were located close to agricultural sites. This is expected, as TOC and nitrogen content is often higher in agriculture soils than other types of soil due to intensive human activities (Boyer and Groffman, 1996; Compton and Boone, 2000).

4.2. Interactions between environmental parameters and the microbial community Although the two selected sites, XK and SM, received different

major contaminations (Sb in the XK site vs. As in the SM site), their indigenous microbial community structures were clustered for both the heavily contaminated groups. This suggested similar microbial responses to the environmental perturbations. Therefore, the interplays between the microbial communities and the geochemical conditions are of great interest to reveal the factors that drive the microbial clusters. Such interactions were investigated in two directions: the influences of geochemical parameters on (i) the overall microbial community compositions and (ii) individual microbial population.

4.3. Microbial community compositions RF predictions were employed to elucidate the impact of the geochemical parameters on the microbial diversity index (i.e., alpha diversities). The Canonical Correspondence Analysis (CCA) was used to demonstrate the impact of the geochemical parameters on the community structures (i.e., beta diversities). As revealed by the Random Forest analysis (RF) and CCA, pH was the most important factor that governed community diversities. In the less contaminated groups (SML and XKL), pH was circumneutral, which promoted the diversity of the community. However, the acidic pH in

X. Sun et al. / Environmental Pollution 260 (2020) 114052

the heavily contaminated groups (SMH and XKH) reduced the diversity of the microbial community. A similar observation has also been found in other mining-impacted sites (Cui et al., 2018; Teng et al., 2017), since the oxidation of sulfide minerals generates acidity (Baker and Banfield, 2003). It has long been recognized that the impacts of pH on the microbial community are significant (Fierer and Jackson, 2006). It is widely accepted that changes in pH can directly impact microbial community functions (Wakelin et al., 2008; Zhalnina et al., 2014). Furthermore, pH also indirectly influences the microbial community by interacting with other geochemical parameters. For instance, an alkaline pH was found to be able to release a substantial portion of As into solution (Masscheleyn et al., 1991). The positive correlation between the observed species and the nitrate concentration, as revealed by the RF predictions, suggested that a lower nitrate content also has contributed to a reduction in the alpha diversity in the heavily contaminated samples. The nitrogen availability is a critical factor that regulates microbial community compositions (Riches et al., 2013). Nitrogen deficiency is known to reduce not only taxonomic but also catabolic diversity (Fierer et al., 2012). For instance, elevated nitrogen availability has been found to enrich ammonia oxidizers (Compton et al., 2004), while changes in nitrogen availability was suggested to alter the relative importance of nitrogen-cycling bacteria and archaea communities (Bru et al., 2011). The nitrogen content could also substantially impact the geochemical cycling of the contaminant fractions (As and Sb). Oremland et al. (2002) reported As oxidation could couple to nitrate reduction by the respiration process of Alkalilimnicola ehrlichii strain MLHE-1. Recently, evidences has been provided that nitrate addition could substantially accelerate the As oxidation by Azoarcus and Pseudogulbenkiania-related spp. (Li et al., 2019). It is worthy to note that the contaminant fractions in the current study generally demonstrated negative effects on microbial diversity, as revealed by RF. Sheik et al. (2012) observed significant reductions in community richness and evenness in soils contaminated with Cr and As compared to regular soils. Similarly, river sediment impacted by long-term metal input also has shown to produce a significant reduction in community diversity (Jacquiod et al., 2018). Therefore, it is proposed that the combined effects of geochemical signatures (i.e., low pH and nitrate but high concentrations of As and Sb) resulted in the lower microbial alpha diversity index in the more contaminated sites. 4.4. Individual microbial populations To further illustrate the responses of individual microbial groups with geochemical parameters, especially those associated with As and Sb contamination, the contaminant-microbial interactions were visualized using co-occurrence networks. Accordingly, Asrelated species had larger sizes than Sb-related species, suggesting that As may exert more influences on innate microbial communities. Indeed, it has been reported that As is more toxic than Sb, and therefore, bacteria may respond more vigorously to As contamination (Gebel, 1997). This observation agrees with the RF analysis that indicated that all three As-related species, as revealed by sequential extraction, showed substantial effects on microbial diversity, while only Sbtot showed a considerable effect. The conclusion obtained from all these observations proposed that As may be more influential on the microbial community than Sb. Such information is valuable, since microorganism could use the same pathways for As and Sb metabolisms (Luo et al., 2014; Wang et al., 2015), the response of these microorganisms to these contaminants are different. Although only the bioavailable fractions of As and Sb (Asexe,

9

Assrp, Sbexe, and Sbsrp) constituted minor proportions compared to the total concentrations, these fractions of As and Sb are suggested to be more influential on the individual microbial populations compared to the total As and Sb concentrations (Astot and Sbtot). This result agrees with the results of previous studies that have suggested bioavailable As and Sb were more influential on microbial communities (Wang et al., 2018), since the bioavailable fractions were more easily released from the soil matrices compared to the remaining fractions. Therefore, the bioavailable fractions had been transported into microbial cells (Kim et al., 2014; Sanders et al., 1997). Another interesting feature is that 109 out of 112 correlations were positive, with only three negative correlations. This suggests that the innate microbial communities may develop specific survival strategies to the elevated As and Sb concentrations. Among the microbial taxa, many taxa showed multiple correlations with the contaminant fractions. Therefore, these taxa are of great interest and were selected for further discussion. Four OTUs associated with Sulfurifustis showed positive correlations with the contaminant fractions. Members of Sulfurifustis have been identified as sulfur-oxidizing bacteria because they displayed the capability for S oxidation and C fixation (Kojima et al., 2015). In the current study, these bacteria were primarily detected in SMH, where elevated As was observed. Consistently, three Sulfurifustis associated OTUs were related with Asexe, while only one OTU was related with Sbsrp. These observations suggest their adaptation to As contaminated habitats. Unfortunately, direct evidence showing the capability of Sulfurifustis on As transformation is not yet available, although these bacteria have been detected in various mining environments (Liu et al., 2019a, 2019b). Other important microorganisms that are known to participate in As and Sb metabolism were found in the As- and Sb-related co-occurrence network. For instance, a Thiobacillus-associated OTU was correlated with Sbsrp. It has been reported that Thiobacillus often exists in high abundance in contaminated mining sites (Fortin et al., 1996; Sun et al., 2016), as it can actively grow on arsenic-containing arsenopyrite (FeAsS) (Collinet and Morin, 1990). Also, the members of Thiobacillus are known to carry the arsenite oxidation gene (aioA) and the arsenic detoxification genes (arsC) (Butcher et al., 2000; Inskeep et al., 2007). A Geobacter-associated OTU was positively correlated with Sbsrp and Sbexe. The members of Geobacter contain diverse genes related to As and Sb detoxification, including the arsenate/antimonate reductase (arrA) gene (Dang et al., 2017). Due to its diverse resistance mechanism against As and Sb, it is often observed in high abundances in As and Sb contaminated sites (Sun et al., 2016; W. Sun et al., 2019). 4.5. Crucial microbial populations in mining soils According to the aforementioned discussion, it is obvious that the geochemical conditions impacted the indigenous microbial diversity and community compositions. The microbial clusters of the more contaminated groups (XKH and SMH) are of more interest because they may contain the microorganisms that can adapt to harsh environments, such as low pH, low nutrient availability concentrations, and high As and Sb. An investigation of the crucial microbial populations that inhabited the heavily contaminated groups may indicate their special ecological role in harsh environments. In the current study, the keystone taxa was investigated for the representative microbial taxa that were essential and exert significant influences on the community structures and functions in the contaminated groups (Banerjee et al., 2018). In the current study, the most-connected OTUs with low betweenness centralities were considered to be the keystone taxa (Hu et al., 2017). Within these OTUs, members of the family Actinobacteria demonstrated

10

X. Sun et al. / Environmental Pollution 260 (2020) 114052

considerable influence on the microbial communities. This observation was in corroboration with the findings of other contaminated soil microbial communities, which also demonstrated the identified members of the Actinobacteria as keystone taxa (Chao et al., 2016; Jiao et al., 2016). Moreover, some groups, such as Acidothermus, Bryobacter, Terrimonas, and Catenulispora, were repeatedly identified as keystone taxa in diverse environments (Kaminsky et al., 2017; Karimi et al., 2019; Lupatini et al., 2014; Mondav et al., 2017). This suggested that these genera are essential members of the microbial community. Specifically, the genera Acidothermus and Bryobacter may be more important, since both of them were enriched in the heavily contaminated groups, suggesting their adaptation to the As/Sb contaminated environments and provided services to other members of the community (Banerjee et al., 2018). Future studies are necessary to reveal the ecological roles of the important members in the community.

5. Conclusions In the current study, the impact of As and Sb contamination on the indigenous microbial community was investigated. The results suggested that, although the contaminated sites had received As and Sb contamination for decades, the major environmental drivers were pH and nutrient availability. The low pH and nitrogen concentrations, along with the metal contamination, significantly decreased the microbial diversity in the more contaminated sites. The similar microbial taxa were enriched in both As and Sb contaminated sites, in correspondence with the observation of similar microbial community compositions in two heavily contaminated sites, possibility as a response to the similar geochemical conditions. The microbial responses toward the contamination fractions, however, revealed a stronger impact from As concentration compared to the Sb concentration. This suggest that although microorganisms could share the same metabolism pathways for As and Sb, their responses to these contaminations are different. The investigation of the keystone taxa revealed a set of the microbial taxa that possessed the ability to survive in the As and Sb contaminated environments, and these taxa may provide critical services to the microbial community. Further investigations of these important microbial groups can provide potential remediation strategies for As and Sb contaminated sites.

CRediT authorship contribution statement Xiaoxu Sun: Conceptualization, Formal analysis, Visualization, Writing - original draft, Writing - review & editing. Tianle Kong: Investigation, Validation. Rui Xu: Software, Writing - review & editing. Baoqin Li: Visualization, Methodology. Weimin Sun: Funding acquisition, Supervision, Writing - review & editing.

Acknowledgements This work was supported by the National Natural Science Foundation of China (grant no. 41907212); GDAS0 Project of Science and Technology Development (grant nos. 2019GDASYL-0302006, 2019GDASYL-0103047 and 2019GDASYL-0301002); the High-level Leading Talent Introduction Program of GDAS (grant no. 2016GDASRC-0103); the National Natural Science Foundation of Guangdong (grant no. 2019A1515011559); and the Science and Technology Planning Project of Guangzhou (grant no. 201904010366).

Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.envpol.2020.114052. References Baker, B.J., Banfield, J.F., 2003. Microbial communities in acid mine drainage. FEMS Microbiol. Ecol. 44, 139e152. https://doi.org/10.1016/S0168-6496(03)00028-X. Banerjee, S., Schlaeppi, K., van der Heijden, M.G.A., 2018. Keystone taxa as drivers of microbiome structure and functioning. Nat. Rev. Microbiol. 1e10. https:// doi.org/10.1038/s41579-018-0024-1. Boyer, J.N., Groffman, P.M., 1996. Bioavailability of water extractable organic carbon fractions in forest and agricultural soil profiles. Soil Biol. Biochem. 28, 783e790. Bru, D., Ramette, A., Saby, N.P.A., Dequiedt, S., Ranjard, L., Jolivet, C., Arrouays, D., Philippot, L., 2011. Determinants of the distribution of nitrogen-cycling microbial communities at the landscape scale. ISME J. 5, 532e542. https://doi.org/ 10.1038/ismej.2010.130. Burgess, W.G., Hoque, M.A., Michael, H.A., Voss, C.I., Breit, G.N., Ahmed, K.M., 2010. Vulnerability of deep groundwater in the Bengal Aquifer System to contamination by arsenic. Nat. Geosci. 3, 83e87. https://doi.org/10.1038/ngeo750. Butcher, B.G., Deane, S.M., Rawlings, D.E., 2000. The chromosomal arsenic resistance genes of Thiobacillus ferrooxidans have an unusual arrangement and confer increased arsenic and antimony resistance to Escherichia coli. Appl. Environ. Microbiol. 66, 1826e1833. https://doi.org/10.1128/AEM.66.5.1826-1833.2000. Bybee, S.M., Bracken-Grissom, H., Haynes, B.D., Hermansen, R.A., Byers, R.L., Clement, M.J., Udall, J.A., Wilcox, E.R., Crandall, K.A., 2011. Targeted amplicon sequencing (TAS): a scalable next-gen approach to multilocus, multitaxa phylogenetics. Genome Biol. Evol. 3, 1312e1323. https://doi.org/10.1093/gbe/ evr106. Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., ~ a, A.G., Goodrich, J.K., Gordon, J.I., Huttley, G.A., Kelley, S.T., Fierer, N., Pen Knights, D., Koenig, J.E., Ley, R.E., Lozupone, C.A., Mcdonald, D., Muegge, B.D., Pirrung, M., Reeder, J., Sevinsky, J.R., Turnbaugh, P.J., Walters, W.A., Widmann, J., Yatsunenko, T., Zaneveld, J., Knight, R., 2010. QIIME allows analysis of highthroughput community sequencing data. Nat. Methods 7, 335e336. https:// doi.org/10.1038/nmeth0510-335. Chao, Y., Liu, W., Chen, Y., Chen, W., Zhao, L., Ding, Q., Wang, S., Tang, Y.T., Zhang, T., Qiu, R.L., 2016. Structure, variation, and Co-occurrence of soil microbial communities in abandoned sites of a rare earth elements mine. Environ. Sci. Technol. 50, 11481e11490. https://doi.org/10.1021/acs.est.6b02284. Collinet, M.-N., Morin, D., 1990. Characterization of arsenopyrite oxidizing Thiobacillus. Tolerance to arsenite, ferrous and ferric iron. Antonie 237e244. Compton, J.E., Boone, R.D., 2000. Long-term impacts of agriculture on soil carbon and nitrogen in new england forests. Ecology 81, 2314e2330. Compton, J.E., Watrud, L.S., Porteous, L.A., DeGrood, S., 2004. Response of soil microbial biomass and community composition to chronic nitrogen additions at Harvard forest. For. Ecol. Manag. 196, 143e158. https://doi.org/10.1016/ j.foreco.2004.03.017. Cui, H., Liu, L. Le, Dai, J.R., Yu, X.N., Guo, X., Yi, S.J., Zhou, D.Y., Guo, W.H., Du, N., 2018. Bacterial community shaped by heavy metals and contributing to health risks in cornfields. Ecotoxicol. Environ. Saf. 166, 259e269. https://doi.org/10.1016/ j.ecoenv.2018.09.096. Dang, Y., Walker, D.J.F., Vautour, K.E., Dixon, S., Holmes, D.E., 2017. Arsenic detoxification by Geobacter species. Appl. Environ. Microbiol. 1e9. DeSantis, T.Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E.L., Keller, K., Huber, T., Dalevi, D., Hu, P., Andersen, G.L., 2006. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069e5072. https://doi.org/10.1128/AEM.03006-05. Edgar, R.C., 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996e998. https://doi.org/10.1038/nmeth.2604. Fierer, N., Jackson, R.B., 2006. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. Unit. States Am. 103, 626e631. https:// doi.org/10.1073/pnas.0507535103. Fierer, N., Lauber, C.L., Ramirez, K.S., Zaneveld, J., Bradford, M.A., Knight, R., 2012. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J. 6, 1007e1017. https:// doi.org/10.1038/ismej.2011.159. Filella, M., Belzile, N., Lett, M.C., 2007. Antimony in the environment: a review focused on natural waters. III. Microbiota relevant interactions. Earth Sci. Rev. 80, 195e217. https://doi.org/10.1016/j.earscirev.2006.09.003. Fortin, D., Davis, B., Beveridge, T.J., 1996. Role of Thiobacillus and sulfate-reducing bacteria in iron biocycling in oxic and acidic mine tailings. FEMS Microbiol. Ecol. 21, 11e24. https://doi.org/10.1016/0168-6496(96)00039-6. Frohne, T., Rinklebe, J., Diaz-Bone, R.A., Du Laing, G., 2011. Controlled variation of redox conditions in a floodplain soil: impact on metal mobilization and biomethylation of arsenic and antimony. Geoderma 160, 414e424. https://doi.org/ 10.1016/j.geoderma.2010.10.012. Gebel, T., 1997. Aresnic and antimony: comparative approach on mechanistic toxicology. Chem. Biol. Interact. 107, 131e144. https://doi.org/10.1016/S00092797(97)00087-2. Green, S.J., Venkatramanan, R., Naqib, A., 2015. Deconstructing the polymerase chain reaction: understanding and correcting bias associated with primer

X. Sun et al. / Environmental Pollution 260 (2020) 114052 degeneracies and primer-template mismatches. PloS One 10, 1e21. https:// doi.org/10.1371/journal.pone.0128122. Hale, M., 1981. Pathfinder applications of arsenic, antimony, and bismuth in geochemical exploration. J. Geochem. Explor. 15, 307e323. He, M., Wang, X., Wu, F., Fu, Z., 2012. Antimony pollution in China. Sci. Total Environ. 421e422, 41e50. https://doi.org/10.1016/j.scitotenv.2011.06.009. Herbold, C.W., Pelikan, C., Kuzyk, O., Hausmann, B., Angel, R., Berry, D., Loy, A., 2015. A flexible and economical barcoding approach for highly multiplexed amplicon sequencing of diverse target genes. Front. Microbiol. 6, 1e8. https://doi.org/ 10.3389/fmicb.2015.00731. Hu, A., Ju, F., Hou, L., Li, J., Yang, X., Wang, H., Mulla, S.I., Sun, Q., Bürgmann, H., Yu, C.P., 2017. Strong impact of anthropogenic contamination on the cooccurrence patterns of a riverine microbial community. Environ. Microbiol. 19, 4993e5009. https://doi.org/10.1111/1462-2920.13942. Inskeep, W.P., Macur, R.E., Hamamura, N., Warelow, T.P., Ward, S.A., Santini, J.M., 2007. Detection, diversity and expression of aerobic bacterial arsenite oxidase genes. Environ. Microbiol. 9, 934e943. https://doi.org/10.1111/j.14622920.2006.01215.x. Islam, F.S., Gault, A.G., Boothman, C., Polya, D.A., Chamok, J.M., Chatterjee, D., Lloyd, J.R., 2004. Role of metal-reducing bacteria in arsenic release from Bengal delta sediments. Nature 430, 68e71. https://doi.org/10.1038/nature02638. Jacquiod, S., Cyriaque, V., Riber, L., Al-soud, W.A., Gillan, D.C., Wattiez, R., Sørensen, S.J., 2018. Long-term industrial metal contamination unexpectedly shaped diversity and activity response of sediment microbiome. J. Hazard Mater. 344, 299e307. https://doi.org/10.1016/j.jhazmat.2017.09.046. Jiao, S., Liu, Z., Lin, Y., Yang, J., Chen, W., Wei, G., 2016. Bacterial communities in oil contaminated soils: biogeography and co-occurrence patterns. Soil Biol. Biochem. 98, 64e73. https://doi.org/10.1016/j.soilbio.2016.04.005. Kaminsky, R., Trouche, B., Morales, S.E., 2017. Soil classification predicts differences in prokaryotic communities across a range of geographically distant soils once pH is accounted for. Sci. Rep. 7, 1e9. https://doi.org/10.1038/srep45369. Karimi, B., Dequiedt, S., Terrat, S., Jolivet, C., Arrouays, D., Wincker, P., Cruaud, C., vost-Boure , N., Ranjard, L., 2019. Biogeography of soil Bispo, A., Chemidlin Pre bacterial networks along a gradient of cropping intensity. Sci. Rep. 9, 1e10. https://doi.org/10.1038/s41598-019-40422-y. Kataoka, T., Mitsunobu, S., Hamamura, N., 2018. Influence of the chemical form of antimony on soil microbial community structure and arsenite oxidation activity. Microbes Environ. 214e221. https://doi.org/10.1264/jsme2.ME17182, 00. Kim, E.J., Yoo, J.C., Baek, K., 2014. Arsenic speciation and bioaccessibility in arseniccontaminated soils: sequential extraction and mineralogical investigation. Environ. Pollut. 186, 29e35. https://doi.org/10.1016/j.envpol.2013.11.032. Kojima, H., Shinohara, A., Fukui, M., 2015. Sulfurifustis variabilis gen. nov., sp. nov., a sulfur oxidizer isolated from a lake, and proposal of Acidiferrobacteraceae fam. nov. and Acidiferrobacterales ord. nov. Int. J. Syst. Evol. Microbiol. 65, 3709e3713. https://doi.org/10.1099/ijsem.0.000479. Lehr, C.R., Kashyap, D.R., McDermott, T.R., 2007. New insights into microbial oxidation of antimony and arsenic. Appl. Environ. Microbiol. 73, 2386e2389. https://doi.org/10.1128/AEM.02789-06. Li, J., Wang, Q., Li, M., Yang, B., Shi, M., Guo, W., McDermott, T.R., Rensing, C., Wang, G., 2015. Proteomics and genetics for identification of a bacterial antimonite oxidase in agrobacterium tumefaciens. Environ. Sci. Technol. 49, 5980e5989. https://doi.org/10.1021/es506318b. Li, J., Wang, Q., Oremland, R.S., Kulp, T.R., Rensing, C., Wang, G., 2016. Microbial antimony biogeochemistry: enzymes, regulation, and related metabolic pathways. Appl. Environ. Microbiol. 82, 5482e5495. https://doi.org/10.1128/ AEM.01375-16. Li, J., Wang, Q., Zhang, S., Qin, D., Wang, G., 2013. Phylogenetic and genome analyses of antimony-oxidizing bacteria isolated from antimony mined soil. Int. Biodeterior. Biodegrad. 76, 76e80. https://doi.org/10.1016/j.ibiod.2012.06.009. €ggblom, M.M., Li, F., Hu, M., 2019. Bacterial communities and Li, X., Qiao, J., Li, S., Ha functional genes stimulated during anaerobic arsenite oxidation and nitrate reduction in a paddy soil. Environ. Sci. Technol. https://doi.org/10.1021/ acs.est.9b04308. Liaw, A., Wiener, M., Breiman, L., Cutler, A., 2018. Breiman and Cutler’s Random Forests for Classification and Regression. R News. https://doi.org/10.1023/A. Liu, J. li, Yao, J., Lu, C., Li, H., Li, Z. fu, Duran, R., Sunahara, G., Mihucz, V.G., 2019a. Microbial activity and biodiversity responding to contamination of metal(loid) in heterogeneous nonferrous mining and smelting areas. Chemosphere. https:// doi.org/10.1016/j.chemosphere.2019.03.051. Liu, J. li, Yao, J., Wang, F., Min, N., Gu, J. hai, Li, Z. fu, Sunahara, G., Duran, R., SolevicKnudsen, T., Hudson-Edwards, K.A., Alakangas, L., 2019b. Bacterial Diversity in Typical Abandoned Multi-Contaminated Nonferrous Metal(loid) Tailings during Natural Attenuation. Environmental Pollution. https://doi.org/10.1016/ j.envpol.2018.12.045. Luo, J., Bai, Y., Liang, J., Qu, J., 2014. Metagenomic approach reveals variation of microbes with arsenic and antimony metabolism genes from highly contaminated soil. PloS One 9. https://doi.org/10.1371/journal.pone.0108185. Lupatini, M., Suleiman, A.K.A., Jacques, R.J.S., Antoniolli, Z.I., de Siqueira Ferreira, A., Kuramae, E.E., Roesch, L.F.W., 2014. Network topology reveals high connectance levels and few key microbial genera within soils. Front. Environ. Sci. 2, 1e11. https://doi.org/10.3389/fenvs.2014.00010. Macy, J.M., Santini, J.M., Pauling, B.V., O’Neill, a H., Sly, L.I., 2000. Two new arsenate/ sulfate-reducing bacteria: mechanisms of arsenate reduction. Arch. Microbiol. 173, 49e57. https://doi.org/10.1007/s002030050007. Masscheleyn, P.H., Delaune, R.D., Patrick, W.H., 1991. Effect of redox potential and

11

pH on arsenic speciation and solubility in a contaminated soil. Environ. Sci. Technol. 25, 1414e1419. https://doi.org/10.1021/es00020a008. Meng, Y.L., Liu, Z., Rosen, B.P., 2004. As(III) and Sb(III) uptake by GlpF and efflux by ArsB in Escherichia coli. J. Biol. Chem. 279, 18334e18341. https://doi.org/ 10.1074/jbc.M400037200. Mondav, R., McCalley, C.K., Hodgkins, S.B., Forlking, S., Saleska, S., Rich, V.I., Chanton, J.P., Crill, P.M., 2017. Microbial network, phylogenetic diversity and community membership in the active layer across a permafrost thaw gradient. Environ. Microbiol. 19, 3201e3218. https://doi.org/10.1111/1462-2920. Newman, M.E.J., 2006. Modularity and community structure in networks. Proc. Natl. Acad. Sci. Unit. States Am. 103, 8577e8582. https://doi.org/10.1073/ pnas.0601602103. Nguyen, V.K., Lee, J.U., 2015. Antimony-oxidizing bacteria isolated from antimonycontaminated sediment e a phylogenetic study. Geomicrobiol. J. 32, 50e58. https://doi.org/10.1080/01490451.2014.925009. Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P.R., O’Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H., Szoecs, E., Wagner, H., 2018. Vegan: Community Ecology Package. R Package Version 2.4-6. https://CRAN.R-project.org/package¼vegan. Oremland, R.S., Hoeft, S.E., Santini, J.M., Bano, N., Hollibaugh, R.A., Hollibaugh, J.T., 2002. Anaerobic oxidation of arsenite in Mono Lake water and by a facultative, arsenite-oxidizing chemoautotroph, Strain MLHE-1. Appl. Environ. Microbiol. 68, 4795e4802. https://doi.org/10.1128/AEM.68.10.4795. Oremland, R.S., Stolz, J.F., Hollibaugh, J.T., 2004. The microbial arsenic cycle in Mono Lake , California. FEMS Microbiol. Ecol. 48, 15e27. https://doi.org/10.1016/ j.femsec.2003.12.016. Riches, D., Porter, I.J., Oliver, D.P., Bramley, R.G.V., Rawnsley, B., Edwards, J., White, R.E., 2013. A review of soil biological properties as indicators of soil quality in Australian viticulture. Aust. J. Grape Wine Res. 19, 311e323. https:// doi.org/10.1111/ajgw.12034. Rodriguez, R.R., Basta, N.T., Casteel, S.W., Armstrong, F.P., Ward, D.C., 2003. Chemical extraction methods to assess bioavailable arsenic in soil and solid media. J. Environ. Qual. 32, 876e884. https://doi.org/10.2134/jeq2003.8760. Roume, H., Heintz-Buschart, A., Muller, E. El, May, P., Satagopam, V.P., Laczny, C.C., Narayanasamy, S., Lebrun, L.A., Hoopmann, M.R., Schupp, J.M., Gillece, J.D., Hicks, N.D., Engelthaler, D.M., Sauter, T., Keim, P.S., Moritz, R.L., Wilmes, P., 2015. Comparative integrated omics: identification of key functionalities in microbial community-wide metabolic networks. npj Biofilms Microbiomes 1, 15007. https://doi.org/10.1038/npjbio. Sanders, O.I., Rensing, C., Kuroda, M., Mitra, B., Rosen, B.P., 1997. Antimonite is accumulated by the glycerol facilitator GlpF in Escherichia coli. J. Bacteriol. https://doi.org/10.1128/jb.179.10.3365-3367.1997. Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B., Ideker, T., 2003. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498e2504. https://doi.org/10.1101/gr.1239303.metabolite. Sheik, C.S., Mitchell, T.W., Rizvi, F.Z., Rehman, Y., Faisal, M., Hasnain, S., McInerney, M.J., Krumholz, L.R., 2012. Exposure of soil microbial communities to chromium and arsenic alters their diversity and structure. PloS One 7. https:// doi.org/10.1371/journal.pone.0040059. Silver, S., Phung, L.T., 2005. Genes and enzymes involved in bacterial oxidation and reduction of inorganic arsenic. Appl. Environ. Microbiol. 71, 599e608. https:// doi.org/10.1128/AEM.71.2.599. €ggblom, M.M., Han, F., Xiao, E., Zhang, M., Wang, Q., Li, F., Sun, W., Sun, X., Li, B., Ha 2019. Bacterial response to antimony and arsenic contamination in rice paddies during different flooding conditions. Sci. Total Environ. 675, 273e285. https:// doi.org/10.1016/j.scitotenv.2019.04.146. Sun, W., Xiao, E., Dong, Y., Tang, S., Krumins, V., Ning, Z., Sun, M., Zhao, Y., Wu, S., Xiao, T., 2016. Profiling microbial community in a watershed heavily contaminated by an active antimony (Sb) mine in Southwest China. Sci. Total Environ. 550, 297e308. https://doi.org/10.1016/j.scitotenv.2016.01.090. €ggblom, M., Krumins, V., Dong, Y., Sun, X., Li, F., Wang, Q., Li, B., Sun, W., Xiao, E., Ha Yan, B., 2018. Bacterial survival strategies in an alkaline tailing site and the physiological mechanisms of dominant phylotypes as revealed by metagenomic analyses. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.8b03853. €ggblom, M., Dong, Y., Tang, S., Sun, W., Xiao, E., Xiao, T., Krumins, V., Wang, Q., Ha Hu, M., Li, B., Xia, B., Liu, W., 2017. Response of soil microbial communities to elevated antimony and arsenic contamination indicates the relationship between the innate microbiota and contaminant fractions. Environ. Sci. Technol. 51, 9165e9175. https://doi.org/10.1021/acs.est.7b00294. Sun, X., Li, B., Han, F., Xiao, E., Wang, Q., Xiao, T., Sun, W., 2019a. Vegetation type impacts microbial interaction with antimony contaminants in a miningcontaminated soil environment. Environ. Pollut. https://doi.org/10.1016/ j.envpol.2019.06.070. Sun, X., Li, B., Han, F., Xiao, E., Xiao, T., Sun, W., 2019b. Impacts of arsenic and antimony co-contamination on sedimentary microbial communities in rivers with different pollution gradients. Microb. Ecol. https://doi.org/10.1007/ s00248-019-01327-5. Tamura, H., Goto, K., Yotsuyanagi, T., Nagayama, M., 1974. Spectrophotometric determination of iron(II) with 1,10-phenanthroline in the presence of large amounts of iron(III). Talanta 21, 314e318. Tang, J., Liao, Y., Yang, Z., Chai, L., Yang, W., 2016. Characterization of arsenic seriouscontaminated soils from Shimen realgar mine area, the Asian largest realgar deposit in China. J. Soils Sediments 16, 1519e1528. https://doi.org/10.1007/ s11368-015-1345-6.

12

X. Sun et al. / Environmental Pollution 260 (2020) 114052

Teng, W., Kuang, J., Luo, Z., Shu, W., 2017. Microbial diversity and community assembly across environmental gradients in acid mine drainage. Minerals 7. https://doi.org/10.3390/min7060106. Wakelin, S.A., Macdonald, L.M., Rogers, S.L., Gregg, A.L., Bolger, T.P., Baldock, J.A., 2008. Habitat selective factors influencing the structural composition and functional capacity of microbial communities in agricultural soils. Soil Biol. Biochem. 40, 803e813. https://doi.org/10.1016/j.soilbio.2007.10.015. Wang, N., Zhang, S., He, M., 2018. Bacterial community profile of contaminated soils in a typical antimony mining site. Environ. Sci. Pollut. Res. 25, 141e152. https:// doi.org/10.1007/s11356-016-8159-y. Wang, Q., Warelow, T.P., Kang, Y.S., Romano, C., Osborne, T.H., Lehr, C.R., Bothner, B., McDermott, T.R., Santini, J.M., Wang, G., 2015. Arsenite oxidase also functions as an antimonite oxidase. Appl. Environ. Microbiol. 81, 1959e1965. https://doi.org/ 10.1128/AEM.02981-14. Wenzel, W.W., Kirchbaumer, N., Prohaska, T., Stingeder, G., Lombi, E., Adriano, D.C., 2001. Arsenic fractionation in soils using an improved sequential extraction

procedure. Anal. Chim. Acta 436, 309e323. https://doi.org/10.1016/S00032670(01)00924-2. Wilson, N.J., Craw, D., Hunter, K., 2004. Antimony distribution and environmental mobility at an historic antimony smelter site , New Zealand. Environ. Pollut. 129, 257e266. https://doi.org/10.1016/j.envpol.2003.10.014. Wu, J., 1993. Antimony vein deposits of China. Ore Geol. Rev. 8, 213e232. https:// doi.org/10.1016/0169-1368(93)90017-S. Yu, H.Y., Liu, Chuanping, Zhu, J., Li, F., Deng, D.M., Wang, Q., Liu, Chengshuai, 2016. Cadmium availability in rice paddy fields from a mining area: the effects of soil properties highlighting iron fractions and pH value. Environ. Pollut. 209, 38e45. https://doi.org/10.1016/j.envpol.2015.11.021. Zhalnina, K., Dias, R., de Quadros, P.D., Davis-Richardson, A., Camargo, F.A.O., Clark, I.M., McGrath, S.P., Hirsch, P.R., Triplett, E.W., 2014. Soil pH determines microbial diversity and composition in the park grass experiment. Microb. Ecol. 69, 395e406. https://doi.org/10.1007/s00248-014-0530-2.