Impacts of Arsenic and Antimony Co-Contamination

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introduced elevated levels of arsenic and antimony into the rivers and has increased the ... association with the sediment matrix and considered nonbioavailable. ... members of the Hyphomicrobiaceae and Bradyrhizobiaceae families, were inhibited ...... antimony, and other trace element contamination in a mine tailings.
Microbial Ecology https://doi.org/10.1007/s00248-019-01327-5

ENVIRONMENTAL MICROBIOLOGY

Impacts of Arsenic and Antimony Co-Contamination on Sedimentary Microbial Communities in Rivers with Different Pollution Gradients Xiaoxu Sun 1 & Baoqin Li 1 & Feng Han 1 & Enzong Xiao 2 & Tangfu Xiao 2 & Weimin Sun 1 Received: 19 October 2018 / Accepted: 14 January 2019 # Springer Science+Business Media, LLC, part of Springer Nature 2019

Abstract Arsenic (As) and antimony (Sb) are both toxic metalloids that are of primary concern for human health. Mining activity has introduced elevated levels of arsenic and antimony into the rivers and has increased the risks of drinking water contamination in China. Due to their mobility, the majority of the metalloids originating from mining activities are deposited in the river sediments. Thus, depending on various geochemical conditions, sediment could either be a sink or source for As and Sb in the water column. Microbes are key mediators for biogeochemical transformation and can both mobilize or precipitate As and Sb. To further understand the microbial community responses to As and Sb contamination, sediment samples with different contamination levels were collected from three rivers. The result of the study suggested that the major portions of As and Sb were in strong association with the sediment matrix and considered nonbioavailable. These fractions, however, were also suggested to have profound influences on the microbial community composition. As and Sb contamination caused strong reductions in microbial diversity in the heavily contaminated river sediments. Microorganisms were more sensitive to As comparing to Sb, as revealed by the co-occurrence network and random forest predictions. Operational taxonomic units (OTUs) that were potentially involved in As and Sb metabolism, such as Anaerolinea, Sphingomonas, and Opitutus, were enriched in the heavily contaminated samples. In contrast, many keystone taxa, including members of the Hyphomicrobiaceae and Bradyrhizobiaceae families, were inhibited by metalloid contamination, which could further impair crucial environmental services provided by these members. Keywords Antimony . Arsenic . Co-occurrence network . Microbial community . Random forest

Introduction Arsenic (As) and antimony (Sb) contaminations are primary concerns of human health worldwide. As, which is a group I carcinogen [1], is released into the environment by natural processes from sediment into groundwater or released through anthropogenic activities, such as mining [2, 3]. Sb, another compound belong to Group 15 of the periodic table [4], is a

Xiaoxu Sun and Baoqin Li contributed equally to this work. * Weimin Sun [email protected] 1

Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Guangdong Institute of Eco-environmental Science & Technology, Guangzhou 510650, China

2

Key Laboratory of Water Quality and Conservation in the Pearl River Delta, Ministry of Education, School of Environmental Science and Engineering, Guangzhou University, Guangzhou 510006, China

suspected carcinogen with potential damage to the immune and nervous systems [5, 6]. Unlike As, the natural presence of Sb is generally low [7]. A typical sediment environment contains only a few ppm of Sb [8]. Elevated Sb concentrations are also found in the environment (more than 1000 ppm) [7, 9, 10] and can be directly associated with anthropogenic activities or sulfidic ores [11]. Due to the fact that they often co-occur in the mining area [12], simultaneous contamination of both As and Sb is often observed in the environment, especially in an environment impacted by mining activities [9, 13–16]. Drinking water contamination of As and Sb is a serious problem in Asia [17, 18]. Contamination of drinking water by both natural and anthropogenic sources has been reported in many regions in Asia, including China, Japan, Bangladesh, and India [19–22]. In China, As and Sb contamination in freshwater is primarily caused by extensive mining activities, especially Sb, given that China is the world’s largest Sb producer [23]. The toxicity and mobility of released As and Sb are determined by their speciation in the environment [6]. Elevated As and Sb concentrations occurred in river sediments are

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usually orders of magnitude higher than that of the water column [11, 24]. Therefore, the environmental behavior of As and Sb in sediments determines the contamination level in the water column. The presence of iron and aluminum oxides, along with other soil minerals, are important sinks for As and Sb [25, 26]. Thus, a substantial portion of both As and Sb are nonbioavailable in the environment [27]. Despite their immobilization with other minerals, biological transformation accounts for a substantial fraction of the As and Sb geochemical cycles [28]. During the chronic exposure history to As, microorganisms have evolved to utilize As as an electron donor/ acceptor for respiration processes or to reduce As(V) to As(III) by a detoxification pathway [29]. The metabolism of Sb has been less studied and has been suggested to share the same metabolic pathways as As due to their similar chemical properties [30]. Pandey et al. suggested that the ars operon, which encodes nonrespiratory As-reducing enzymes, could also reduce Sb [31, 32]. Unique Sb metabolism pathways, however, also exist. Lehr et al. discovered a unique Sb oxidation mechanism in an Agrobacterium tumefaciens strain, which could only oxidize Sb but not As [30]. Due to the diverse metabolism capability, microorganism is considered for bioremediation of contaminant metalloids in the environment. Mateos et al. modified a Corynebacterium glutamicumas strain to remove As from contaminated environment [33]. Nevertheless, a sulfate-reducing bacteria consortium has demonstrated the ability to remove Sb from water column [34]. A recent study has revealed the biological strategies for microbes that thrive in the contaminated environment, and provided further support for bioremediation of As and Sb [35]. Since the diverse microbial-contaminant interactions exist in the environment, the environmental behavior and toxicity of As and Sb in the environment are determined by the microbial activities, which was previously suggested in the literatures [36–38]. Owing to the importance of microorganisms in As and Sb transformation in the environment, it is critical to understand the microbial community responses to As and Sb contamination. Diverse groups of bacteria can tolerate high concentrations of As and Sb. In As and Sb co-contaminated soil samples, Proteobacteria was suggested to be the dominant phylum, and the arsC/aioA genes were positively correlated with the contaminant concentrations [38]. Comparison between two As- and Sb-contaminated soil cores revealed that various phylogenetic groups of microorganisms, including diverse members from Alphaproteobacteria and Actinobacteria, responded to As and Sb contamination differently, as revealed in the LEfSe analysis and correlation networks [39]. The speciation and bioavailability of the metalloids also have profound impacts on microbial community responses. In the contaminated soil, Sb(V) and As(V) were suggested to have stronger impacts on the microbial community than Sb(III) and As(III) [40]. In a case study of As co-contamination with three Sb compounds of different bioavailability, the results

revealed that only the soluble potassium antimonate had induced As oxidizing gene abundances [41]. Understanding the bioavailability of As and Sb, which were considered as the contaminants in the current study, and their impacts on the microbial communities in river sediments have the potential to remediate As and Sb contamination and contribute to the safe drinking water supply. Since a substantial fraction of As and Sb compounds are insoluble, As and Sb are found in much higher concentrations in sediments than in water columns [11]. The behavior of these metalloids depends on the environmental condition and microbial activity [42]. To better understand the speciation and accessibility of As and Sb contaminants, sediment samples from three rivers with different contamination levels were collected in Yunnan, southwest China. The objectives of this study were to (i) characterize the microbial communities by SSU rRNA as a marker gene and (ii) use statistical tools to reveal the keystone taxa in the sediment microbial community and their response to the elevated metalloid contaminations.

Method Sample Collection The sediment samples were collected from three rivers/creeks with different contamination levels in the rural area of the city Kaiyuan. The ZXG indicates a river that passes through the abandoned Zuoxiguo Sb mine (23° 37’ N, 103° 31’ E). The DZ is a short river that passes through the active Danzhai Sb mine (23° 37’ N, 103°22’ E). The ND samples were collected in a river that was not directly impacted by mining activities and the river was used as the water source (NDSY) for the nearby Nandong scenic area (23° 38’ N, 103° 17’ E). At each site, the surface sediment (0~5 cm) was collected using a sterilized scoop. A total of 30 samples were collected, 12 from ZXG, 5 from DZ, and 13 from ND. The number of samples collected was in accordance with the length of the river to ensure sufficient coverage of the entire river. Collected samples were and stored at 4 °C for geochemical analysis and − 20 °C for microbiological analysis.

Geochemical Analysis Sediment samples were air-dried before being grounded into fine particles and passed through a 200-mesh sieve. For pH measurement, the dried sediment was mixed with 25 ml distilled water in a 100-ml falcon tube. The mixture was shaken for 5 min following by 20 min of equilibration. The pH was then measured with a HACH HQ30d pH meter (Colorado, USA). The total carbon (TC) was measured using an elemental analyzer (Vario MACRO cube, Elementar, Hanau, Germany). To measure the inorganic carbon (IC) and the total

Impacts of Arsenic and Antimony Co-Contamination on Sedimentary Microbial Communities in Rivers with...

organic carbon (TOC), the samples were digested with 5% HCl to remove IC and then analyzed with an elemental analyzer. The total concentration of metal(loid)s, including As (Astot) and Sb (Sbtot), as well as Fe, Mn, Cu, and Mg, was measured on an atomic fluorescence spectrometer (AFS-920, Jitian, Beijing, China) after complete digestion of the dry sediments with HNO3 and HF (5:1, v/v). Certified reference materials (SLRS5, National Research Council, Canada) and internal standards (Rh, 500 μg/L) were analyzed for quality control purposes. The standard reference material GBW07310 (Chinese National Standard) was analyzed for calibration [43]. Different bioavailable fractions of As and Sb in the sediment were determined by a combination of the sequential extraction protocol (SEP) from Wenzel et al. [44] and the citric acid extraction protocol adapted from Ge et al. [45]. The first step was to extract nonspecifically absorbed As and Sb (Asexe and Sbexe) by mixing 1 g soil with 10 ml 0.05 M (NH4)SO4 at 20 °C for 4 h. The second step was to extract As and Sb that could be released by the addition of weak organic acid (Ascit and Sbcit) by adding 0.2 g of soil to 10 ml 100 mM citric acid (pH 2.08) and shaking for 1 h, followed by equilibrating at room temperature for 4 h [40]. The mixtures were then centrifuged and filtered. The filtrate was then analyzed on a HGAFS (AFS-920, Jitian, Beijing). The third step was to extract specifically absorbed As and Sb (Assrp and Sbsrp) phase by mixing 1 g soil with 10 ml 0.05 M NH4H2PO4 at 20 °C for 16 h. After digestion, the mixtures of the previous steps were centrifuged and filtered. The filtrate was analyzed by atomic fluorescence spectrometry (AFS-920, Jitian, Beijing, China).

DNA Extraction and Sequencing DNA of the sediment samples was extracted using a MoBio Powersoil kit (MoBio Laboratories, Inc., California, USA) following the manufacturer’s protocol. Extracted DNA was stored at − 80 °C until further processing. The V4-5 region of the 16S rRNA gene was amplified with the primer set 515F (5′G T G Y C A G C M G C C G C G G TA A ) / 9 2 6 R ( 5 ′ CCGYCAATTYMTTTRAGTTT) [46]. The amplicons were barcoded and sequenced on an Illumina MiSeq at the Ecogene bioinformatic company (Shenzhen, China) according to established methods [47–49]. Sequence libraries were analyzed using multiple bioinformatics tools. Paired-end reads were merged using PEAR with default setting. The merged sequences were then demultiplexed with vsearch and trimmed using mothur (tdiffs = 5, qwindowsize = 50, qwindowaverage = 30, flip = T, maxambig = 0) [50], respectively. Chimeras were detected and removed with vsearch with both denovo and against 16s reference database. Dereplicated sequences were clustered into operational taxonomic units (OTUs) using UPARSE with a threshold of 97% similarity [51]. Representative sequences were then taxonomically assigned

via the RDP classifier with a minimum confidence threshold of 80% [52] against the Greengene database V13-5 [53]. The sequence libraries were uploaded to the NCBI database under the accession number PRJNA497181.

Statistical Analysis The OTU table was imported to the R (v3.4.3) package phyloseq (V1.25.2) [54]. The OTU counts were normalized by cumulative sum scaling (CSS). Community alpha diversities with pairwise comparisons were generated using the R package MicrobiomSeq (V0.1) [55]. The PCoA graph of bray-curtis distance that displays the community beta diversity was generated using the R package phyloseq. The bar graph elucidated the community composition at the phylum level (class level for Proteobacteria) was plotted using ggplot2 [56]. Predictions of the relative importance of individual environmental parameters on the community alpha diversity (observed species index) were calculated using the machine learning algorithm Random forest (RF) model, following the descriptions in previous literature [57]. A two-dimensional interaction plot that described community response to individual parameters was visualized using the R package ggplot2. The OTUs that were tested by the Kruskal-Wallis test, which is a test for whether samples originate from the same distribution, for the impact of As and Sb concentrations. The taxa that were significantly impacted are listed. Co-occurrence networks were generated using Cytoscape to visualize the pairwise correlations between the individual OTUs [58]. The connections in the genus-genus network indicated very strong (|r| >0.8) and significant (p < 0.05) Spearman correlations. The node size was in proportion to its number of connections. The absolute Spearman correlation value was in proportion to the thickness of the edge. The calculated networks were visualized using the interactive platform Gephi [59].

Results Geochemical Characteristics The environmental parameters were measured and compared among the three different sites, and the results were shown in Fig. 1. The majority of the samples had a circumneutral pH, except for 3 acidic samples (pH < = 6). The average values were significantly different (p < 0.05) between ND (7.56 ± 0.25) and the other two sites but were not different between DZ (7.19 ± 0.18) and ZXG (6.51 ± 1.3), which could attribute to fewer available samples from DZ, as the creek was much shorter. The contaminant (As and Sb) concentrations varied significantly among the three rivers. The overall concentrations decreased in the order of ZXG, DZ, and ND. The As

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concentrations were significantly different (p < 0.05) among the three sites for all fractions, except between DZ and ZXG for the Asexe fraction. The highest concentration for Astot was the ZXG, which contained 463 ± 196 mg As/kg soil, followed by DZ, which contained 120 ± 15 mg As/kg soil. The lowest Astot was found in ND, which was 91 ± 33 mg As/kg. The majority of the total As was in the nonbioavailable forms [40]. In all three sites, 0.1% to 1.47% of the total As was in the exchangeable phase, followed by the citric extractable phase, which in average constituted 1.7%, 3.1%, and 7.5% of the total As in ND, DZ, and ZXG, respectively. Assrp accounted additional 3.66%, 5.64%, and 8.6% of the total As in ND, DZ, and ZXG, respectively. The highest Sbtot concentration was also found in the ZXG, with an average concentration of 462 ± 373 mg Sb/kg soil. The average Sbtot content in DZ was 376 ± 73 mg Sb/kg soil, followed by 248 ± 165 mg Sb/kg soil in ND. The other fractions also followed the same trend, with the highest concentration in ZXG, followed by DZ and ND. Significant differences (p < 0.05) were observed between ND and the other two sites but not between DZ and ZXG in all fractions, except for Sbcit. The relative abundance of the Sbexe fraction was significantly higher (p < 0.5) than that of Asexe, ranging from 0.26 to 6.07%. The other two fractions (Sbcit and Sbsrp), however, were not significantly different from the corresponding As fractions.

Microbial Community Analysis Illumina Miseq sequencing generated an average of 100,000 raw reads per sample. After quality control and removal of chimeras, an average depth of 71,500 sequences per sample remained. Before further processing the data, all libraries were rarified to the same depth (18,494 seqs per library). Microbial analysis (Fig. 2) indicated that community richness (observed species index) was significantly (p < 0.05) lower in ZXG compared to the other two sites, while the Simpson index suggested no difference among all three sites, and the Shannon index revealed that only ZXG and DZ had differences. Beta diversity analysis demonstrated that the microbial community was clustered based on sample sites (Fig. 3). The x-axis of the PCoA plot explained 15.7% of the variance in the microbial community, while the y-axis explained 9.2%. Relative community compositions at the phylum/class level were shown in Fig. 4. The most abundant phylum was Proteobacteria, which constituted 45% of the overall microbial community. Within the phylum, Beta-, Alpha-, Gamma-, and Delta-proteobacteria contributed 17%, 13%, 11%, and 3% of the total community, respectively. The relative abundance of the phylum Proteobacteria decreased significantly in ZXG (38 ± 13%) compared to the other two sites (52 ± 9% and 51 ± 11%

Impacts of Arsenic and Antimony Co-Contamination on Sedimentary Microbial Communities in Rivers with...

Fig. 2 Boxplot indicates the microbial community alpha diversities in three different measurements. Color represents different sample regions. * indicates p value < 0.05, ** indicates p value < 0.01

for DZ and ND, respectively) (p < 0.05). Within the phylum Proteobacterium, both Alpha- and Beta-proteobacteria were inhibited by metal(loid)s contamination (p < 0.05), while Gammaproteobacteria was not impacted according

to the t test. Bacteroidetes was the second most abundant phylum (11%), followed by Firmicutes (10%) and Actinobacteria (9%). None of the aforementioned three phyla were significantly different among the sites.

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Fig. 3 PCoA plots show the Bray-Curtis distance of community beta diversity. Symbol colors and shape indicate sample regions

0.765, and average path length is 4.181. In the current study, OTUs have more than 10 strong connections with OTUs is considered the keystone taxa, as they are assumed essential roles in the microbial community [60]. A total number of 50

The biotic interaction analysis of the top 1000 OTUs (Fig. 5) was visualized in a co-occurrence network. The network has 307 nodes and 810 edges, average degree is 2.638, network diameter is 11, graph density is 0.009, modularity is ZXG

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Fig. 4 Bar plot elucidates the microbial community composition of the top ten taxonomic groups at the class level for each individual sample. Samples from the same region are in the same box. The size of the circle indicates the local contribution to beta diversity (LCBD)

Impacts of Arsenic and Antimony Co-Contamination on Sedimentary Microbial Communities in Rivers with... Fig. 5 The correlation network indicates the interactions between the abundant genera with significant (p < 0.05) and very strong (|R| > 0.8) Spearman correlations. The size of each node indicates the number of correlations. The thickness of each edge indicates the strength of Spearman’s correlation coefficient. Color indicates the taxonomic assignment at the phylum level

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keystone taxa were identified, which included 25 interdependent OTUs. A group of 25 OTUs of diverse phyla, mostly P ro t e o b a c t e r i a , B a c t e ro i d e t e s , F i r m i c u t e s , a n d Actinobacteria, formed a strong interdependent cluster without interacting with other groups. Other than the cluster, the Proteobacteria appeared to be essential for the microbial community, as 9 OTUs of this phylum demonstrated strong connections with other taxa groups, including Rhodoplanes, Pedomicrobium, and Hyphomicrobium of the family Hyphomicrobiales, and Bradyrhizobium of the family Bradyrhizobiales. The remaining keystone OTUs were assigned to the phylum Acidobacteria, Actinobacterium, Gemmatimonadetes, Planctomycetes, and Chloroflexi.

Impact of Geochemical Parameters on the Community Diversity To further reveal the contribution of individual environmental parameters to the community diversity, random forest (RF) predictions were employed to explain the efficacy of the geochemical parameters on the microbial community richness (observed species) (Fig. 6a). Overall, the measured geochemical parameters explained 45.46% of the variances of the

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observed species among the samples. Astot, which contributed a 7.1% difference in alpha diversity, was the most effective parameter in shaping the microbial community alpha diversity, followed by Assrp (6.77%), pH (6.23%), Ascit (5.93%), Sbsrp (5.38%), TOC (5.33%), and Cu (5.16%). The scatter plot elucidated the microbial community response to variations in individual environment parameters (Fig. 6b). All As and Sb fractions, except for Asexe, led to reverse U-shaped responses of microbial community richness, with a slight increase in richness at low concentrations and then a rapid decrease with increased contamination level. A similar pattern was also observed for the pH. Community richness was significantly increased as the pH increased from acidic to circumneutral conditions and then slightly decreased as the pH became alkaline. Positive correlations were observed for other parameters, including Cu, TOC, TC, Fe, and Mn. The interactions between the individual taxa and the contaminant fractions were further analyzed to investigate the microbial response to As and Sb contamination. Based on the mean values of the most important As (Astot) and Sb (Sbsrp) fractions, the sites were categorized into high and low contamination conditions. Revealed by the KruskalWallis test, the microbial response towards As contamination

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Fig. 6 Random forest prediction of the relationship between individual geochemical parameters and the microbial community richness. a The bar graph demonstrates the relative contribution of each geochemical parameter to the microbial community richness. b The scatter graph shows the predicted correlation between geochemical parameter

concentration and the observed species. The red line is the partial dependence data line, and the blue line indicates the predicted trendline. The gray area represents the 95% confidence interval of the predicted relationship

was different from that towards Sb. Over 30 genera were significantly impacted (p < 0.05 in the Kruskal-Wallis test) by As (Fig. 7a). In contrast, only 10 genera responded significantly to Sb contamination (Fig. 7b). The relative abundance of the majority of responding genera was negatively correlated with elevated contaminant concentrations, with only several taxa were enriched in high contamination conditions, including Anaerolinea, Opitutus, and Sphingomonas. Many members within the Alphaproteobacteria group, especially members within the order Rhizobiales, were downregulated by As and Sb contamination. Similarly, many members of the phylum Planctomycetes were also inhibited under high contamination conditions.

area, As is another key pollutant with significant environmental concerns due to the co-occurrence of As with Sb-bearing minerals [16]. Although As and Sb are often considered to have similar behaviors, their environmental impacts may be distinct based on geochemical conditions [62]. As a result, bacteria, as the critical mediators for As and Sb geochemical cycling [63, 64], have been reported to react differently towards As and Sb contaminations [40]. Therefore, it is necessary to compare the microbial response to As and Sb contamination in situ, which will provide important knowledge for potential bioremediation strategies. In the current study, comparisons of the three sites provided valuable insights into the microbial responses towards different extents of As and Sb contamination. In ZXG, the slag from the abandoned antimony mine was piled randomly on the ground due to lack of management. In comparison, the active mining site of DZ was organized with less slag exposed. Although ND might be connected with ZXG or DZ through groundwater, the site was considered pristine and was used as a municipal water supply for nearby regions. These differences were reflected in the geochemical conditions, as the highest metalloid concentrations were observed in ZXG.

Discussion China is the world’s largest Sb producer, and the Sb reservoir in Yunnan accounts for approximately 12% of the total Sb reserve in China [61]. Extensive mining activities led to severe environmental problems within this region, especially in the soil and sediment (up to 1163 mg/kg) [10]. In the Sb mining

Massilia padj = 0.048365

Kribbella padj = 0.04565

Arthrobacter padj = 0.024598

Pedomicrobium padj = 0.048365

Pilimelia padj = 0.048365

Phormidium padj = 0.048365

Sedimentibacter padj = 0.04565

Anaerolinea padj = 0.024598

Opitutus padj = 0.048365

Hyphomicrobium padj = 0.016554

Pilimelia padj = 0.00029626

Massilia padj = 8.8632e−07

Candidatus.Koribacter padj = 9.0108e−05

Janthinobacterium padj = 8.8632e−07

Pedomicrobium padj = 9.8064e−06

Bradyrhizobium padj = 0.0067634

Kribbella padj = 0.0031879

Rhodoplanes padj = 0.0001209

b Sphingobium padj = 0.0073878

a

Janthinobacterium padj = 0.03877

Impacts of Arsenic and Antimony Co-Contamination on Sedimentary Microbial Communities in Rivers with...

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Fig. 7 The boxplots show the individual genera that were significantly impacted (adjusted p value) by Astot (a) and Sbsrp (b) concentrations

The concentrations in DZ were only slightly higher than those in ND. The concentrations of both metalloids were within a comparable range to those of previous studies. Hiller et al. reported that the As and Sb concentrations in the contaminated river sediments were also approximately hundreds of mg/kg in an abandoned antimony mine in Slovakia [15]. Similarly, in the Xikuangshan Sb mining area, contaminated soils were also found to contain similar levels of As and Sb [37]. Concentrations of other metals were comparable to typical Karst area sediments [65]. The contamination concentrations in the environment often decrease with distance from the source [66]. In the current study, however, heterogenous distribution of As and Sb with high variations were observed within each sampling site. The similar distribution pattern was also observed in previous studies of soil and river sediments [38, 43]. Sequential extraction is a common method for estimating the distribution of metals in sediments [27, 44, 66, 67]. Using different extraction chemicals, each successive step could resolve the metals in association with different matrix. Mexe indicated the fraction that is nonspecifically binding to the outer-sphere complexes [44]. Mcit and Msrp indicated the fractions binding to Fe and Al oxides and specifically sorbed to mineral surface, respectively [68]. The different fractions indicate the potential of releasing and availability potential to the environment [68]. In the current study, these fractions were considered as bioavailable, which refers to the fractions that could be accessible to plants or animals [69, 70]. The result suggested that the majority of As and Sb was not extractable

by weak acids, indicating that the majority of the As and Sb fractions in the sediments was in association with crystalline forms of Fe and Al oxides or in other minerals, which were considered nonbioavailable [44]. This result was in corroboration with those of the previous literature, which claimed that the majority of As and Sb was fixed within the soil or sediment matrix [71]. Environmental stressors, such as metalloid contaminations, are known to impact microbial community structure and reduce microbial richness and diversity [72, 73]. The bacterial community was suggested to be the most sensitive group of microorganisms to As and Sb contamination [74]. In the current study, dramatic loss in community richness through the measurement of observed species index was only found in the heavily contaminated ZXG river, with no significant differences between ND and DZ. This finding suggested that As and Sb contamination might have only minor impacts on the microbial community richness at low concentrations. The stimulation effect might be caused by the Hormesis effect, which indicated that low levels of toxicity might actually stimulate microbial activities [75]. In fact, diverse microbial groups have been proven to tolerate As and Sb contamination by detoxification or metabolization mechanisms [76, 77]. A further RF model was employed to investigate the effects of various geochemical parameters, including metalloid fractions, on the community richness as expressed by observed species. Accordingly, different metalloid fractions had profound impacts on the community structures. The Astot was predicted to possess the most significant impacts on the

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community diversity derived from the RF model (Fig. 6), even though a major portion of As was nonbioavailable [67]. Although bioavailable fractions are considered to be critical [41], Kim et al. suggested that the nonbioavailable portion of As could impact the microbial community in contaminated soils [27]. Furthermore, previous studies have demonstrated that microbes can directly interact with the mineralized form of As through metal reduction under anaerobic conditions [78, 79]. The interaction, as reviewed by microscopic evidences, could be done by both direct attaching to the mineral or modifying the surrounding chemical to solubilize the metal compounds [80]. Overall, as suggested by RF predictions, As fractions were more profoundly impacted the community diversity in comparison to Sb fractions. This result was in corroboration with those of the previous literatures in which As fractions had higher impacts on the microbial community than Sb fractions [40]. In contrast, Wang et al. found that Sb was more critical in shaping the microbial community compared to As [37]. Luo et al. also predicted that the microbial community was more sensitive to Sb than As [38]. The concentrations of Sb, however, were four times higher than the concentration of As in their study. Combining these results, the difference observed in the relative contributions of targeted contaminants to the microbial community was governed by complex environmental parameters and appeared to be dose-dependent. The remaining parameters that could govern the variations in the microbial community structure include the biotic factors, such as bioturbation, as biotic interactions between various biological guilds could also introduce variations in the community composition [81]. Unfortunately, these parameters cannot be measured and thus we cannot quantify their effects on microbial communities. It has been reported that more than 20% data could be explained is reasonable [40]. In this study, more than 50% data could be studied is acceptable for the current measuring techniques. In the current study, microbial groups expressed a taxaspecific response to As and Sb contaminations. The most influential fractions on the contaminant were sufficient to demonstrate the impact of the contaminant on the microbial community. As an effective tool for analyzing differential expression of marker gene, the Kruskal-Wallis test is sensitive for significantly impacted taxa in microbial communities [82]. Positive response in microbial relative abundance towards to the most influential As and Sb fraction indicates the resistance or tolerance of the microorganisms to the metalloid contamination. These groups of microorganisms are of specific interest as they hold the potential to mitigate the toxicity of these compounds [83]. Based on the analysis, only a small portion of the taxonomic groups were upregulated in the heavily contaminated samples and were selected for further discussion. The genus Anaerolinea was a genus of low abundance at clean sites but was significantly enriched in heavily contaminated

sites. This genus was characterized as an obligate anaerobe within the phylum Chloroflexi and was found in various Asand Sb-contaminated sites [84, 85]. In fact, the taxa and their close relatives (within the same family) were widely distributed in As-contaminated rice paddies in Bangladesh, China, and the UK [86]. Gorra et al. found that Anaerolinea was associated with Fe-As precipitates in the groundwater system in Bangladesh, suggesting that it could potentially interact with nonbioavailable fractions of As [87]. Another genus that positively responded to both As and Sb contamination was the obligate anaerobe, Opitutus, from the relatively less known phylum Verrucomicrobia as the taxa could hardly be grown by traditional cultivation-based methods [88]. The type strain of this genus, O. terrae, was originally isolated from paddy soil [89]. The genus Opitutus was ubiquitously present in As-contaminated soil environments [90, 91]. Members of this genus were reported to possess dissimilatory As reductase to reduce As(V) to As(III) and could excrete As(III) through ACR3encoded membrane transport protein [92]. Sphingomonas responded positively towards elevated As contamination but not towards Sb. This aerobic microorganism was taxonomically classified within the Alphaproteobacteria groups and has been found in a broad range of habitats, with a substantial portion of the isolates from contaminated environments [93]. This group could tolerate elevated As concentration by aerobically reducing As(V) to As(III) as a detoxification mechanism [94]. Because of its relatively high tolerance to diverse contaminants, the genus was suggested not only to be widely distributed but also to be highly abundant in As-contaminated soil environments [86]. Although it did not show tolerance to Sb contamination in the current study, this genus was enriched in Sb-contaminated river sediments in other environments and was correlated with potential Sb biogeochemical cycling [9]. Instead of being enriched, a substantial portion of the native microbial community was inhibited by As and Sb contamination. Among these taxa, the Janthinobacterium was of specific interest because it was the most abundant OTU within the microbial community. In the current study, this group was downregulated by elevated As and Sb concentrations, and in specific association with the most soluble Asexe fraction. This result contradicted previous findings, which suggested that members of this genus could tolerate high As concentrations by oxidizing As(III) to As(V) or reducing As(V) to As(III) by a detoxification mechanism [41, 92, 95]. By blasting the sequence against the NCBI 16s rRNA database, the OTU was 99% similar to both Janthinobacterium svalbardensis and Massilia aurea. Both species were isolated from pristine environments and could potentially be sensitive to As and Sb contamination [96, 97]. This result suggested that microbes, even within the same genus, could belong to different phenotypes and respond differently towards environmental variables [98].

Impacts of Arsenic and Antimony Co-Contamination on Sedimentary Microbial Communities in Rivers with...

Elevated As and Sb contamination demonstrated substantial impacts on the keystone taxa. The keystone taxa are considered the drivers of the microbial community and provide critical ecological roles to sustain the health of the microbial ecosystems [60]. The high abundance of Proteobacteria led to the fact that many members of the taxa were identified in the correlation network and were suggested to constitute a crucial part of the indigenous microbial community. In the current study, the members of the family Hyphomicrobiaceae and Bradyrhizobiaceae within the order Rhizobiales showed significant negative impacts on As and Sb contamination. As shown by the co-occurrence network, these groups had strong interactions with many other microbial groups in the community. In previous studies, these groups were also identified as the keystone taxa in various ecosystems [99–101]. Other studies suggested that they could provide essential services to the ecosystem, such as nitrogen fixation [102] or organic carbon biodegradation [103]. The exact impacts of As and Sb contamination on environmental functions, however, require further investigation, as most previous studies have mainly focused on the dynamics of As-related genes [38, 86, 92]. The 25 interdependent microbial OTUs are of low abundance in the microbial community and had no connections outside the guild. Based on literature reviews, many of them were previously reported as denitrifiers, such as Biolophila [104], Petrobacter [105], Paraprevotella [106], Hydrogenophilus [107], and Aggregatibacter [108]. Thus, the group of microorganism could potentially share similar niches in the soil matrix, and therefore were identified as strong connections among each other [109]. The current study had revealed the environment-microbial interactions in river sediments with different As and Sb contamination levels. In general, the native microbial community was sensitive to elevated As and Sb contamination, although certain groups could tolerate high contaminant concentrations. Although the majority of the As and Sb in the sediment is considered nonbioavailable, they still demonstrated strong and significant interactions with indigenous microorganisms. In such environment, keystone taxa, such as the members of the family Hyphomicrobiaceae and Bradyrhizobiaceae, were inhibited by the contamination, which could impair critical environmental services provided by these groups. Further studies are suggested to focus on the changes in detailed ecosystem functions in contaminated sites to further understand the impacts of As and Sb contamination. Acknowledgements We thank Hanna Han and her team from Shenzhen Ecogene Co., Ltd. for their technical service. Funding Information This research was funded by GDAS’ Project of Science and Technology development (2017GDASCX-0106, 2019GDASYL-0103047, 2019GDASYL-0302006, and 2018GDASCX-0601), the National Natural Science Foundation of China (41771301, 41420104007), the High-level Leading Talent

Introduction Program of GDAS (2016GDASRC-0103), and the Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program (2017BT01Z176).

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