Impacts of antimony and arsenic co-contamination on the river sedimentary microbial community in an antimony-contaminated river

Journal Pre-proof Impacts of antimony and arsenic co-contamination on the river sedimentary microbial community in an antimony-contaminated river

Rui Xu, Xiaoxu Sun, Feng Han, Baoqin Li, Enzong Xiao, Tangfu Xiao, Zhaohui Yang, Weimin Sun PII:

S0048-9697(19)36447-2

DOI:

https://doi.org/10.1016/j.scitotenv.2019.136451

Reference:

STOTEN 136451

To appear in:

Science of the Total Environment

Received date:

24 September 2019

Revised date:

29 November 2019

Accepted date:

30 December 2019

Please cite this article as: R. Xu, X. Sun, F. Han, et al., Impacts of antimony and arsenic co-contamination on the river sedimentary microbial community in an antimonycontaminated river, Science of the Total Environment (2018), https://doi.org/10.1016/ j.scitotenv.2019.136451

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© 2018 Published by Elsevier.

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Impacts of antimony and arsenic co-contamination on the river sedimentary microbial community in an antimony-contaminated river Rui Xu 1, Xiaoxu Sun 1, Feng Han 1, Baoqin Li 1, Enzong Xiao 2, Tangfu Xiao 2, Zhaohui Yang 3, Weimin Sun 1,# 1

Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management,

Guangdong Institute of Eco-environmental Science & Technology, Guangzhou 510650, China

Key Laboratory of Water Quality and Conservation in the Pearl River Delta, Ministry of Education, School

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College of Environmental Science and Engineering, Hunan University, Changsha 410082, China Corresponding author: Weimin Sun ([email protected]) at 808 Tianyuan Road, Guangzhou, Guangdong,

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of Environmental Science and Engineering, Guangzhou University, Guangzhou 510006, China

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Abstract

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China.

Antimony (Sb) and arsenic (As) are toxic elements that occur widely in trace soil concentrations. Expansion

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of mining activities has increased Sb and As pollution, thus posing a severe threat to human welfare and ecological systems worldwide. Knowledge regarding the composition and adaptation of the microbial communities in these metal(loid) contaminated sites is still limited. In the current study, samples along a river flowing through the world’s largest Sb mining area (Xikuangshan) were selected to investigate the microbial response to different Sb or As species. A comprehensive analysis of geochemical parameters, high-throughput sequencing, and statistical methods were applied to reveal the different effects of Sb and As on sedimentary microorganisms. Results suggested that the majority of the Sb and As fractions were not bioavailable. The Sb extractable fraction had a stronger effect on the microbial community compared with its As counterpart. Random forest analyses indicated that the easily exchangeable Sb fraction and specifically sorbed surface-bound fraction were the two most selective variables shaping microbial community diversity. A total of 11 potential keystone phyla, such as bacteria associated with the Bacteroidetes, Proteobacteria, and Firmicutes, were identified according to a molecular ecological network analysis. Strong correlations (|R| > 0.7, P < 0.05) were identified among the indigenous microbial community and pH (negative), sulfate

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(negative), and exchangeable Sb fraction (positive). Bacteria associated with the genera Geobacter, Phormidium, Ignavibacterium, Desulfobulbus, Ferruginibacter, Fluviicola, Methylotenera, and Scytonema, were predicted to tolerate or metabolize the Sb extractable fraction.

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Keywords: antimony, arsenic, mining pollution, sediment, microbial community

1.

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Introduction

Antimony (Sb) and arsenic (As) are group 15 elements in the periodic table that occur ubiquitously in the environment at trace levels. Sb is not only a pollutant, but also a suspected carcinogen, based on the classification of the US Environmental Protection Agency and the European Union (He et al., 2019). Sb shares some similar chemical and toxicological properties with As, which has been studied extensively (Matschullat, 2000). The distribution of Sb is frequently associated with As in many sulfide-rich ore mining areas. Co-contamination of Sb and As has been reported frequently at many Sb-contaminated sites

(Palmer et al., 2019; Sun et al., 2017; Wu et al., 2019b). Natural processes and anthropogenic factors cause

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Sb and As pollution in the ecosystem. Volcanic activity and weathering of Sb-bearing crustal rocks/minerals

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are two important natural sources (Li et al., 2018). Background concentration of Sb in soil is low (<

1mg/kg), suggesting that natural processes may not be the major Sb contamination source (Li et al., 2018).

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Instead, metalliferous ore-mining activities have disturbed the natural equilibrium and increased the levels of

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Sb and As pollution (He et al., 2019).

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The current reserves of Sb are estimated to be more than 1,800,000 tons, and particularly Southwest China, accounts for > 80% of global production capacity (Dupont et al., 2016). Among various Chinese Sb mining

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areas, the Xikuangshang mining area, located in Southwest China, is the world’s largest Sb mine. A previous survey showed that the waste heaps and tailing ponds from the north mine have the highest Sb and As

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concentrations. Levels of Sb in water samples from the Xikuangshan mining area reach > 6000μg/L, which is much higher than the global average value (1μg/L) (He et al., 2019). Extremely high Sb concentrations have severely contaminated this area and endangered the exposed population (Zhou et al., 2016). Rivers flowing through Sb mining areas are an important contamination source, as they carry contaminated river water for long distances and contaminate downstream areas. In the case when they are used to irrigate agricultural fields, the Sb and As will directly endanger the exposed populations. Previous studies observed high concentrations of Sb and As in rivers located in Sb mining areas (Sun et al., 2016a; Xiao et al., 2016b). Our previous studies indicated that river sediments are a hotspot for microbial activity, especially for potential microbial-mediated As and Sb cycling. In addition, the Sb concentration fluctuates substantially in a contaminated river. For example, levels of Sb and As in the sediment around tailing ponds decrease dramatically from ~20,000mg/kg (dw) to ~1,000mg/kg (dw) over a distance of 150m (Warnken et al., 2017). These changes in physical and chemical properties further shape the activity and composition of the

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sedimentary microbial community, which is critical for biogeochemical cycling of Sb and As in the environment (Mateos et al., 2006). It is reported that the processes of reduction, oxidation, and methylation/demethylation of Sb and As polyvalent metalloids are actively mediated by various groups of microbes in many habitats (Li et al., 2016). The Sb/As biotransformation process could alter their toxicity, mobility, and bioavailability, which might ultimately lead to accumulation or translocation in sediments (Sundar and Chakravarty, 2010). Previous studies have suggested that a wide range of phylogenetic taxa are involved in such biotransformation processes. For instance, many Sb(V)-reducing bacteria (such as Sinorhizobium) and Sb(III)-oxidizing bacteria

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(such as Pseudomonas and Acinetobacter) have been identified or isolated in Sb mine-related sediments or

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soils (Hamamura et al., 2013; Jie et al., 2013; Nguyen and Lee, 2014). The speciation, mobility, and concentrations of Sb and As may change along the length of a river as well as their bioavailability. Such

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variations can alter the microbial community, and in turn, the microbial response to such changes may

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influence the behavior of Sb and As. However, knowledge regarding the correlations between indigenous

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microbes and the bioavailability of Sb and As is relatively limited. In the current study, a series of sedimentary samples was collected from a river passing through the

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Xikuangshan Sb mining area. The geochemical parameters and bacterial communities in the sediment were measured with respect to water flow. Random forest and network analyses were applied to investigate the

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correlations between the environment and microbes. The hypothesis of this study was that the bioavailability of Sb and As in the sediment soil would differ according to water flow and further influence the microbial community.

2.

Materials and methods

2.1 Sampling and location of sampling sites Sediment samples were collected near the shore (ca. 1m distance). The sedimentary samples were collected from a severely Sb and As co-contaminated river passing through the abandoned Xikuangshan mine (27.7°N, 111.4°E), located in Lengshuijiang city, Hunan province, which was the largest Sb mine (stibnite-Sb2S3 as the main ore mineral) in China. The Sb deposit consists of one north mine and one south mine. The contaminated fields of Sb deposit cover an area over 70km2 (Ye et al., 2018). Mining activities have generated huge amount of waste dumps containing high concentrations of Sb and As. A river receiving mine drainage was considered

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in this study. Five sites were selected during the wet season of May (2018) alongside the river flow from upstream (close to the mine) to downstream to cover the contaminated area, namely S1 to S5 (Fig.1). A GPS was used to mark the sampling locations. At each sampling site, a sterilized scoop was used to collect the surface sedimentary soil from 0 to 5cm. Each site was in quadruplicated and a total of 20 samples were collected. All the sedimentary samples were stored at 4°C immediately for the following geochemical analysis and −80°C for the microbiological analysis.

2.2 Geochemical analysis

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2.2.1 General characterization

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About 10g fresh sample was ground into fine particles via passing through a sieve (around 200-mesh) then

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then mixed with certain volume of distilled water (25mL) following by 20min of equilibration. The supernatant was used for the pH measurement with a pH meter (HACH HQ30d, Colorado, USA). The

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well-mixed mixture was then centrifugated (4000g, 10min) and filtered (0.45μm membrane) for the

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determination of the concentration of sulfate (SO42-) and nitrate (NO3-) with the ion chromatography (IC, DIONEX ICS-40, USA). Ferrous and ferric irons were detected using the spectrophotometric method

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(UV-9000s, METASH, China) (Sun et al., 2015). Total organic carbon (TOC) was determined with an

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elemental analyzer (vario MACRO cube; Elementar, Germany). 2.2.2 Total concentration of As and Sb

For the determination of total concentration of As (Astot) and Sb (Sbtot), fresh soil samples were air-dried and then completely digested with HNO3 and HF (5: 1, volume ratio). Then digested samples were analyzed with an atomic fluorescence spectrometer (AFS-920, Jitian, Beijing, China). The standard reference material of Sb/As was utilized to control the quality, according to the Chinese National Standard (GBW07310) (Sun et al., 2016b). The detection levels reached 0.02 mg/kg (dw). 2.2.3 Bioavailable fraction of As and Sb In addition, according to the binding strengths between the soil matrix and different species of metal(loid)s, the metal distribution of As and Sb within a sediment sample could be classified into: (1) the non-specifically absorbed metal(loid)s in exchangeable forms (extracted by MgCl2, pH=7)); (2) the specifically absorbed fractions (extracted by NaH2PO4); (3) the Sb/As in the form of Fe-Mn oxides (extracted by ammonium

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oxalate, pH=3); (4) organic matter/amorphous sulfide fraction (extracted by HNO3+H2O2+NH4OAc), and (5) the residual fraction (digested by HNO3/HF) (Buanuam and Wennrich, 2010; Wang et al., 2011b). The associations with these fractions of Sb and As in soil usually influenced their mobility and bio-accessibility. Above fractions of Sb and As in the sediment samples were evaluated using a modified sequential extraction procedure (SEP) (Tessier et al., 1979). Notably, the current study only considered the non-specifically absorbed and easily exchangeable fraction (-exe, Sbexe and Asexe), as well as the specifically absorbed surface-bound fraction (-srp, Sbsrp and Sbsrp), due to their bio-accessibility to soil microorganisms (Sun et al., 2019a). These two fractions indicate the potential of releasing and availability of Sb and As to the environment (Filella, 2011), which were considered as bioavailable to plants or animals (He, 2007). The

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details of extraction conditions, such as the reagents and reaction times, were described elsewhere (Savonina

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et al., 2012; Sun et al., 2019b). After extraction, the supernatant was centrifuged (4000g, 15min) and filtered

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(0.45μm membrane) for the analysis at the end of each extraction state using a hydride generation atomic fluorescence spectrometer (HG-AFS-920, Jitian, China). The analysis quality was guaranteed by the

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determination of standard reference sediment GBW (E) 070003 obtained from the China National Research

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Center for Certified Reference Materials (Wang et al., 2011b). Collectively, this study used the term

-srp) as described above.

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2.3 DNA extraction

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―co-contaminant fractions‖ to refer to the total As/Sb (tot) and various extractable As/Sb fractions (-exe and

Extraction of genomic DNA of the sediment samples (ca. 0.5g well-mixed soil) was performed with a MoBio Powersoil kit (MoBio Laboratories, Inc., USA) following the manufacturer’s instruction. The quality and concentration of genomic DNA were determined by the NanoDrop spectrophotometer and 1% (w/v) agarose gels electrophoresis (Xu et al., 2018a; Xu et al., 2018b).

2.4 PCR, sequencing, and bioinformatic analysis The universal primer set of 515F/926R was applied to amplify the V4-V5 region of the 16S rRNA gene (Sun et al., 2019b). The amplicons were barcoded and sent to Novogene for high-throughput sequencing (Novogene Bioinformatics Company, China, Illumina MiSeq platform) (Xu et al., 2017). The 16S rRNA amplicon sequences were archived in the NCBI SRA database (PRJNA554201). Raw sequence data were merged, filtered, clustered into operational taxonomic units (OTUs, with a similarity of 97% as the threshold), and annotated (Green Genes Database) following the established pipelines (Sun et al., 2019a; Sun et al.,

2019b).

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2.5 Statistical analysis The OTU counts were normalized in R (v 3.5.2) before statistical analysis. Community alpha diversity indices of community, including Shannon, observed species number, Simpson, ACE, Chao 1, and PD whole tree index, were determined with R package (MicrobiomSeq). The composition of community was visualized using the bar graph and heatmap with R package (ggplot2). Statistical analysis to determine the significant difference was performed using SPSS (v 19), including the two-sided t-test, Tukey test, and Wilcox test. The

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molecular ecological network analysis (MENA) of individual OTU was performed using the online pipeline (Deng et al., 2012). Only the top 1000 abundant OTUs in 80% samples (16/20) were selected (Xu et al.,

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2018b). Module separation and modularity were calculated using the greedy algorithm. Keystone taxa were

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jointly evaluated with three topological parameters, including the degree centrality, betweenness centrality, and stress centrality (Banerjee et al., 2018). Degree of a node represents the number of its adjacent edges.

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Betweenness is defined by the number of geodesics (shortest paths) going through a node or an edge. Stress

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centrality is similar to betweenness. High stress node could serve as a broker (Deng et al., 2012). The two-dimensional interaction (Pearson correlation index) between the selected microbial taxa and the

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individual parameter was calculated in the R package (ggcorrplot). Co-occurrence network was constructed in Cytoscape software to present the pairwise interactions between the environment-microbe (Xu et al., 2018b).

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The top 1000 OTUs were filtered to calculate the strong correlation (|R| > 0.7, P < 0.05). The absolute value of Pearson correlation was proportional to the thickness of connected edge. The color of the edge represented a positive (red) or negative (blue) correlation. The color of the node represented the phylum of each OTU. Random forest (RF) model, an advanced machine learning algorithm, was performed to predict the variable importance of individual environmental factor on the alpha diversity of microbial community (observed species index) in R package (randomForest).

3.

Results

3.1 Characteristics of sedimentary samples The overall geochemical parameters of the five different sampling sites were shown in Fig.2. The pH values at the five sites averaged 7.8–8.1 (Tukey’s test, P > 0.07). The concentration of total organic carbon (TOC) was much higher near the contamination source (S1) than at the downstream sites (S2–S5). The average TOC

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values were significantly different between S1 and the other sites (Tukey’s test, P < 0.01) but not among S2 to S5. The sulfate concentrations showed an opposite trend to that of the TOC. The concentrations of Fe(II), as well as total iron (Fetot, Fe(II) plus Fe(III)) in S1 were higher than 200 and 1000mg/kg, respectively, then decreased significantly with water flow (Tukey’s test, P < 0.01).

This study utilized the sequential extraction method to estimate the distribution of Sb and As (M) in sediment or soil samples (Kim et al., 2014). Two bioavailable extractable fractions were selected: Mexe represented the non-specifically bound fraction to the outer-sphere complexes, while Msrp represented the specifically absorbed fractions to the mineral surface. Overall, the concentrations of Sb and As varied from S1 to S5. The

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concentrations of total Sb (Sbtot) and total As (Astot) were > 4500 and 1300mg/kg, respectively. The highest

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Sbtot and Astot concentrations were 7451 ± 587 and 1749 ± 115mg/kg soil (dw) in S1, respectively. These results indicate that the closer to the mining area (S1), the higher concentrations of Sbtot and Astot. In addition,

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the concentrations of Asexe in five sites were less than 0.10 mg/kg. The concentrations of Sbexe in five sites

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were higher than Asexe, reached 2~6 mg/kg. The concentrations of Assrp decreased from S1 (around 25 mg/kg) to S5 (around 15 mg/kg). The concentrations of Sbsrp also showed a decreasing trend from S1 (around 28

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mg/kg) to S5 (15 mg/kg). Notably, significant differences (Tukey’s test, P ≤ 0.01) in the concentrations of all Sb fractions (including Sbtot, exchangeable fraction (Sbexe), and specifically absorbed surface-bound fraction

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(Sbsrp) were observed between S1 and the other four sites (S2–S5). Besides, the majority of Sbtot and Astot

(Table S1).

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were not bioavailable because the proportions of -exe and -srp fractions were less than 2% at all five sites

3.2 Microbial community composition

Raw reads of sequencing were generated by Illumina Miseq platform. After quality control and rarefying to the same depth, the alpha diversity of the microbial community was evaluated using six indices (Table 1). Notably, the Shannon and Simpson indices were slightly higher at downstream sites compared with upstream sites (except S4), suggesting that the bacterial communities around the mining area were less diverse. Tukey’s test indicated that the alpha diversity of the microbial community was not significantly different (P > 0.05). The Wilcoxon test suggested a significant difference between S5 and the other samples (P < 0.05).

The relative abundance of the sedimentary samples at the phylum level was shown in Fig.3. Generally, the top 10 phyla constituted over 95% of the whole microbial community. Among them, the most abundant phyla were Proteobacteria, Bacteroidetes, Firmicutes, and Cyanobacteria, with average proportions of 35 ± 7%, 30

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± 10%, 14 ± 10%, and 8 ± 8% among all samples, respectively. The other phyla comprised less than 2%. Within the phylum Proteobacteria, Beta-, Delta-, Alpha-, and Gamma- proteobacteria (at the class level) contributed 11 ± 3%, 9 ± 5%, 8 ± 3%, and 6 ± 2% to the whole community, respectively (Table S2). Furthermore, the relative abundances of the specific genera that varied with river flow (from S1 to S5) were filtered and presented in Fig.4. The hierarchical clusters indicated that the relative abundance of one group increased from upstream to downstream, while that of another group decreased. Anaerocella, Christensenellaceae_R-7 group, Novosphingobium, Perlucidibaca, Romboutsia, Terrisporobacter, Thauera, Trichococcus, Turicibacter, and vadinBC27 (alpha-beta order) were more abundant in the downstream

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samples, whereas Arthronema, Aureispira, Ferruginibacter, Fluviicola, Methylotenera, Phormidium,

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Prolixibacter, Scytonema, and Sulfuritalea (alpha-beta order) were enriched at the contamination source. For instance, the relative abundance of the genus vadinBC27 increased from 2.07 ± 0.44% at S1 to 7.41 ± 0.92%

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at S5. Similarly, the genus Perlucidibaca increased from 0.04 ± 0.01 at S1 to 0.21 ± 0.04% at S5. In contrast,

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the relative abundance of the genus Scytonema decreased gradually from 8.05 ± 0.79% to 0.61 ± 0.03%.

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3.3 Microbe-microbe interaction network

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Arthronema and Phormidium also showed the same trend.

The top 1000 OTUs were selected for molecular ecology network analysis (MENA) and visualized by

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Cystoscope (Fig.5-a). Based on the random matrix theory, the MENA network generated 1816 edges and 517 nodes with a strong correlation (|R| > 0.9, P < 0.05). More details of the global properties of the network were shown in Table S3. Based on the ecological theories of microbial network, the nodes with high centrality of degree / betweenness/stress, as well as eigenvector, was usually considered as potential keystone taxa in driving the microbiome structure and functioning. However, the identified species often depends on the selection of topological index (Banerjee et al., 2018; Deng et al., 2012). For example, in the current study, nodes with a maximum degree of centrality, betweenness centrality, stress centrality, and eigenvector centrality were identified to be OTU-4684 (Woodsholea), OTU-3275 (Pseudomonas), OTU-395 (Tahibacter), and OTU-41 (Nitrospiraceae), respectively. OTUs with high degree of centrality could be further assigned into 14 phyla, such as the phyla Bacteroidetes, Proteobacteria, and Firmicutes. These highly connected OTUs were likely to be critical in maintaining the stability of whole microbial community (Banerjee et al., 2018). Current study jointly evaluated the connection properties of the network using betweenness centrality and stress centrality. The distribution curve suggested that the top 100 OTUs contributed mainly to the highly

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connective property of the network (Fig.5-b). OTUs with the top 100 betweenness centrality and stress centrality values were also classified. No matter which topological index was selected (e.g. degree / betweenness/stress), a total of 11 phyla were identified as the highly possible keystone taxa (Fig.5-c), including Acidobacteria, Bacteroidetes, Cyanobacteria, Firmicutes, Gemmatimonadetes, Ignavibacteriae, Nitrospirae, Planctomycetes, Proteobacteria, Spirochaetes, and Verrucomicrobia (alpha-beta order).

3.4 Correlation between microorganisms and geochemical parameters The interactions between the geochemical parameters and abundant OTUs were calculated. Only the pairwise

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links with a significant and strong Pearson’s correlation coefficient (|R| > 0.7 and P < 0.05) were selected for further analysis (Fig.6). Due to the number of connected nodes, elevated pH, SO42-, and Sbexe levels had strong

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effects on the microbes, followed by NO3-, Sbtot, Fe(II), Fetot, and Asexe levels. The interactions between

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individual geochemical parameters and the OTUs were separated into five modules based on the network profile, namely module I (Sbexe-Sbsrp-sulfate), module II (pH), module III (nitrate), module IV (Sbtot), and

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module V (Asexe). The separated modules have been suggested to be different functional components within

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the entire microbial community (Deng et al., 2012). Based on the connection profiles, the response of microbial relative abundance towards individual geochemical parameters presented two patterns. Most of the

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OTUs were negatively affected by pH and SO42-, such as OTU-80 (family Holophagaceae, -pH pair) and OTU-152 (family Cytophagaceae, -SO42- pair). In contrast, some OTUs were positively correlated with Sbexe

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and NO3-, such as OTU-4675 (family Desulfobulbaceae, -Sbexe pair) and OTU-1642 (family Comamonadaceae, -NO3- pair). These filtered OTUs belonged mainly to the phyla Bacteroidetes, Proteobacteria, and Ignavibacteriae.

The response of the microbes to As contamination (Asexe) was different from that to Sb contamination (Sbexe and Sbsrp). Notably, the majority of Sb-related OTUs were positively correlated with all Sb fractions (Sbexe, Sbsrp, and Sbtot), suggesting that there were certain indigenous groups that can tolerate Sb contamination. These OTUs were assigned mainly to the genera Geobacter (such as OTU-2194, OTU-388, and OTU-548), in addition to Phormidium, Ignavibacterium, Desulfobulbus, and Arthronema (Table S4).

3.5 Impacts of geochemical conditions on the diversity of microbial community Random forest was employed to further predict the contribution of individual geochemical conditions on the alpha diversity of the microbial community (weighted by the observed species index) (Fig.7). The majority of

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the variation shaping microbial community diversity was explained by Sbexe and Sbsrp, which contributed 8.47% and 6.93% to the difference, respectively. The rest of the variation was explained by TOC (4.12%), Fetot (3.19%), Fe(II) (2.04%), and pH (1.55%). The As fractions had less of an impact on community diversity. These results suggest that the effects of Sb and As contamination on the sedimentary microbial community might differ.

4.

Discussion

4.1 Co-contamination of Sb and As in mining area

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The geochemical parameters of five sedimentary samples were presented in Fig.2. pH, nitrate, sulfate, TOC,

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Fe concentration, and Sb/As concentration varied from S1 to S5 alongside the river. S1 (close to mine

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drainage) formed a condition of low pH, low sulfate, but high TOC, high Fe concentrations and high Sb/As concentrations. Especially, the Sb concentration was significantly decreased along with the distance from the

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mine (Tukey’s test, P ≤ 0.01). Moreover, this study used the term ―contaminant fractions‖ to refer to the total

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concentrations of As and Sb (Astot and Sbtot), in addition to the exchangeable fraction (-exe, Sbexe and Asexe) and specifically-sorbed surface-bound fraction (-srp, Sbsrp and Sbsrp). Due to extensive mining actives, Sb

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pollution has been considered a global environmental concern. China has the largest reservoir of Sb worldwide, which accounts for over 80% of global production capacity (Dupont et al., 2016). As the world’s

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largest Sb mining area, the Sb concentrations in the topsoil at the Xikuangshan mine range from 10 to 2159mg/kg (Wang et al., 2010). Another similar study showed that the mining soil contains a high Sbtot content and bioavailable fraction, ranging from 500 to 11,000mg/kg and from 6 to 700mg/kg, respectively (Okkenhaug et al., 2011). These data suggested that Xikuangshan area was severely contaminated by Sb. Furthermore, As is an important toxic element with a major environmental threat due to its co-occurrence with Sb in Sb-bearing minerals (Sun et al., 2019b). Previous reports evaluated the mobility and distribution of Sb and As in the soil around the Xikuangshan mining area (He et al., 2019; Li et al., 2018). Despite that Sb and As were frequently considered to share similar chemical and toxicological characteristics, such as their oxidized forms (Sb (V) and As (V)) were less toxic than their reduced forms (Sb (III) and As (III)), many studies have suggested that Sb and As exert different environmental pressures based on distinct soil conditions (Sun et al., 2019b). In an experiment to test influence of combined pollution of Sb and As on culturable soil microbial populations, microbial counts showed differences at the same dose of two metal(loid)s. Results confirmed that the different growth rates of soil microbial populations in response to these two metal(loid)

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(Wang et al., 2011a). Consistently, the current study further confirmed that the relative contributions of Sb and As to the microbial community structure differed in this contaminated river sediments (Fig.6 and Fig.7).

4.2 Impacts of environmental parameters on the overall microbial community It has been reported that bacteria respond differently towards Sb and As contamination in our previous study (Sun et al., 2017). Additionally, microbes inhabiting mining areas adopt certain survival strategies to utilize these chemical elements (Sun et al., 2018). As critical mediators in biogeochemical cycles, microorganisms are responsible for bioaccumulation, biosorption, and biochemical transformation of Sb/As contamination (He

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et al., 2019), which may hold the potential to remediate Sb and As contamination. Therefore, it is necessary to evaluate the microbial interactions with the Sb and As fractions in the sediment to get a first glance of the

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essential knowledge for bioremediation.

RF model indicated that the Sb fractions had a stronger impact on the alpha diversity of the microbial

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community compared with As, which might be attributed to the higher concentration of Sbtot than Astot in this

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study (Fig.7). Previous reports have shown that microbial communities were more sensitive to Sb than As (Luo et al., 2014). Wang et al. (2016) also reported that the Sb fractions, rather than the As fractions, were

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more profound in shaping microbial community composition. Many studies have also reported that the As fractions exert a stronger influence on the microbial community than do the Sb fractions (He et al., 2019; Li et

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al., 2018). The difference might be governed by other geochemical parameters due to the complexity of the soil ecosystem. The remaining biotic factors, such as bioturbation, also introduce variations in the community composition via biotic interactions among various biological guilds (Sun et al., 2019b). Additionally, co-occurrence network suggested that pH strongly affects the dynamics of microbial community composition (Fig.6). In both terrestrial and aquatic environments, many studies have reported critical impacts of pH on the diversity and composition of the overall microbial community (Li et al., 2015; Xiao et al., 2016b). It was suggested that pH may pose a direct selecting pressure on acidophiles, alkaliphiles, or neutrophils (Sun et al., 2016a). Another possible explanation is that pH can change the solubility of many kinds of solids, and then affect the release of metals (Hu et al., 2016). Stumm and Morgan (1981 revealed that the surface charge of minerals was a pH-dependent factor, which may regulate their dissolution and polarization due to surface chemical bonds. Thus, various pH gradients can influence the behavior of Sb and As fractions and other metal(loid)s (Yasuo et al., 2006), which might further affect the indigenous microbial community. This could be supported by the RF prediction of the influence of pH on different Sb and As fractions (Fig. S1).

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4.3 Response of microorganisms to the co-contamination of Sb and As The interactions between microorganisms and Sb/As include mainly microbial-mediated oxidation and reduction and biotic adsorption by microorganisms, which may influence the mobility and toxicity of Sb and As. These interactions may also affect other biogeochemical processes, such as nutrient cycling, carbon fixation, nitrogen fixation, or soil structure (Youn-Joo and Minjin, 2009). Previous studies demonstrated that the microbial composition and diversity in terrestrial and aquatic environments were regulated by the increased Sb/As concentrations (He et al., 2019; Sun et al., 2017; Sun et al., 2019b; Xiao et al., 2016b). Microbial community structure and diversity have been suggested as potential indicators to reflect the soil

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properties. The current study observed that the microorganisms showed a taxon-specific response to different

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Sb and As fractions. Although alpha-diversity index showed no significant difference among all sites (Table 1), results indicated that the microbial community structure changed with different Sb and As

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co-contamination (Fig.4). A small number of microbial populations were detected in the heavily Sb and As

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contaminated environment, such as Fluviicola (Reis et al., 2013), Methylotenera (Crognale et al., 2017), Sulfuritalea (Xiao et al., 2016a), Scytonema, and Ferruginibacter (Han et al., 2016), suggesting their

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resistance to elevated Sb concentrations. Consistently, Scytonema was reported to able to remove As(III) from

et al., 2006).

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water (pH=6.9) via the formation between sulfhydryl groups of its surface proteins and arsenous acid (Prasad

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Moreover, the ecological network was assessed to investigate the interactions among microorganisms inhabiting contaminated sediments. The strong interactions among microorganisms at the community level were presented using the ecological network (Fig.5). This approach has been suggested to affect the ecological niche more than the independent microbe (Xu et al., 2018b). MENA has been successfully applied in many studies related to various habitats (i.e., anaerobic digestion in plants, oceans, and the human gut) (Banerjee et al., 2018; Deng et al., 2012; Xu et al., 2018b). In the current study, three network topological parameters were used to evaluate the keystone species that were an important ecological niche and the main drivers of the stability and function of the whole microbial community (Banerjee et al., 2018). Among them, OTU-4684 (Woodsholea), OTU-3275 (Pseudomonas), OTU-395 (Tahibacter), and OTU-41 (Nitrospiraceae) were of most interest due to their high connectivity with many other partners. These groups have been identified as keystone species in many habitats , such sediment, soil, river (Sun et al., 2017; Sun et al., 2019b; Wang et al., 2017; Zhao et al., 2016). Their critical functions in the ecosystem, such as biodegradation of organic carbon and nitrogen fixation, have also been reported previously (Buckley et al., 2007; Zhang et al.,

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2019a; Zhang et al., 2019b). For example, many Pseudomonas-affiliated members, such as Pseudomonas arsenitoxidans and Pseudomonas putida, were able to oxidize As fractions (Costa et al., 2014; Sun et al., 2017). Li et al. (Li et al., 2016) reported that Pseudomonas comprises 34% of the known Sb(III)-oxidizing bacteria, indicating their potential important role in the Sb(III) oxidation (Makk et al., 2011).

The correlation network analysis further revealed a positive response of microbes towards Sbexe, Sbsrp and Sbtot, indicating their potential capability to interact with Sb (Fig.6 and Table S4). Among these bacteria, Geobacter spp. was of great interest because it was correlated with two bioavailable fractions of Sb. Although Geobacter have been characterized as As-metabolizing bacteria previously (Giloteaux et al., 2013),

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substantial correlations between the Sb fractions and Geobacter have also been observed in paddy soil and

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Sb-bearing tailing ponds (Majzlan et al., 2011; Sun et al., 2019a). Geobacter reduce metals (e.g. Fe) via respiration using metal(loid)s as electron acceptors (Lovley et al., 1993). One study reported that some genes

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that were highly similar to As metabolic genes have been found in the genomes of certain Geobacter spp.,

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such as the arrA (encoding for the dissimilatory As(V) reductase) and acr3 genes (encoding for the arsenic pump protein Acr3) (Giloteaux et al., 2013). Given that some physicochemical and toxic properties were

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shared by Sb and As, it was hypothesized that Geobacter might be able to bear or utilize certain Sb fractions. Results further suggested that Geobacter may show similar physiological characteristics to contaminants with

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similar chemical properties (i.e. Sb vs. As). Besides, Desulfobulbaceae was positively correlated with the

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Sbexe fraction (Fig.6). Desulfobulbaceae was believed to be well adapted to various environmental stressors, such as extreme shortages of sulfate, cadmium pollution, and petroleum pollution (Liang et al., 2019; Wu et al., 2019a; Xu et al., 2019). The occurrence of Desulfobulbaceae in the Sbexe-associated network suggests its tolerance to Sb stress. Phormidium, in the phylum Cyanobacteria, was also characterized as an abundant genus in an Sb-rich tailing dump from Southwest China, where the Sb concentration was reported to be as high as 5200mg/kg (Xiao et al., 2016a). They further revealed that the relative abundance of Phormidium was strongly associated with geochemical pH, TOC, and the sulfate/sulfide ratio. Phormidium sp. was firstly isolated from an As-contaminated environment and further characterized as an As-tolerant freshwater blue-green alga (Maeda et al., 1988). They tested the As concentration of the cells of Phormidium sp. increased with an increase of the surrounding As concentration up to 7000mg/kg. As Baas–Becking put forward in his famous hypothesis that ―everything is everywhere, but the environment selects‖ (Baas-Becking, 1934). Given that As and Sb shared similar chemical and toxicological properties, the wide occurrence of Phormidium in Sb-contaminated environment highly suggests their critical roles in biogeochemical Sb cycling. However, further research is required to characterize the ecology of Phormidium in Sb-contaminated

sediments.

5.

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Conclusions

This study revealed the strong interactions of environment-microbe in river sediments with different Sb and As contamination levels. The overall concentrations of Sb and As reached over 4500 and 1300mg/kg, while decreased with the increase of migration distance. Although the majority of the Sb and As in the sediment were non-bioavailable fractions, the bioavailable Sbexe and Sbsrp were critical to the microbial community diversity. RF model suggested that Sb were more profoundly impacted the microbial community in

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comparison of As, partially due to the four times higher concentrations of Sbtot than Astot. Certain OTUs related to the genera Pseudomonas, Woodsholea, Tahibacter, and Nitrospiraceae, in addition to 11 other phyla,

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were identified as the potential keystone microorganisms in such severely contaminated environment. The

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genera Geobacter, Phormidium, Ignavibacterium, Desulfobulbus, Ferruginibacter, Fluviicola, Methylotenera, Scytonema, might be able to tolerate or metabolize Sb compounds. Further studies are suggested to focus on

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these members to understand the in-situ bioremediation strategy to relieve the Sb and As contamination in

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Acknowledgments

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sediment.

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This work was supported by GDAS' Project of Science and Technology Development (grant nos. 2019GDASYL-0302006 and 2019GDASYL-0301002); the National Natural Science Foundation of China (grant no. 41771301); the High-level Leading Talent Introduction Program of GDAS (grant no. 2016GDASRC-0103); the GDAS’ Project of Science and Technology Development (grant nos. 2018GDASCX-0601 and 2018GDASCX-0106); the High-Level Talents Project of the Pearl River Talents Recruitment Program (grant no. 2017GC010570); the Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program (grant no. 2017BT01Z176); Guangdong Basic and Applied Basic Research Foundation the National Natural Science Foundation of Guangdong (grant no. 2019A1515011559); and the Science and Technology Planning Project of Guangzhou (grant no. 201904010366).

Conflict of Interest

The authors declare that they have no conflict ofinterest.

References

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Declaration of interests

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√ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐The authors declare the following financial interests/personal relationships which may be considered as

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potential competing interests:

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Table 1

Alpha diversity of sedimentary microbial community of five sampling sties. Observed species

Shannon

Simpson

Chao1

ACE

PD whole tree

S1

2357±137

8.496±0.396

0.985±0.012

2596±149

2642±145

177±6

S2

2099±412

8.597±0.232

0.991±0.002

2310±504

2325±519

162±24

S3

2266±499

8.363±0.818

0.988±0.009

2854±967

2728±731

169±29

S4

2069±258

7.677±0.943

0.967±0.026

2262±256

2282±246

158±16

S5

2486±243

8.815±0436

0.993±0.004

2740±238

2771±232

183±16

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Site

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Figure Captions Fig.1 Sampling site of this study.

Fig.2 Box plots indicating the concentrations of geochemical parameters for five sample sites (from S1 to S5) alongside with the waterflow. Fig.3 Bar chart presented the relative abundance of top 10 phylum in the sedimentary sites. Fig.4 Heatmap indicates the dynamic change of specific genus alongside with waterflow. Fig.5 (a) The molecular ecology network analysis (MENA) indicates the interactions among the top 1000

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OTUs. The most three connective nodes were colored using the taxonomic assignment at the phylum level.

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(b) The stress centrality and betweenness centrality of generated nodes. (c) Venn diagram reveals the

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potential keystone taxa using degree centrality, betweenness centrality, and stress centrality. Fig.6 Strong correlation of microorganisms and geochemical parameters (Pearson correlation index with the

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|R| > 0.7 and P < 0.05). Nodes are labeled with annotations of each OTU at the closest level (p_: phylum, c_:

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class, o_: order, f_: family, g_: genus).

Fig.7 Random forest prediction of the relationship between individual geochemical parameter and the

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microbial community diversity.

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observed species index. The bar graph shows the relative contribution of each geochemical parameter to the

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Fig.1 Sampling site of this study.

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Fig.2 Box plots indicating the concentrations of geochemical parameters for five sample sites (from S1 to S5) alongside with the waterflow.

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Fig.3 Bar chart presented the relative abundance of top 10 phylum in the sedimentary sites.

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Fig.4 Heatmap indicates the dynamic change of specific genus alongside with waterflow.

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Fig.5 (a) The molecular ecology network analysis (MENA) indicates the interactions among the top 1000

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OTUs. The most three connective nodes were colored using the taxonomic assignment at the phylum level.

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(b) The stress centrality and betweenness centrality of generated nodes. (c) Venn diagram reveals the

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potential keystone taxa using degree centrality, betweenness centrality, and stress centrality.

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Fig.6 Strong correlation of microorganisms and geochemical parameters (Pearson correlation index with the |R| > 0.7 and P-value < 0.05). Nodes are labeled with annotations of each OTU at the closest level (p_: phylum, c_: class, o_: order, f_: family, g_: genus).

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Fig.7 Random forest prediction of the relationship between individual geochemical parameter and the

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observed species index. The bar graph shows the relative contribution of each geochemical parameter to the microbial community diversity.

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Graphical Abstract

Highlights

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Most of Sb and As were not bioavailable in the sediments. Sb and As triggered different bacterial responses alongside with water flow. Sb exerted more impacts on microbiota than As.

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Putative Sb and As resistant microorganisms were identified.

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