Single molecule sequencing reveals response of manganese-oxidizing microbiome to different biofilter media in drinking water systems

Water Research 171 (2020) 115424

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Single molecule sequencing reveals response of manganese-oxidizing microbiome to different biofilter media in drinking water systems Xin Zhao, Bingfeng Liu, Xiuheng Wang, Chuan Chen, Nanqi Ren, Defeng Xing* State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 3 November 2019 Received in revised form 17 December 2019 Accepted 18 December 2019 Available online 19 December 2019

Rapid sand biofiltration (RSBF) is widely used for the removal of contaminants from drinking water treatment systems. Biofilm microbiomes in the biofilter media play essential roles in biotransformation of contaminants, but is not comprehensively understood. This study reports on Mn(II) oxidation and the core microbiomes in magnetite sand RSBF (MagSeRSBF) and manganese sand RSBF (MnSeRSBF). MnS eRSBF showed a relatively higher Mn(II) removal rate (40e91.2%) than MagSeRSBF during the startup. MagSeRSBF and MnSeRSBF had similar Mn(II) removal rates (94.13% and 99.16%) over stable operation for 80 days. Mn(II) removal rates at different depths in the MnSeRSBF reactor significantly changed with operation time, and the filter in the upper layer of MnSeRSBF made the largest contribution to Mn(II) oxidation once operation had stabilized. PacBio single molecule sequencing of full-length 16S rRNA gene indicated that biofilter medium had a significant impact on the core microbiomes of the biofilms from the two biofilters. The magnetite sand biofilter facilitated the enrichment of Mn(II)oxidizing biofilms. The dominant populations consisted of Pedomicrobium, Pseudomonas, and Hyphomicrobium in the RSBF, which have been affiliated with putative manganese-oxidizing bacteria (MnOB). The relative abundance of Pedomicrobium manganicum increased with operation time in both RSBF reactors. In addition, Nordella oligomobilis and Derxia gummosa were statistically correlated with Mn(II) oxidation. Species-species co-occurrence networks indicated that the microbiome of MnSeRSBF had more complex correlations than that of MagSeRSBF, implying that biofilter medium substantially shaped the microbial community in the RSBF. Hyphomicrobium and nitrite-oxidizing Nitrospira moscoviensis were positively correlated. The core microbiomes’ composition of both RSBF reactors converged over operation time. A hybrid biofilter medium with magnetite sand and manganese sand may therefore be best in rapid sand filtration for Mn(II) oxidation. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Single molecule sequencing Microbiome Manganese oxidation Biofiltration Network Drinking water

1. Introduction Rapid sand biofiltration (RSBF) is widely used around the world for treatment of drinking water from surface water and groundwater (Bouwer and Crowe, 1988; White et al., 2012; Gülay et al., 2016). Ammonia, iron, and soluble manganese (Mn(II)) contaminants in groundwater (Gülay et al., 2014) can be oxidized by microorganisms in RSBF (Tebo et al., 2004). Microbial ecology plays an essential role in managing processes in environmental biotechnology, including wastewater treatment, bioenergy generation, and

* Corresponding author. School of Environment, Harbin Institute of Technology, P.O. Box 2614, 73 Huanghe Road, Nangang District, Harbin, Heilongjiang Province, 150090, China. E-mail address: [email protected] (D. Xing). https://doi.org/10.1016/j.watres.2019.115424 0043-1354/© 2019 Elsevier Ltd. All rights reserved.

drinking water purification systems (Rittmann, 2006). Although some manganese-oxidizing bacteria (MnOB) have been isolated from drinking water systems (Cerrato et al., 2010), the processes by which these microbes work have not been well characterized due to the lack of understanding the physiological ecology and interspecies interactions of uncultured microorganisms. Groundwater chemistry can be a crucial factor in determining the microbial community structure of biofilms, as shown in a sand filtration system (Albers et al., 2015). Biofilter media, as the primary component of the biofiltration process, also affect the conversion of contaminants and influence biofilm formation and microbial community structure (Oh et al., 2018; Vignola et al., 2018). Different kinds of biofilter media, including anthracite, quartz sand, manganese sand, gravel, pumice stone, and granulated activated carbon (GAC), have been used in rapid sand filtration (RSBF) (Katsoyiannis and Zouboulis, 2004; Gülay et al., 2014; Hoyland

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et al., 2014; Yang et al., 2014; Albers et al., 2015; Cai et al., 2015; Bai et al., 2016). Double layer biofilters (pumice stone or GAC and quartz sand, anthracite and manganese sand) have been studied to improve Mn(II) removal (Lautenschlager et al., 2014; Cheng et al., 2017). Manganese sand has been used as biofilter medium in drinking water treatment plants in China (Qin et al., 2009; Li et al., 2013) given its relatively high adsorption ability (Qin et al., 2009; Li et al., 2016) and autocatalytic oxidation of Mn(II) in the presence of manganese oxides (Sahabi et al., 2009). A batch test showed that when MnOx(s) coated magnetic particles were used as adsorbents, removal of Mn(II) was enhanced (Funes et al., 2014). Conductive magnetite (Fe3O4) and magnetic fields, have been reported to facilitate the growth of iron-reducing bacteria capable of extracellular electron transfer (Kato et al., 2013; Zhou et al., 2017). Magnetite or its nanoparticle can mediate direct interspecies electron transfer (DIET) between electroactive bacteria and methanogens (Inaba et al., 2019). In addition, magnetite, as one of mixed-valent iron oxide minerals, plays an important role in microbial extracellular respiration, biogeochemical cycling of elements, trace-metal sequestration, and pollutant transformation (Peng et al., 2019). However, the effect of magnetic filter medium on Mn(II) removal and the enrichment of MnOB in RSBF has not been investigated. Next generation sequencing of 16S rRNA gene amplicons and metagenomic analyses have identified that the dominant microbes in sand biofilter populations were closely related to those capable of oxidizing ammonium, nitrite, iron, manganese, and methane (White et al., 2012; Gülay et al., 2016; Palomo et al., 2016). The core microbiomes were dominated by the lineages within the genera of Nitrosomonas and Nitrospira (Cao et al., 2015; Gülay et al., 2016). There may also be microbial interactions between MnOB and autotrophic nitrifying-bacteria as soluble microbial products (SMP), which are produced by nitrifying microbes, can support growth of heterotrophs such as manganese-oxidizing bacteria (MnOB) (Kindaichi et al., 2004; Cao et al., 2015). Community analyses showed that Leptothrix, Pseudomonas, Hyphomicrobium, and Pedomicrobium may play roles in the biological oxidation of Mn(II) in sand biofiltration (Li et al., 2013; Yang et al., 2014; Albers et al., 2015; Gülay et al., 2016). However, the relationship between the abundance of identified MnOB and Mn(II) oxidation in sand filtration is still unclear. Previous studies have speculated that uncultured populations in the sand biofilters might have the ability to oxidize Mn(II), but were not affiliated with identified MnOB (Cao et al., 2015; Nitzsche et al., 2015). These results suggested that there are unknown bacteria that may participate in Mn(II) oxidation. Moreover, there are even fewer studies that have focused on the effects of the biofilter media on the microbiome of biofilms. Next generation DNA sequencing technology has greatly increased our comprehension of the ecology of microorganisms, however, the short reads (<500 bp) provide limited information regarding species discrimination and taxonomic assignment. Third-generation sequencing technology, such as PacBio singlemolecule real-time (SMRT) DNA sequencing and Oxford Nanopore sequencing, are able to capture full-length 16S rRNA gene which allows high resolution taxonomic identification at the species-level. The aim of this study was to explore the effects of different biofilter media on manganese oxidation in RSBF and to identify the core contributors to Mn(II) oxidation in biofilms using Pacific Bioscience’s SMRT sequencing of 16S rRNA gene amplicons, and determine the interactions between biofilter medium (magnetite sand and manganese sand) and microbiomes of biofilms.

2. Materials and methods 2.1. Rapid sand biofiltration reactor and operation Magnetite sand (MagS) and manganese sand (MnS) were used as biofilter mediums in rapid sand biofiltration (RSBF) reactors, which were purchased from Hengxin Filter Plant (Gongyi, China) and the size of particles ranged from 0.5 to 2 cm. Two polymethyl methacrylate (PMMA) column bioreactors with an inner diameter of 60 cm and a height of 130 cm were constructed for the experiments. The two reactors had an active depth of 85 cm on top with 20 cm of coarse grain support material below, and were operated at an average hydraulic loading rate of 3 m/h. Because Mn(II) and Fe(II) often co-occur in metal-contaminated surface and groundwaters, the synthetic water containing 0.9e2.3 mg/L Mn(II) and 0.3 mg/L Fe(II) was used as the influent to the RSBF. The filters were operated in down flow mode and backwashed when the flow rate slowed down to less than 3 m/h due to choking by air bubbles. All RSBF reactors were operated at 25 ± 1
C room temperature. The influent pH, temperature, and dissolved oxygen were stably around 6.7, 15
C, and 9.3, respectively. To assess the contribution of biofilms at different filter depths to Mn(II) oxidation in the RSBF reactors, water samples collected along the depth were analyzed for Mn(II) oxidation at given intervals (40, 80, and 120 days). To investigate the effects of environmental factors (filter medium, filter depth, and operation time) on microbial community structure, for every 9 samples, samples of filter sands were collected from the upper (5 cm), middle (45 cm), and bottom (85 cm) layers of each reactor on days 40, 80, and 120. 2.2. DNA extraction Genomic DNA of biofilms on the different biofilter media were extracted using the FastDNA Spin Kit for Soil (MP Biomedicals, Solon, OH, USA) following the manufacturer’s instructions, and stored at 80
C for further analysis. The quantity and quality of extracted DNA were measured using a NanoDrop ND-8000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and agarose gel electrophoresis, respectively. 2.3. PCR amplification and single molecule sequencing of 16S rRNA gene amplicons PCR amplification of the nearly full-length bacterial 16S rRNA genes was performed using the forward primer 27F (50 -AGAGTTTGATCMTGGCTCAG-30 ) and the reverse primer 1492R (50 ACCTTGTTACGACTT-30 ). The total of PCR amplicons were purified with Agencourt AMPure XP Beads (Beckman Coulter, Indianapolis, IN) and quantified using the PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). After the individual quantification step, amplicons were pooled in equal amounts, and single molecule real time (SMRT) sequencing technology was performed using the Pacific Bioscience’s Sequel platform at Shanghai Personal Biotechnology Co., Ltd (Shanghai, China). 2.4. Bioinformatics and statistical analysis The sequencing data were analyzed using the Quantitative Insights Into Microbial Ecology (QIIME, v1.8.0) pipeline as previously described (Caporaso et al., 2010). The remaining high-quality sequences were clustered into operational taxonomic units (OTUs) at 97% sequence identity by UCLUST (Edgar, 2010). OTU taxonomic classification was conducted by BLAST, searching the representative sequences set against the NCBI 16S ribosomal RNA Database using the best hit with a confidence threshold of 90% (Altschul et al.,

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1997). Alpha diversity indices, Chao 1 richness estimator, abundancebased coverage estimator (ACE), and Shannon and Simpson indices, were calculated based on OTUs using QIIME. Beta diversity analysis was performed to investigate the structural variation of microbial communities across samples using UniFrac distance metrics (Lozupone and Knight, 2005; Lozupone et al., 2007) and visualized via principal coordinate analysis (PCoA) and arithmetic means (UPGMA) hierarchical clustering. A Venn diagram was generated to visualize the shared and unique OTUs among samples or groups using the R package “Venn Diagram”, based on the occurrence of OTUs across samples/groups regardless of their relative abundances (Zaura et al., 2009). Spearman correlation analysis was conducted to identify the relationships among the top 50 bacterial species, environmental parameters, and Mn(II) removal rate. Spearman’s correlation indices were calculated using the R software (Version 2.15.3). The differences of microbial communities in MagSeRSBF and MnSeRSBF reactors needed to be taken into account, so Spearman correlations of the top 50 bacterial species were calculated separately for each reactor. To elucidate microbial interactions in different biofilters, phylogenetic molecular ecological networks (pMENs) based on OTUs were constructed in a molecular ecological network analysis pipeline (MENA, http:// ieg4.rccc.ou.edu/mena) (Deng et al., 2012). The Gephi 0.9.2 and Cytoscape 3.6.1 were applied to generate the network between putative manganese-oxidizing bacteria (MnOB) and their correlated bacterial species (Shannon et al., 2003; Bastian et al., 2009). To estimate the potential functional contributions of the observed shifts in microbial diversity, functional profiles were predicted using a phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) from OTUs based on 16S rRNA gene sequences (Langille et al., 2013). The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Clusters of Orthologous Groups (COG) databases were employed to analyze metabolic pathways. The contributions of medium type, depth, and operational time to the variations of bacterial communities were assessed with variance partitioning analysis using the RDA R package (v3.2.0). All raw sequences were deposited in the NCBI Sequence Read Archive under accession number PRJNA541102. 3. Results

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Fig. 1. Mn(II) removal rate (A) and concentration of effluent (B) of MagSeRSBF and MnSeRSBF over time.

lower (P < 0.01) than in MagSeRSBF which had a mean concentration of 0.079 mg/L. The Mn(II) concentrations of the effluents of both RSBF reactors were much lower than drinking water standards (0.1 mg/L Mn). Depth profiles for the Mn(II) removal rate in MagSeRSBF at three time points had similar patterns, and Mn(II) removal rate increased linearly with increasing depth in the biofilter (Fig. 2). By contrast, Mn(II) removal rates at different depths in MnSeRSBF changed substantially over time. The removal rate in the top

3.1. Mn(II) oxidation in RSBF with different biofilter media Manganese sand RSBF (MnSeRSBF) had a higher Mn(II) removal rate of 40e91.2% than magnetite sand RSBF (MagSeRSBF) during the first 30 days (Fig. 1A). Mn(II) removal rate of MagSeRSBF increased during the period from 30 days to 70 days and exceeded those obtained by MnSeRSBF during the same period. It stabilized in both MnS and MagS RSBF reactors after operating for 80 days. The Mn(II) removal rates were 94.13% and 99.16% in MagSeRSBF and MnSeRSBF on 120th day, respectively. Mn(II) concentration of effluent treated with MnSeRSBF quickly decreased at the beginning of treatment, but increased within 80 days (Fig. 1B), suggesting that the highly effective physical adsorption and chemical oxidation of Mn(II) in MnSeRSBF took place during the first 30 days. By contrast, the Mn(II) concentration of effluent in MagSeRSBF significantly decreased to 0.08 mg/L after 32 days of operation. Then, the Mn(II) concentration of effluent of MagSeRSBF increased to higher than 0.43 mg/L from day 32e67, above 50% of Mn(II) was removed, which was still higher than that in MnSeRSBF (30.42e49.05%). Mn(II) concentration of effluent in both RSBF reactors decreased to 0.09 mg/L on day 80. Mn(II) concentration of effluent of MnSeRSBF was maintained under 0.021 mg/L after 80 days, which was significantly

Fig. 2. Depth profiles of Mn(II) removal in MagSeRSBF and MnSeRSBF during different periods of operation (40 d, 80 d, and 120 d).

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25 cm of the biofilter progressively increased from 12.2% (on day 40) and 45.65% (on day 80) to 93.04% (on day 120), indicating that the upper layer of the biofilter in MnSeRSBF made the largest contribution to Mn(II) oxidation. 3.2. Community diversity of microbial biofilms on different biofilter media PacBio Sequel sequencing showed that 6058 to 11,350 highquality effective sequences with full-length 16S rRNA gene for each sample were obtained, and 354 to 573 operational taxonomic units (OTUs) were determined at 97% identity (Fig. S1). Rarefaction curves indicated that sequencing depth was sufficient to capture most of the predominant bacterial populations in all samples (Fig. S1). The Chao 1, ACE estimator, Simpson index, and Shannon index increased after day 40, indicating that microbial communities of biofilms in MagSeRSBF and MnSeRSBF were shaped over time (Table S1). The Chao 1 (435.48e534.32) and Shannon indices (6.96e7.57) of MnSeRSBF were relatively higher than those of MagSeRSBF (Chao1 of 354e444.68 and Shannon of 6.64e6.95) on day 40. The diversity of microbial communities in MnSeRSBF were slightly higher than in MagSeRSBF. The MagSeRSBF and MnSeRSBF reactors shared 433 OTUs on day 40, accounting for 57.3% and 56.5% of total OTUs (Fig. S2A), respectively. The number of shared OTUs in both RSBF reactors increased to 651 on day 80 and 658 on day 120 (Figs. S2B and S2C). Furthermore, the biofilms in the upper layer of biofilters shared the largest number of OTUs (Fig. S2D), making up 63.6% and 63.3% of the total OTUs in the MagSeRSBF and MnSeRSBF reactors, respectively. Principal coordinate analysis (PCoA) based on weighted UniFrac distance showed a clear separation between magnetite sand and manganese sand biofilters (Fig. 3A), indicating that there were differences in the microbiomes of the biofilms from the MagSeRSBF and MnSeRSBF reactors. The differences between the two microbial communities in the two reactors reduced gradually from day 40 to day 120 (the samples from the two filters were clustered). Hierarchical clustering analysis revealed that microbial communities in the upper layer of MnS and MagS reactors on day 80 and day 120 were clustered closely (Fig. 3B). Otherwise, the bacterial communities in the middle and bottom layers of each reactor were more similar. 3.3. Core microbiome in the biofilms of RSBF with different biofilter media Proteobacteria was the most dominant phylum in all samples, accounting for 67.94%e91.79%, followed by Actinobacteria (1.87e8.72%), Acidobacteria (1.17e7.86%), Planctomycetes (0.63e14.44%), Bacteroidetes (0.66e6.93%), and Gemmatimonadetes (0.34e4.78%) (Figs. S3A and S3B). The relative abundance of Proteobacteria in all samples reach to around 70% on day 120. The relative abundance of Planctomycetes (14.44%) was highest in biofilms of the bottom layer of MagSeRSBF on day 40 (Fig. S3A), and it was much higher than that of MnSeRSBF (2.32%) (Fig. S3B). At the class level, Alphaproteobacteria (33.35e41.46%) dominated in biofilms on the upper layers of both RSBF reactors (Figs. S3C and S3D). The relative abundance of Betaproteobacteria (26.99e39.12%) was higher in the MnSeRSBF reactor, while Gammaproteobacteria (8.43e33.19%) was the predominant class in the MagSeRSBF reactor. Additionally, the relative abundance of Gammaproteobacteria in biofilms in the upper layers of both reactors decreased from 29.56% to 15.25% on day 40 to 8.95% and 4.26% on day 120, respectively. Hierarchical cluster analysis and heatmap based on predominant genera illustrated the distinct clusters between different

Fig. 3. Principal coordinate analysis (PCoA) (A) and hierarchical cluster analysis (B) based on weighted UniFrac distances of the biofilm microbiomes in different layers of MagSeRSBF and MnSeRSBF during different operation periods.

samples from the RSBF, indicating that temporal and spatial dynamics existed in the microbiomes of RSBF with different filter media (Fig. 4). The core microbiomes in the RSBF reactors with different biofilter media were more distinct by day 40. However, the core microbiome of MagSeRSBF on day 120 and MnSeRSBF on day 80 and 120 were clustered together, which was also true for Mn(II) oxidation. The predominant populations from the middle and bottom layers in MnSeRSBF were located in a branch on day 40, 80, and 120, while the predominant populations in the upper layer in both RSBF reactors on day 120 were clustered together. The succession of predominant populations took place over spatial and temporal scales. Predominant populations, including Pseudomonas, Hyphomicrobium, Pedomicrobium, and Leptothrix were affiliated with putative Mn(II)-oxidizing bacteria (Fig. 4). The relative abundance of Pseudomonas was relatively high in both reactors on day 40 (6.49e19.73%), and decreased to 1.64e7.09% by day 120. In contrast, the relative abundance of Hyphomicrobium increased from 0.69-9.44% to 8.22e13.62% on day 40 and day 120, respectively. The relative abundance of Hyphomicrobium was higher in MnSeRSBF

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Fig. 4. Hierarchical cluster analysis of dominant populations in RSBF. The genera with relative abundances in the top 50 are shown. The genera clustering tree is on the left and the sample clustering tree is on the top. Each box of the heatmap represents a Z-score, a positive score indicates a datum above the mean, while a negative score indicates a datum below the mean.

(3.85e13.62%) than that in MagSeRSBF (0.69e9.56%). The maximum abundance of Leptothrix occurred in the biofilms of the upper layer of MagSeRSBF (4.48%) and MnSeRSBF (1.93%). In addition, the relative abundances of Oxalicibacterium, Gemmatinonas, and Gaiella in both RSBF reactors increased on day 120. 3.4. Predominant species in the biofilms of RSBF Pedomicrobium manganicum capable of Mn(II) oxidation was enriched in the upper layer, it’s abundance increased from 0.5% to 2.2% in the MagSeRSBF reactor from day 40 to day 120, while it increased from 0.9% to 1.5% in the MnSeRSBF reactor (Fig. 5). Hyphomicrobium nitrativorans and Hyphomicrobium zavarzinii in the MnSeRSBF reactor increased significantly from 0.95 to 1.09% and 0.83e2.76% to 3.03e3.79% and 3.92e5.28%, respectively, over time. Similar trends of increase were also found in the MagSeRSBF reactor. The relative abundance of Hyphomicrobium facile subsp. ureaphilum increased in the MagSeRSBF reactor (from 0.28-2.49% to 2.88e4.32%), but decreased in the MnSeRSBF reactor (from 0.83-4.5% to 0.85e2.73%) with operational time. The relative abundances of both species in the MnSeRSBF reactor

(0.83e5.28%) was consistently higher than those in the MagSeRSBF reactor (0.12e2.96%) during operation. The relative abundances of Pseudomonas veronii, Pseudomonas multiresinivorans, and Pseudomonas citronellolis varied with RSBF depth and operation time. The relative abundance of Pseudomonas veronii in the MagSeRSBF reactor (2.04e17.09%) was higher than that in the MnSeRSBF reactor (0.97e2.57%) and slightly increased with depth over time, except in the upper layer of the MagSeRSBF reactor. Furthermore, the Mn(II) removal rate of MagSeRSBF (98.99%) was higher than that of MnSeRSBF reactor (44.98%) on day 40, suggesting that P. veronii may have the capacity to oxidize Mn(II). The relative abundances of P. multiresinivorans (0.41e3.82%) and P. citronellolis (0.49e4.44%) in both RSBF reactors were substantially decreased from 1.07 to 3.82% and 1.27e4.44% on day 40 to 0e0.96% and 0.04e1.12% on day 120, respectively. In addition, Acidiferrobacter thiooxydans and Sulfurifustis variabilis were unique and predominant species in MagSeRSBF, their abundances ranged from 0.47% to 10.22% and 0.32%e5.3%, respectively. Noviherbaspirillum massiliense (4.11e7.42%), Massilia timonae (0.89e5.63%), and Noviherbaspirillum canariense (0.4e4.83%)

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Fig. 5. Relative abundances of dominant species in MagSeRSBF and MnSeRSBF.

showed higher relative abundances in the MnSeRSBF reactor compared to the MagSeRSBF reactor. The increases of relative abundances of Denitratisoma oestradiolicum, Oxalicibacterium solurbis, and Gaiella occulta were observed over time in both RSBF reactors. 3.5. Correlation between bacterial species and abiotic factors The majority of bacterial species’ abundances were related to biofilter medium type and operation time (Fig. 6). Correlation analysis indicated that Hyphomicrobium zavarzinii, Hyphomicrobium nitrativorans, Noviherbaspirillum massiliense, and Massilia timonae had positive relation with the MnS biofilter, while Acidiferrobacter thiooxydans and Sulfurifustis variabilis positively related with the MagS biofilter (P < 0.01). Rhodopseudomonas pseudopalustris and Hyphomicrobium facile subsp. ureaphilum were positively correlated with biofilter depth. Pseudomonas citronellolis and Pseudomonas multiresinivorans, on the other hand, were negatively correlated with operational time while Denitratisoma oestradiolicum and Hyphomicrobium zavarzinii were positively correlated with operation time. Nordella oligomobilis and Derxia gummosa had positive correlations with Mn(II) oxidation, while Leptothrix mobilis was negatively correlated with Mn(II) oxidation (Fig. 6). These results were confirmed by variance partitioning analysis (VPA) (Fig. S4), which concluded that biofilter medium type and operation time explained 33% and 25%, respectively, of microbial community composition. When Spearman correlations were separately analyzed in the two reactors, Derxia gummosa was the sole species that had a positive correlation with Mn(II) oxidation in both RSBF reactors. Taken separately, Thiobacillus thiophilus and Nitrosospira multiformis were positively correlated with Mn(II) oxidation in MagSeRSBF, and Clostridium cylindrosporum and Sideroxydans lithotrophicus had positive correlations with Mn(II) oxidation in MnSeRSBF. 3.6. Species-species co-occurrence network of the biofilms in RSBF Network analysis based on OTUs showed that biofilter medium induced different co-occurrence patterns in microbial

communities. The two empirical networks were significantly different from random networks (P < 0.05), indicating that bacterial community assembly assemblages in the RSBF reactors was nonrandom (Fig. 7). The species-species co-occurrence network of biofilms in MnSeRSBF was more complex than that in MagSeRSBF, as was indicated by a higher average connectivity (avgK) (8.245 vs. 4.355), shorter average path (GD) (3.049 vs. 3.767), and smaller modularity (0.595 vs. 0.763) (Table S2). The network in the MnSeRSBF reactor consisted of 319 nodes and 1315 edges, both of which were higher than in the MagSeRSBF reactor (259 nodes and 564 edges) (Fig. 7). The proportion of the network connections that were negative was 40.6% in the MnSeRSBF reactor, while it was 12.9% in the MagSeRSBF reactor network. The majority of OTUs in the network structure of the MagSeRSBF reactor belonged to Hyphomicrobium, Pedomicrobium, and Pseudomonas, and had positive relationships (Fig. 7A). Two clear subclusters in the network of MnSeRSBF showed complicated correlations of the putative manganeseoxidizing Hyphomicrobium spp. and Pseudomonas spp. with other species (Fig. 7B). Hyphomicrobium spp. had complex correlations with other species in the microbiome of both RSBF reactors, while Pseudomonas spp. had more correlations in MnSe RSBF than that in MagSeRSBF (Fig. 7C and D). Pseudomonas spp. had more positive correlations with other species in the MnSeRSBF network than Hyphomicrobium spp. Nordella oligomobilis was positively correlated with Pseudomonas veronii, Hyphomicrobium facile subsp. ureaphilum, and Pedomicrobium manganicum in MagSeRSBF, while in MnSeRSBF, it had positive correlations with Hyphomicrobium facile subsp. ureaphilum, Hyphomicrobium zavarzinii, Pseudomonas multiresinivorans, and Rhodopseudomonas pseudopalustris. Denitratisoma oestradiolicum and Derxia gummosa were positively correlated in both RSBF reactors. Additionally, Nitrosospira multiformis was also positively correlated with Derxia gummosa in MagSeRSBF, which was found to be positively related to Mn(II) oxidation in the MagSeRSBF reactor (Fig. S5). Moreover, Nitrospira moscoviensis was positively correlated with Hyphomicrobium spp. (Hyphomicrobium zararzinii and Hyphomicrobium nitrativorans) in both RSBF reactors.

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Fig. 6. Correlation between the top 50 microbial species and environmental variables (filter medium, filter depth, operation time, and Mn(II) removal rate) based on Spearman analysis. Colors represent positive (red) or negative (blue) correlations. The color depth indicates the degree of positive or negative correlation. Statistical significance levels: *P < 0.05, **P < 0.01. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

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Fig. 7. Correlation based network analysis from MagSeRSBF (A, C) and MnSeRSBF (B, D). The network reveals positive (Spearman correlation coefficient > 0.6, P < 0.05) and negative (Spearman correlation coefficient <  0.6, P < 0.05) correlations. Node sizes are proportional to the average relative abundance of that taxa in all samples (A and B). The putative MnOB and significant microbes were labeled in black. Edges were the correlations between bacteria. Red lines represent positive correlations and green lines negative correlations. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

4. Discussion 4.1. The effects of biofilter media on Mn(II) oxidation in RSBF There were differences in Mn(II) oxidation between MnSeRSBF and MagSeRSBF during different operation periods. Mn(II) oxidation of MnSeRSBF was higher than that of MagSeRSBF during first 30 days (Fig. 1A), suggesting that manganese sand (MagS) has a higher adsorption capability of Mn(II), which was consistent with a previous study (Qin et al., 2009). Magnetite sand’s (MagS) weaker adsorption ability of Mn(II) during the first 30 days was similar to anthracite (Greenstein et al., 2018) and quartz sand (Qin et al., 2009). Interestingly, a sharp increase of Mn(II) oxidation in MagSeRSBF was observed on day 30 (Fig. 1), presumably because MagS facilitated the enrichment of Mn(II)-oxidizing bacteria better than MnS. MnS showed relatively slower acclimation of MnOB, presumably the surface characterization or natural oxides impacted the cell attachment. Mn(II) oxidation by MnSeRSBF (99.16%) was slightly higher than that by MagSeRSBF (94.13%) after operation for 80 days, presumably because manganese was further oxidized by manganese oxides derived from MnS (Fig. S6). X-ray photoelectron spectrometer (XPS) analysis showed that biogenic manganese oxides of both RSBF were the 3e4 valence manganese (Zhao et al., 2018). Manganese oxides, accumulated on the surface of the biofilter medium (Fig. S6), lead to autocatalytic oxidation of

Mn(II) (Bargar et al., 2005; Bruins et al., 2014). XPS analysis also indicated that MnS was more suited to the retention of manganese oxides (Fig. S6B), which might explain why the Mn(II)oxidation rate increased on top layer of the MnSeRSBF reactor over time (Fig. 2). The manganese sand biofilter exhibited better Mn(II) oxidation later in operation than at the beginning, the magnetite manganese sand biofilter, on the other hand, had higher Mn(II) oxidation initially, before operation stabilized. Therefore, a mixture of equal amounts of biofilter medium or double layers of MnS/MagS, may be beneficial for Mn(II) oxidation in drinking water treatment. Hybrid biofilter mediums should be further investigated in the future. 4.2. The effects of biofilter medium on microbial community structure PCoA and hierarchical clustering results showed that biofilter medium significantly affected the succession of microbial communities in RSBF (Fig. 3), an area of study which was neglected in previous studies on sand filtration (Qin et al., 2009; Wu et al., 2011). In this study, the microbial diversity increased and the community structure shifted towards being more similar as the RSBF reactors reached stable operation. Especially, the convergence of microbiomes in the upper layers of both RSBF implied that a consistent correlation might occur between the selection pressure of Mn(II) and the succession of manganese-oxidizing bacteria (Fig. 3).

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Operation time was found to be the second most important factor, explaining 25% of the communities’ structures. Moreover, mineral coating and metal oxides aggregated on the surface of biofilter material along with other cationic species positively affected the activity and structures of microbiota (Gülay et al., 2014). The increase of biogenic manganese oxides might also have played a role in the shaping the microbial communities. Microbiomes in the middle layer of MnSeRSBF and the bottom layer of MagSeRSBF still diverged from that in other layers of corresponding RSBF. The divergence and the convergence of microbial communities co-occurred at the different layers indicated that community assembly in RSBF was more complicated and depended upon different environmental conditions. In addition, the effect of filter medium on community assembly should be further studied under different physicochemical conditions such as pH, redox conditions, temperature, and humic acid. Magnetite sand (MagSeRSBF) facilitated the enrichment of Pseudomonas veronii, while Hyphomicrobium spp. dominated in MnSeRSBF. For example, the relative abundances of Hyphomicrobium zavarzinii and Hyphomicrobium nitrativorans were higher in the MnSeRSBF reactor. In addition, two representative 16S rRNA sequences which were affiliated with Acidiferrobacter thiooxydans (an acidophilic iron- and sulfur-oxidizing bacterium) (Hallberg et al., 2011), dominated in the MagSeRSBF reactor. However, Mn(II) oxidation by A. thiooxydans is not fully understood and should be further investigated. Mn(II) and biofilter medium as primary selection pressures influenced community assembly and microbiome compositions. The differences in the microbiome compositions of RSBF with different filter media implicated that the microbiome in RSBF could be manipulated by selecting or optimizing environmental variables. When keystone species and its metabolic networks are identified in RSBF for manganese removal, it will shed light on the design of reactor microbiome or synthetic community to enhance the efficiency of RSBF.

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contribute to Mn oxidation. Phylogenetic analyses based on 16S rRNA sequences showed 25 OTU sequences were closely related to manganese-oxidizing bacteria (MnOB) (Fig. S7). These OTUs formed two main clusters. One cluster was affiliated with Pedomicrobium sp. ACM 3067 (Larsen et al., 1999), while the other was familiar with Pseudomonas sp. PCP2 (Francis and Tebo, 2001). Both OTU 3755 and OTU 2672 were close to Pseudomonas putida MnB1 (Caspi et al., 1998) and OTU 2672 was familiar with Pseudomonas sp. GB1 (Brouwers et al., 1999). Overall this showed that diverse and uncultivated MnOB were abundant in RSBF. Until now, the ability of Mn(II) oxidation by various Pseudomonas spp. is not fully understood. For instance, it was known that P. putida GB-1, P. putida MnB1, and several other P. putida strains (ATCC12633 and ATCC 33015) were capable of oxidizing Mn(II), while other Pseudomonas species (i.e., P. fluorescens ATCC 13525, P. stutzeri JM300) could not oxidize Mn(II) (Francis and Tebo, 2001). P. putida GB-1 was identified to not only possess two kinds of multicopper oxidase (MnxG and McoA), but also an animal heme peroxidase (MopA), indicating the two pathways for regulating the oxidation of Mn(II) (Geszvain et al., 2016). Additionally, some of species, like P. aeruginosa PAO1 could perform denitrification under anaerobic condition (Schreiber et al., 2007), suggesting a possible role on the process of manganese and nitrogen cycle. The Mn(II) oxidation capacity of P. veronii, P. citronellolis, and P. multiresinivorans, which were abundant in both biofilters, is still unclear. Determination of Mn(II) oxidation capability by different species within the genera Hyphomicrobium and Pedomicrobium still needs direct evidence based on pure culture tests. Furthermore, previous studies have reported that no MnOB, or only one clone affiliated with Bacillus subtilis, were identified in biofilters for Mn(II) removal (Cai et al., 2015; Cao et al., 2015; Nitzsche et al., 2015). Based on those results, it is difficult to conclude that the abundances of those known MnOB alone can explain the Mn(II) removal. Mn(II) oxidation by putative MnOB in the microbiomes needs further investigation through multi-omics approaches and pure culture analyses.

4.3. Putative manganese-oxidizing bacteria (MnOB) Previous investigations have speculated that Hyphomicrobium and Pedomicrobium are affiliated with manganese-oxidizing bacteria in sand filtration systems (Li et al., 2013; Albers et al., 2015; Matsushita et al., 2018), and that Pseudomonas and Leptothrix play important roles in drinking water treatment systems (Burger et al., 2008; Matsushita et al., 2018). To date, previous studies have not provided direct evidence to support a positive correlation between well-known MnOB and Mn(II) oxidation, which might due to limitations in detection methods at the species-level resolution, and the lack of direct functional validation based on Mn(II) oxidationrelated genes. The identification of uncultivated MnOB are yet more challenging, because the mechanisms by which MnOB oxidize Mn(II) are diverse. Multicopper oxidase (MCO) and hemecontaining manganese peroxidases (MnPs) derived from different species have different capabilities to directly oxidize Mn(II) (Francis and Tebo, 2001; Zhao et al., 2018), it is difficult to identify unknown MnOB based on orthologous genes that encode Mn-oxidizing proteins using metagenomic and metatranscriptomic approaches. Indirect oxidation by uncultivated MnOB through modification of redox environments (pH increase, Eh, free radicals, and reactive oxygen species) is also difficult to identify without pure culture tests. In this study, Pedomicrobium manganicum was identified as a MnOB based on the sequencing of full length of 16S rRNA gene. No correlation was observed between Pedomicrobium manganicum and Mn(II) oxidation based on the change of relative abundances (Fig. 6), but this does not mean Pedomicrobium manganicum did not

4.4. Microbial interactions in the two biofilters Nitrospira moscoviensis, capable of nitrite oxidation, had a positive relationship with Hyphomicrobium spp. (Fig. 7). Nitrospira was enriched in oligotrophic waters such as drinking water treatment plants and distribution networks (Regan et al., 2002; Pinto et al., 2012; Palomo et al., 2016; Vignola et al., 2018). The processes of the nitrogen cycle have a close connection with Mn(II) oxidation. Correlation analysis also revealed the ammonia-oxidizing bacteria Nitrosospira multiformis was positively correlated with Mn(II) oxidation in MagSeRSBF. Previous studies have implied that MnOB could not prevail in autotrophic conditions (Cao et al., 2015). MnOB growth in oligotrophic water depends on soluble microbial products (SMPs) that are produced by nitrifiers (AOB and NOB) or methane-oxidizing bacteria (MOB) (Cao et al., 2015; Matsushita et al., 2018). SMPs are usually classified into two distinct groups, utilization-associated products (UAPs) and biomass-associated products (BAPs) (Barker and Stuckey, 1999; Ni et al., 2011). However, the contribution of SMPs to different sources should be further analyzed. The greater complexity of the network in the MnS reactor (Table S2) might be one of the key contributors to the stable Mn(II) removal after 80 days, because the diversity of interaction types seemed effective at stabilizing the microbial communities (Mougi and Kondoh, 2012). More research on temporal factors in interspecific relationships in RSBF must be conducted to validate the possibility.

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4.5. Multiple pathways of Mn(II) oxidation

microorganisms.

PICRUSt showed that the relative abundance of the multicopper oxidase (MCO) gene was higher than that of the peroxidase gene (Fig. S8). The gene abundance was not consistent with Mn(II) oxidation, suggesting that Mn(II) oxidation relied on the expression of Mn(II) oxidation-related genes and other pathways not revealed by gene abundances. The reason was the limited information from the COG and KEGG databases, where a large portion of the microbial taxa in this study could not be found. Metagenomic analysis found that a member affiliated with Burkholderiales in the sand biofilter contained multicopper oxidase (Palomo et al., 2016). NCBI blast results found that a large number of the abundant populations harbored cotA, which encodes spore coat protein with the activities of MCO and laccase. Therefore, apart from model strains of MnOB, there might be other species that processed MCO for Mn(II) oxidation in this study. CotA from Bacillus pumilus WH4 exhibited manganese-oxidase activity (Su et al., 2013), but CotA from Bacillus subtilis was unable to oxidize Mn(II) (Hullo et al., 2001). However, whether CotA possessed the capability of Mn(II) oxidation was uncertain and metagenomics-derived protein data should be further analyzed to predict more accurate mechanism of Mn(II) oxidation in biofiltration systems. Moreover, microbial function and metabolic pathway the microbial community possess will be further investigated by metagenomics. It has been demonstrated that Mn(II) oxidation carried out by Arthrobacter, whose Mn(II)-oxidizing potential was inactive in monoculture, was activated in response to the presence of Sphingopyxis (Liang et al., 2017). Arthrobacter spp. and Sphingopyxis spp. were both enriched in the biofilms of the RSBF, opening the possibility that cooperative Mn(II) oxidation might have occurred in the RSBF. A positive correlation between Denitratisoma oestradiolicum and Derxia gummosa was identified in both RSBF reactors, suggesting that they may have a similar cooperative mechanism for Mn(II) oxidation. Additionally, Stenotrophomonas and Lysinibacillus might indirectly oxidize Mn(II) via a nonenzymatic pathway (Barboza et al., 2015). These results suggested that multiple pathways of Mn(II) oxidation may work in concert in RSBF.

Declaration of competing interest

5. Conclusions This study reports on Mn(II) oxidation and the dynamics of the core microbiomes of biofilms in rapid sand biofiltration (RSBF) systems with two different types of filter media for drinking water treatment. Biofilter medium, which has previously been overlooked, had a significant effect on Mn(II) oxidation and biofilm microbiome during the start-up and operation periods. Manganese sand RSBF (MnSeRSBF) facilitated the faster initial operation compared to magnetite sand RSBF (MagSeRSBF). Mn(II) oxidation profile along depth was significantly different in the two RSBF reactors. The Mn(II) removal rate at different depths of the MagSeRSBF reactor changed little, while Mn(II) removal shifted towards the upper layer of MnSeRSBF over time. The initial microbiomes were shaped by biofilter media but converged over operational time. Various genera affiliated to putative manganeseoxidizing bacteria were identified by PacBio single molecule sequencing. However, Pedomicrobium manganicum was the only species known to be a MnOB. Nordella oligomobilis and Derxia gummosa were statistically correlated with Mn(II) oxidation and several dominant species of Hyphomicrobium, Pseudomonas, and Denitratisoma. The species-species co-occurrence network of MnSeRSBF was more complex than MagSeRSBF. Therefore, the MagSeRSBF was better able to form a microbiome conducive to Mn(II) oxidation via facilitating a higher proportion of positive biological interactions among the putative keystone

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. Acknowledgement This study was supported by the National Natural Science Foundation of China (No. 31870114), the State Key Laboratory of Urban Water Resources and Environment (Harbin Institute of Technology) (No. 2019DX02). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.watres.2019.115424. References Albers, C.N., Ellegaard-Jensen, L., Harder, C.B., Rosendahl, S., Knudsen, B.E., Ekelund, F., Aamand, J., 2015. Groundwater chemistry determines the prokaryotic community structure of waterworks sand filters. Environ. Sci. Technol. 49 (2), 839e846. Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J., 1997. Gapped blast and psi-blast: a new generation of protein database search programs. Nucleic Acids Res. 25 (17), 3389e3402. Bai, Y., Chang, Y., Liang, J., Chen, C., Qu, J., 2016. Treatment of groundwater containing Mn(II), Fe(II), As(III) and Sb(III) by bioaugmented quartz-sand filters. Water Res. 106, 126e134. Barboza, N.R., Amorim, S.S., Santos, P.A., Reis, F.D., Cordeiro, M.M., Guerra-S
a, R., Le~ ao, V.A., 2015. Indirect manganese removal by Stenotrophomonas sp. and Lysinibacillus sp. isolated from Brazilian mine water. BioMed Res. Int. 2015, 925972. Bargar, J., Tebo, B., Bergmann, U., Webb, S., Glatzel, P., Chiu, V.Q., Villalobos, M., 2005. Biotic and abiotic products of Mn(II) oxidation by spores of the marine Bacillus sp. strain SG-1. Am. Mineral. 99 (1), 143e154. Barker, D.J., Stuckey, D.C., 1999. A review of soluble microbial products (SMP) in wastewater treatment systems. Water Res. 33 (14), 3063e3082. Bastian, M., Heymann, S., Jacomy, M., 2009. Gephi: an open source software for exploring and manipulating networks. Proc. Third Int. ICWSM Conf. 361e362. Bouwer, E.J., Crowe, P.B., 1988. Biological processes in drinking water treatment. J. Am. Water Work. Assoc. 80 (9), 82e93. Brouwers, G.J., de Vrind, J.P., Corstjens, P.L., Cornelis, P., Baysse, C., de Vrind-de Jong, E.W., 1999. CumA, a gene encoding a multicopper oxidase, is involved in Mn2þ oxidation in Pseudomonas putida GB-1. Appl. Environ. Microbiol. 65 (4), 1762e1768. Bruins, J.H., Petrusevski, B., Slokar, Y.M., Kruithof, J.C., Kennedy, M.D., 2014. Manganese removal from groundwater: characterization of filter media coating. Desalin. Water Treat. 55 (7), 1851e1863. Burger, M.S., Mercer, S.S., Shupe, G.D., Gagnon, G.A., 2008. Manganese removal during bench-scale biofiltration. Water Res. 42 (19), 4733e4742. Cai, Y., Li, D., Liang, Y., Luo, Y., Zeng, H., Zhang, J., 2015. Effective start-up biofiltration method for Fe, Mn, and ammonia removal and bacterial community analysis. Bioresour. Technol. 176, 149e155. Cao, L.T.T., Kodera, H., Abe, K., Imachi, H., Aoi, Y., Kindaichi, T., Ozaki, N., Ohashi, A., 2015. Biological oxidation of Mn(II) coupled with nitrification for removal and recovery of minor metals by downflow hanging sponge reactor. Water Res. 68, 545e553. Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., Fierer, N., Pena, A.G., Goodrich, J.K., Gordon, J.I., Huttley, G.A., Kelley, S.T., 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 (5), 335e336. Caspi, R., Tebo, B.M., Haygood, M.G., 1998. C-type cytochromes and manganese oxidation in Pseudomonas putida MnB1. Appl. Environ. Microbiol. 64 (10), 3549e3555. Cerrato, J.M., Falkinham 3rd, J.O., Dietrich, A.M., Knocke, W.R., McKinney, C.W., Pruden, A., 2010. Manganese-oxidizing and -reducing microorganisms isolated from biofilms in chlorinated drinking water systems. Water Res. 44 (13), 3935e3945. Cheng, Q., Nengzi, L., Bao, L., Huang, Y., Liu, S., Cheng, X., Li, B., Zhang, J., 2017. Distribution and genetic diversity of microbial populations in the pilot-scale biofilter for simultaneous removal of ammonia, iron and manganese from real groundwater. Chemosphere 182, 450e457.

X. Zhao et al. / Water Research 171 (2020) 115424 Deng, Y., Jiang, Y.-H., Yang, Y., He, Z., Luo, F., Zhou, J., 2012. Molecular ecological network analyses. BMC Bioinf. 13 (1), 113. Edgar, R.C., 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26 (19), 2460e2461. Francis, C.A., Tebo, B.M., 2001. CumA multicopper oxidase genes from diverse Mn(II)-oxidizing and non-Mn(II)-oxidizing Pseudomonas strains. Appl. Environ. Microbiol. 67 (9), 4272e4278. Funes, A., de Vicente, J., Cruz-Pizarro, L., de Vicente, I., 2014. The influence of pH on manganese removal by magnetic microparticles in solution. Water Res. 53, 110e122. Geszvain, K., Smesrud, L., Tebo, B.M., 2016. Identification of a third Mn(II) oxidase enzyme in Pseudomonas putida GB-1. Appl. Environ. Microbiol. 82 (13), 3774e3782. Greenstein, K.E., Lew, J., Dickenson, E.R.V., Wert, E.C., 2018. Investigation of biotransformation, sorption, and desorption of multiple chemical contaminants in pilot-scale drinking water biofilters. Chemosphere 200, 248e256. Gülay, A., Musovic, S., Albrechtsen, H.-J., Al-Soud, W.A., Sørensen, S.J., Smets, B.F., 2016. Ecological patterns, diversity and core taxa of microbial communities in groundwater-fed rapid gravity filters. ISME J. 10, 2209. Gülay, A., Tatari, K., Musovic, S., Mateiu, R.V., Albrechtsen, H.-J., Smets, B.F., 2014. Internal porosity of mineral coating supports microbial activity in rapid sand filters for groundwater treatment. Appl. Environ. Microbiol. 80 (22), 7010e7020. Hallberg, K.B., Hedrich, S., Johnson, D.B., 2011. Acidiferrobacter thiooxydans, gen. nov. sp. nov.; an acidophilic, thermo-tolerant, facultatively anaerobic iron- and sulfur-oxidizer of the family Ectothiorhodospiraceae. Extremophiles 15 (2), 271e279. Hoyland, V.W., Knocke, W.R., Falkinham III, J.O., Pruden, A., Singh, G., 2014. Effect of drinking water treatment process parameters on biological removal of manganese from surface water. Water Res. 66, 31e39. Hullo, M.F., Moszer, I., Danchin, A., Martin-Verstraete, I., 2001. CotA of Bacillus subtilis is a copper-dependent laccase. J. Bacteriol. 183 (18), 5426e5430. Inaba, R., Nagoya, M., Kouzuma, A., Watanabe, K., 2019. Metatranscriptomic evidence for magnetite nanoparticle-stimulated acetoclastic methanogenesis under continuous agitation. Appl. Environ. Microbiol. 85 (23) https://doi.org/ 10.1128/AEM.01733-19. Kato, S., Hashimoto, K., Watanabe, K., 2013. Iron-oxide minerals affect extracellular electron-transfer paths of Geobacter spp. Microb. Environ. 28 (1), 141e148. Katsoyiannis, I.A., Zouboulis, A.I., 2004. Biological treatment of Mn(II) and Fe(II) containing groundwater: kinetic considerations and product characterization. Water Res. 38 (7), 1922e1932. Kindaichi, T., Ito, T., Okabe, S., 2004. Ecophysiological interaction between nitrifying bacteria and heterotrophic bacteria in autotrophic nitrifying biofilms as determined by microautoradiography-fluorescence in situ hybridization. Appl. Environ. Microbiol. 70 (3), 1641e1650. Langille, M.G., Zaneveld, J., Caporaso, J.G., McDonald, D., Knights, D., Reyes, J.A., Clemente, J.C., Burkepile, D.E., Vega Thurber, R.L., Knight, R., Beiko, R.G., Huttenhower, C., 2013. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31 (9), 814e821. Larsen, I.E., Sly, I.L., McEwan, G.A., 1999. Manganese(II) adsorption and oxidation by whole cells and a membrane fraction of Pedomicrobium sp. ACM 3067. Arch. Microbiol. 171 (4), 257e264. Lautenschlager, K., Hwang, C., Ling, F., Liu, W.T., Boon, N., Koster, O., Egli, T., Hammes, F., 2014. Abundance and composition of indigenous bacterial communities in a multi-step biofiltration-based drinking water treatment plant. Water Res. 62, 40e52. Li, C., Wang, S., Du, X., Cheng, X., Fu, M., Hou, N., Li, D., 2016. Immobilization of ironand manganese-oxidizing bacteria with a biofilm-forming bacterium for the effective removal of iron and manganese from groundwater. Bioresour. Technol. 220, 76e84. Li, X.K., Chu, Z.R., Liu, Y.J., Zhu, M.T., Yang, L., Zhang, J., 2013. Molecular characterization of microbial populations in full-scale biofilters treating iron, manganese and ammonia containing groundwater in Harbin, China. Bioresour. Technol. 147, 234e239. Liang, J., Bai, Y., Men, Y., Qu, J., 2017. Microbe-microbe interactions trigger Mn(II)oxidizing gene expression. ISME J. 11 (1), 67e77. Lozupone, C., Knight, R., 2005. Unifrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71 (12), 8228e8235. Lozupone, C.A., Hamady, M., Kelley, S.T., Knight, R., 2007. Quantitative and qualitative beta diversity measures lead to different insights into factors that

11

structure microbial communities. Appl. Environ. Microbiol. 73 (5), 1576e1585. Matsushita, S., Komizo, D., Cao, L.T.T., Aoi, Y., Kindaichi, T., Ozaki, N., Imachi, H., Ohashi, A., 2018. Production of biogenic manganese oxides coupled with methane oxidation in a bioreactor for removing metals from wastewater. Water Res. 130, 224e233. Mougi, A., Kondoh, M., 2012. Diversity of interaction types and ecological community stability. Science 337 (6092), 349e351. Ni, B.J., Rittmann, B.E., Yu, H.Q., 2011. Soluble microbial products and their implications in mixed culture biotechnology. Trends Biotechnol. 29 (9), 454e463. Nitzsche, K.S., Weigold, P., Losekann-Behrens, T., Kappler, A., Behrens, S., 2015. Microbial community composition of a household sand filter used for arsenic, iron, and manganese removal from groundwater in Vietnam. Chemosphere 138, 47e59. Oh, S., Hammes, F., Liu, W.-T., 2018. Metagenomic characterization of biofilter microbial communities in a full-scale drinking water treatment plant. Water Res. 128, 278e285. Palomo, A., Jane Fowler, S., Gulay, A., Rasmussen, S., Sicheritz-Ponten, T., Smets, B.F., 2016. Metagenomic analysis of rapid gravity sand filter microbial communities suggests novel physiology of Nitrospira spp. ISME J. 10 (11), 2569e2581. Peng, H., Pearce, C.I., N’Diaye, A.T., Zhu, Z., Ni, J., Rosso, K.M., Liu, J., 2019. Redistribution of electron equivalents between magnetite and aqueous Fe2þ induced by a model quinone compound AQDS. Environ. Sci. Technol. 53 (4), 1863e1873. Pinto, A.J., Xi, C., Raskin, L., 2012. Bacterial community structure in the drinking water microbiome is governed by filtration processes. Environ. Sci. Technol. 46 (16), 8851e8859. Qin, S., Ma, F., Huang, P., Yang, J., 2009. Fe (II) and Mn (II) removal from drilled well water: a case study from a biological treatment unit in Harbin. Desalination 245 (1e3), 183e193. Regan, J.M., Harrington, G.W., Noguera, D.R., 2002. Ammonia- and nitrite-oxidizing bacterial communities in a pilot-scale chloraminated drinking water distribution system. Appl. Environ. Microbiol. 68 (1), 73e81. Rittmann, B.E., 2006. Microbial ecology to manage processes in environmental biotechnology. Trends Biotechnol. 24 (6), 261e266. Sahabi, D.M., Takeda, M., Suzuki, I., Koizumi, J., 2009. Removal of Mn2þ from water by “aged” biofilter media: the role of catalytic oxides layers. J. Biosci. Bioeng. 107 (2), 151e157. Schreiber, K., Krieger, R., Benkert, B., Eschbach, M., Arai, H., Schobert, M., Jahn, D., 2007. The anaerobic regulatory network required for Pseudomonas aeruginosa nitrate respiration. J. Bacteriol. 189 (11), 4310e4314. 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 (11), 2498e2504. Su, J., Bao, P., Bai, T., Deng, L., Wu, H., Liu, F., He, J., 2013. CotA, a multicopper oxidase from Bacillus pumilus WH4, exhibits manganese-oxidase activity. PLoS One 8 (4), e60573. Tebo, B.M., Bargar, J.R., Clement, B.G., Dick, G.J., Murray, K.J., Parker, D., Verity, R., Webb, S.M., 2004. Biogenic manganese oxides: properties and mechanisms of formation. Annu. Rev. Earth Planet Sci. 32 (1), 287e328. Vignola, M., Werner, D., Wade, M.J., Meynet, P., Davenport, R.J., 2018. Medium shapes the microbial community of water filters with implications for effluent quality. Water Res. 129, 499e508. White, C.P., DeBry, R.W., Lytle, D.A., 2012. Microbial survey of a full-scale, biologically active filter for treatment of drinking water. Appl. Environ. Microbiol. 78 (17), 6390e6394. Wu, K., Liu, R., Liu, H., Zhao, X., Qu, J., 2011. Arsenic(III, Ⅴ) adsorption on iron-oxidecoated manganese sand and quartz sand: comparison of different carriers and adsorption capacities. Environ. Eng. Sci. 28 (9), 643e651. Yang, L., Li, X., Chu, Z., Ren, Y., Zhang, J., 2014. Distribution and genetic diversity of the microorganisms in the biofilter for the simultaneous removal of arsenic, iron and manganese from simulated groundwater. Bioresour. Technol. 156, 384e388. Zaura, E., Keijser, B.J., Huse, S.M., Crielaard, W., 2009. Defining the healthy “core microbiome” of oral microbial communities. BMC Microbiol. 9, 259. Zhao, X., Wang, X., Liu, B., Xie, G., Xing, D., 2018. Characterization of manganese oxidation by Brevibacillus at different ecological conditions. Chemosphere 205, 553e558. Zhou, H., Liu, B., Wang, Q., Sun, J., Xie, G., Ren, N., Ren, Z.J., Xing, D., 2017. Pulse electromagnetic fields enhance extracellular electron transfer in magnetic bioelectrochemical systems. Biotechnol. Biofuels 10, 238.