- Email: [email protected]
Metagenomic signatures of a tropical mining-impacted stream reveal complex microbial and metabolic networks
Chemosphere 161 (2016) 266e273
Contents lists available at ScienceDirect
Chemosphere journal homepage: www.elsevier.com/locate/chemosphere
Metagenomic signatures of a tropical mining-impacted stream reveal complex microbial and metabolic networks a Mariana P. Reis a, Marcela F. Dias a, Patrícia S. Costa a, Marcelo P. Avila , Laura R. Leite b, ^ nica Bucciarelli-Rodriguez a, vio M.G. de Araújo b, Anna C.M. Salim b, Mo Fla b, c a M.A. Nascimento a, * Guilherme Oliveira , Edmar Chartone-Souza a, Andre a gicas, Universidade Federal de Minas Gerais, Av. Anto ^nio Carlos 6627, Belo Horizonte, Minas Departamento de Biologia Geral, Instituto de Ci^ encias Biolo Gerais 31270-901, Brazil b ~o Oswaldo Cruz, Av. Augusto de Lima 1715, Belo Horizonte, Minas Gerais 30190-002, Brazil Centro de Pesquisas Ren e Rachou Fundaça c 66055-090, Brazil gico Vale, Rua Boaventura da Silva 955, Bel Instituto Tecnolo em, Para
h i g h l i g h t s The microbiome of a metal-rich tropical stream sediment was studied by metagenomics. The metagenome showed complexity of microbial interaction and metabolic processes. Functional analysis highlighted nitrogen and methane metabolic pathways. Metal stress seems to contribute to distinct life strategies in this environment.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 1 March 2016 Received in revised form 24 June 2016 Accepted 26 June 2016
Bacteria from aquatic ecosystems significantly contribute to biogeochemical cycles, but details of their community structure in tropical mining-impacted environments remain unexplored. In this study, we analyzed a bacterial community from circumneutral-pH tropical stream sediment by 16S rRNA and shotgun deep sequencing. Carrapatos stream sediment, which has been exposed to metal stress due to gold and iron mining (21 [g Fe]/kg), revealed a diverse community, with predominance of Proteobacteria (39.4%), Bacteroidetes (12.2%), and Parcubacteria (11.4%). Among Proteobacteria, the most abundant reads were assigned to neutrophilic iron-oxidizing taxa, such as Gallionella, Sideroxydans, and Mariprofundus, which are involved in Fe cycling and harbor several metal resistance genes. Functional analysis revealed a large number of genes participating in nitrogen and methane metabolic pathways despite the low concentrations of inorganic nitrogen in the Carrapatos stream. Our findings provide important insights into bacterial community interactions in a mining-impacted environment. © 2016 Elsevier Ltd. All rights reserved.
Handling Editor: Shane Snyder Keywords: Shotgun metagenome Iron-rich sediment 16S rRNA Freshwater
1. Introduction Freshwater ecosystems are threatened by a myriad of pollutants, especially those generated by mining activities. Among these, iron ores mining is of great importance since iron is the most mined metal worldwide (Edwards et al., 2004). Brazil is one of the world’s largest producers of iron ores (Yellishetty et al., 2010), which considerably impact Brazilian ecosystems. Iron exists in different oxidation states, but in biological
* Corresponding author. E-mail address: [email protected] (A.M.A. Nascimento). http://dx.doi.org/10.1016/j.chemosphere.2016.06.097 0045-6535/© 2016 Elsevier Ltd. All rights reserved.
systems, it can be found as Fe(II) or Fe(III). Redox cycling of iron in aquatic ecosystems can be closely tied to biogeochemical transformations of other elements, including carbon, nitrogen, and sulfur. In addition, iron cycling is important for pollutant transformation and mobility, meaning great environmental relevance of iron (Ghiorse, 1984; Lovley, 2000; Emerson and Weiss, 2004; Picardal and Cooper, 2005; Roden and Emerson, 2007). Iron redox reactions are often driven by microorganisms. Fereducing bacteria (FeRB), e.g., Geobacter sulfurreducens, couple reduction of Fe(III) with oxidation of organic matter or molecular hydrogen, while Fe-oxidizing bacteria (FeOB), which occur in many phyla (e.g., Nitrospirae, Firmicutes, Actinobacteria, Chlorobi, and
M.P. Reis et al. / Chemosphere 161 (2016) 266e273
especially Proteobacteria), can use Fe(II) or molecular hydrogen as electron donors (Lovley et al., 1989; Jiao et al., 2005; Emerson et al., 2010; Byrne et al., 2015). Recent advances in metagenomic technologies allow for more comprehensive assessment of the microbial biodiversity and functioning in aquatic environments. Sediments are important for metal cycling in these ecosystems because they can retain over 90% of metals and metalloid pollutants and have higher microbial diversity than bulk water (Lozupone and Knight, 2007; Mortatti et al., 2010). To our knowledge, only a few other studies that involved pyrosequencing and denaturing gradient gel electrophoresis have shown the structure of microbial communities in iron-rich environments at circumneutral pH (Gorra et al., 2012; Wang et al., 2014; Emerson et al., 2015). Although FeOB are present under oxic and anoxic conditions, from extremely acidic to circumneutral pH, only a few genera of neutrophilic FeOB have been identified to date, e.g., Gallionella, Sideroxydans, Ferriphaselus, Ferritrophicum, and Mariprofundus (Emerson et al., 2010; Kato et al., 2014). Therefore, ironrich sediments of tropical freshwater streams are not sufficiently explored. In this study, we performed a taxonomical and functional analysis of a microbial community from mining-impacted stream sediment by means of metagenomics and compared our results with different datasets in order to improve the current understanding of the role of bacteria in iron-rich environments. 2. Materials and methods 2.1. The study site The Carrapatos stream (CS; 19 580 15.400 S and 43 270 50.700 W) is located in the Iron Quadrangle (Minas Gerais state, Brazil), a region rich in ores, especially iron ones, explored by mining since the 19th century. One study (Reis et al., 2014) showed that CS can be classified as a mesotrophic, circumneutral (pH 7.9), and mesothermal (17 C) environment. Furthermore, its sediment contains high metal concentrations, including magnesium (1416 mg kg1), manganese (2319 mg kg1) and especially iron (21,850 mg kg1) (Reis et al., 2014). 2.2. DNA extraction A composite sample from CS sediment (100 g) was collected, and DNA was extracted using the PowerSoil DNA Extraction Kit (MoBio Laboratories, USA). Quantification of total DNA was performed using a Qubit fluorometer (InvitrogeneLife Technologies, USA). 2.3. Sequencing and analysis of the V3 and V4 regions of the 16S rRNA gene For taxonomic profiling, primers S-D-Bact-0341-b-S-17 (50 CCTACGGGNGGCWGCAG30 ) and S-D-Bact-0785-a-A-21 (50 GACTACHVGG-GTATCTAATCC30 ) (Klindworth et al., 2013), targeting the V3 and V4 hypervariable regions of the bacterial 16S rRNA gene were used, with Illumina adapters added. The amplification step was performed with the KAPA HiFi HotStart ReadyMix (KAPA, Woburn, MA, USA), followed by purification with AMPure XP beads (Agencourt Bioscience, Beverley, MA, USA). Library construction was performed according to the manufacturer’s instructions for paired-end sequencing on the MiSeq platform (Illumina, Inc., USA). Bioinformatic analysis was carried out in the Mothur software, version 1.33.0 (http://www.mothur.org). Sequences of low quality
267
(Q 20, with ambiguities and homopolymers above eight) or outside the range of 422e466 bp were discarded. Filtered reads were aligned and classified against the Silva v.223 16S rRNA database (Quast et al., 2013). Mitochondrial and chloroplast reads and reads that did not match any reference sequence were discarded. Chimeric reads were identified and removed by means of Uchime (http://drive5.com/uchime). The remaining reads were grouped into operational taxonomic units (OTUs), with 97% similarity, by the average neighbor method. Construction of the rarefaction curve, calculated for the evolutionary distance of 0.03, was conducted according to the Mothur pipeline (Schloss et al., 2009). The nucleotide sequences were deposited to GenBank [GenBank ID: SRR2087753]. 2.4. Shotgun sequencing and analysis To obtain a functional profile of the metagenome, wholecommunity shotgun sequencing from the CS sediment was performed on a SOLiD™ v.4 sequencer (Applied Biosystems), according to the manufacturer’s protocol. Briefly, 10 mg of total DNA was randomly fragmented using the Covaris™ S2 system (Life Technologies, USA). DNA fragment library (200e250 bp long) was then constructed. Emulsion PCR was carried out to clonally amplify fragments on sequencing beads, followed by enrichment, deposition in a plate, and sequencing. The DNA fragment size of the library was verified using the Agilent 2100 Bioanalyzer. Bioinformatic analysis comprised an initial step of error screening in the SOLiD™ Accuracy Enhancer Tool (SAET) software (http://solidsoftwaretools.com/gf) and conversion of sequences from color space to letter space format by means of the encodeFasta.py software (http://gnome.googlecode.com/svn/trunk/ pyGenotypeLearning/src/pytools/encodeFasta.py). Filtered reads were assembled in the Metavelvet software, as described by Namiki et al. (2012). Contigs were analyzed by means of the Metagenomics RAST Server (MG-RAST v3.3) (Meyer et al., 2008), which provides quality control (duplicate removal and size or base quality screening) prior to annotation. Functional analysis was conducted using SEED and KEEG subsystems (available on the MG-RAST Server), with an e-value of 105 and identity of 60% (Mitra et al., 2011; Costa et al., 2015). Sequencing data of the present study are available at MG-RAST (code 4520540.3). The metagenome from the CS sediment was compared with two other metagenomes, one from an arsenic-rich sediment (Mina stream sediment [MSS]: MG-RAST ID 4519449.3) and another from rin [FER]: NCBI a low-metal-concentration freshwater sediment (Fe Bioproject ID 246439), described by Costa et al. (2015) and Gillan et al. (2014), respectively. These metagenomes were chosen for the comparative analysis on the basis of the study region similarity with CS (MSS) and availability of pristine-environment data (FER). To identify possible significant differences with CS in the functional composition of each metagenome, we performed two-sided Fisher’s exact test (Fisher, 1958), with the p value > 0.05, using the Statistical Analysis of Metagenomic Profiles (STAMP; Parks and Beiko, 2010). Metagenomic recruitment plots were used to identify the abundant species in the CS metagenome, by comparing the CS contigs with individual bacterial genomes available in the National Center for Biotechnology Information (NCBI) database, as reported by Costa et al. (2015). 2.5. Quantitative real-time PCR (qPCR) qPCR was used to estimate the absolute number of copies of bacterial 16S rDNA in the sediment. The primers were 338F (50 TACGGGAGGCAGCAG30 ) (Raskin et al., 1994) and 518R
268
M.P. Reis et al. / Chemosphere 161 (2016) 266e273
(50 ATTACCGCGGCTGCTGG30 ) (Muyzer et al., 1993). The procedure was performed on an ABI PRISM 7900HT sequence detection system (Applied Biosystems, Foster City, CA) as described by Reis et al. (2013). Standard curves were generated from seven dilutions of 16S amplicons from Escherichia coli ATCC 25922. A control reaction without template DNA was included in the qPCR assay. Triplicate reactions were run for standards, DNA samples, and negative control. The quantification procedure yielded a slope of 3.23 and R2 value greater than 0.99 (Fig. S1).
3. Results 3.1. An overview of the taxonomic composition and abundance based on 16S rRNA and qPCR data The high-quality reads were grouped into 34,827 OTUs. Table S1 summarizes the quality control dataset from the CS library. The number of reads and taxonomic assignments of these OTUs are presented in Table S2. The rarefaction curve (Fig. S2) and high Good’s coverage value (90%) indicated that the sequencing depth was sufficient to capture overall diversity. qPCR revealed the bacterial concentration of 1.04 108 cells/g in the CS sediment (Fig. S1). The taxonomic data indicated high diversity in CS, spanning a wide spectrum of phyla (Fig. 1 and Table S2) in addition to unclassified bacteria (5.9% of all the reads). Proteobacteria (39.4%), Bacteroidetes (12.2%), and Parcubacteria (candidate division OD1: 11.4%) were the predominant phyla, accounting for 63% of all reads. Other phyla such as Cyanobacteria, Chloroflexi, Acidobacteria, Nitrospirae, Verrucomicrobia, Microgenomates, Candidate division OP3, and Planctomycetes were present in lower proportions, ranging from 2% to 4%. Although Chloroflexi accounted for only 3.5% of all the bacterial reads, this phylum harbored the fourth most abundant OTU (Anaerolineaceae) of the CS metagenome. OTUs with abundance below 2% were grouped into “Other bacteria”, which included Chlamydiae, Actinobacteria, Elusimicrobia, Chlorobi, Spirochaetes, Gemmatimonadetes, Armatimonadetes,
Fig. 1. Taxonomic composition of bacterial taxa from Carrapatos stream (CS) sediment, on the basis of the Silva database. The more representative taxa of each phylum are highlighted in colors. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Saccharibacteria, and Gracilibacteria, representing 8.9% of all reads (Table S2). Proteobacteria comprised broad diversity, with five identified classes: Beta- (44.61%), Delta- (19.8%), Alpha- (17.1%), Gamma(13.1%), and Zetaproteobacteria (2.7%) (Fig. 1). The 50 top proteobacterial OTUs accounted for 48.4% of all the proteobacterial reads, comprising 46% of representatives of various orders or families, such as Gallionellaceae (15.3%), Comamonadaceae (7.5%), Methylococcales (4.7%), Desulfuromonadales (3.7%), Mariprofundaceae (2.5%), Rhodobacteraceae (2.5%), Hyphomonadaceae (2.4%), Rhizobiales (2.3%), Bdellovibrionaceae (1.1%), Sphingomonadaceae (1%), Rhodocyclaceae (0.9%), and Caulobacteraceae (0.5%). The remaining OTUs were classified only at phylum (0.05%) or class levels (Betaproteobacteria, 3.1%). Among the 23 most frequent OTUs (>1000 reads), 10 OTUs were affiliated at the genus level, namely Gallionella, Mariprofundus, Sideroxydans, and Woodsholea (Proteobacteria); Haliscomenobacter, Sediminibacterium, and Leadbetterella (Bacteroidetes); and Nitrospira (Nitrospirae).
3.2. Functional analysis of the metagenome The whole-community shotgun sequencing generated approximately 14.5 Gbp, corresponding to 289,015,844 reads 50 bp long. After MG-RAST quality filtering, 278,993,472 reads remained for further analysis (~13.9 Gbp of reads). Assembly of individual reads by means of Metavelvet resulted in 439,778 contigs, with the length of 157 ± 98 bp (mean ± SD). Functional annotation of the CS metagenome revealed that 48% of the contigs (183,032/380,882) can be classified against protein databases available at MG-RAST. Among these, approximately 73% (133,356 contigs) were assigned to functional categories. The comparison between the CS metagenome and reference genomes showed that the majority of contigs were assigned to Sideroxydans lithotrophicus ES-1 (24,064 contigs) and Gallionella capsiferriformans ES-2 (19,255 contigs), which are neutrophilic FeOB (Fig. 2). Other bacterial species were reasonably well recruited, such as Leptothrix cholodnii, which is also a FeOB, Nitrosospira multiformis ATCC 25196 and Nitrosomonas europaea ATCC 19718, both ammonia-oxidizing bacteria (Fig. S3). A broad range of functional categories was detected by SEED subsystems (Fig. 3). The highest proportions of functional genes were categorized into the clustering-based subsystem (unknown function) and protein metabolism subsystem (10% each), followed by the miscellaneous subsystem (9%); RNA metabolism subsystem (7%); and virulence, disease, and defense subsystem (4%). The latter harbored heavy-metal (and metalloid) resistance genes, mainly cobalt-zinc-cadmium resistance (20%), copper homeostasis (12%), and arsenic resistance (9%). The Kyoto Encyclopedia of Genes and Genomes (KEGG) Mapper tool, available at MG-RAST, produced an integrated view of the CS microbiome metabolism. KEGG pathway analysis revealed that the CS metagenome contigs were distributed among the majority of the metabolic pathways, including 33 of 68 genes involved in nitrogen metabolism and 13 of 34 genes related to methane metabolism (Figs. S4 and S5). The SEED subsystem classification ranked the most abundant functions in the nitrogen metabolism subsystem, such as nitrate and nitrite ammonification, ammonia assimilation, and nitrogen fixation. Functional comparative analysis between CS metagenome and MSS metagenome (arsenic-rich stream; Fig. 4) and between CS metagenome and FER metagenome (low-metal freshwater sediment; Fig. 5) revealed different profiles.
M.P. Reis et al. / Chemosphere 161 (2016) 266e273
269
Fig. 2. Fragment recruitment plots of CS contigs for (A and B) Sideroxydans lithotrophicus ES-1 (CP001965.1) and (C and D) Gallionella capsiferriformans ES-2 (NC_014394.1). The comparison was made using BLASTn. (A and C) Recruitment by means of the R software; the vertical axis shows the percentage of identity of the metagenomic contigs to the respective bacterial genome. (B and D) Recruitment by means of MG-RAST.
4. Discussion The main purpose of this study was to enlighten the connections between microbial functions and community structure of a tropical stream sediment exposed to long-term mining activities. The metagenomic data revealed a diverse bacterial community characterized by a complex metabolic network. Our results are consistent with other studies that showed the dominance of Proteobacteria in communities from metal-rich environments (Horner-Devine et al., 2004; Chen et al., 2008; Reis et al., 2013). Members of FeOB are distributed among a variety of phyla, such as Nitrospirae and Firmicutes, but Proteobacteria, especially all neutrophilic iron-oxidizing Beta- and Zetaproteobacteria, are the most numerous (Hedrich et al., 2011). Noticeably, the assignment of Mariprofundus (iron-oxidizing Zetaproteobacteria), which was detected in our sample, contradicts the findings of Hedrich et al. (2011). Our results do not support the separation of neutrophilic Fe oxidizing Beta- and Zetaproteobacteria by their habitat: freshwater (Betaproteobacteria) versus marine water (Zetaproteobacteria). This marine genus is a representative of a group that has the ability to produce an Fe-oxyhydroxide-encrusted helical stalk:
apparently a requirement for the use of ferrous iron, Fe(II), as an energy source (Singer et al., 2011). The enrichment of the CS community in various neutrophilic FeOB groups points to their importance for iron cycling in this ecosystem. Other studies on the phylogenetic diversity and ecological properties of Gallionella-related FeOB revealed that this group is highly adapted to metal-rich environments because its genome harbors several resistance genes to different metals and metalloid, such as arsenic, mercury, cadmium, cobalt, silver, zinc, and copper (Emerson et al., 2013; Reis et al., 2014). Furthermore, Emerson et al. (2013) reported that both Gallionella and Sideroxydans have a rich genetic repertory for adaptation to stressful conditions, such as environmental sensing, motility, and chemotaxis; this repertory may allow them to survive in this miningimpacted ecosystem. Crenothrix-related OTUs (3566 reads) represented an abundant genus in the CS community. These filamentous FeOB are closely related to methanotrophs known to promote steel corrosion (Coetser and Cloete, 2005; Stoecker et al., 2006). Twentythree OTUs (417 reads) were found to be affiliated with the Ferriphaselus genus, novel stalk-forming neutrophilic FeOB, recently described by Kato et al. (2014).
270
M.P. Reis et al. / Chemosphere 161 (2016) 266e273
Fig. 3. Distribution of SEED subsystems of the Carrapatos stream sediment on the basis of the MG-RAST annotation. The cutoff parameters were as follows: e-value 105 and identity 60%.
Fig. 4. Significant SEED subsystem differences as a result of Fisher’s exact test between the Carrapatos stream (CS) sediment and Mina stream sediment (MSS), conducted in the STAMP software. Enrichment of the CS metagenome with the SEED subsystem has a negative difference between proportions (orange circles), while enrichment of the MSS metagenome with the SEED subsystem has a positive difference between proportions (blue circles). Bars on the left represent the proportion of each subsystem in the data. Subsystem differences with a p value of >0.05 were considered significant. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
The most abundant genera affiliated with Bacteroidetes were Haliscomenobacter, Sediminibacterium, and Leadbetterella, which are not well studied at present. Members of this phylum are widely distributed in nature, especially in aquatic environments (Weon et al., 2005). The Haliscomenobacter genus comprises filamentous
species that inhabit the pelagic zone of natural freshwater lakes and ponds (Hahn and Schauer, 2007; Santos et al., 2015). Sediminibacterium is rarely found in mining-impacted environments although it was previously recovered from metal-rich sediment (Reis et al., 2013). Leadbetterella was originally isolated from cotton-
M.P. Reis et al. / Chemosphere 161 (2016) 266e273
271
Fig. 5. Significant SEED subsystem differences as a result of Fisher’s exact test between the Carrapatos stream (CS) sediment and low-metal-concentration freshwater sediment rin, FER) conducted in the STAMP software. Enrichment of the CS metagenome with the SEED subsystem has a negative difference between proportions (orange circles), while (Fe enrichment of the FER metagenome with the SEED subsystem has a positive difference between proportions (blue circles). Bars on the left represent the proportion of each subsystem in the data. Subsystem differences with a p value of >0.05 were considered significant. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
waste composts (Weon et al., 2005), and the knowledge about its metabolism and ecology is limited. The third most abundant phylum in the CS community is Parcubacteria. Members of this phylum have been identified in lakes, mainly under suboxic conditions, but they were barely studied. High abundance of this phylum was reported only in a few studies e et al., 2007; Peura et al., 2012), including the present one. (Brie According to Peura et al. (2012), this finding could be explained by insufficient coverage of Parcubacteria members in previous bacterial PCR surveys, due to the use of general bacterial primers, which do not target this group. The primers used by Peura et al. (2012) and in our study span variable regions V3 and V4 of the 16S rRNA gene, thereby promoting much greater coverage of known sequences of Parcubacteria. Recently, Parcubacteria (OD1), Microgenomates (OP11), Saccharibacteria (GN02), and Gracilibacteria (TM7) phyla, represented by 15% of all the reads here, were proposed to form a new superphylum called Patescibacteria (Rinke et al., 2013; Sekiguchi et al., 2015; Shipunov, 2015). Genomic and metagenomic studies showed that members of this superphylum have reduced metabolic capabilities, which likely limit their cultivation (Kantor et al., 2013; Rinke et al., 2013; Brown et al., 2015). They are also involved in hydrogen production, sulfur cycling (Wrighton et al., 2012, 2014), and anaerobic methane oxidation (Peura et al., 2012). Because of the high proportion of members of this superphylum and genes related to the methane oxidation pathway, it is possible that these bacteria are responsible for methane oxidation in the CS sediment. Anaerolineaceae-related reads represented a majority in the Chloroflexi phylum (77.6% of all the Chloroflexi reads). This phylum was previously identified as green nonsulfur bacteria and includes relatively unstudied organisms with diversified metabolism (Hug et al., 2013). The Anaerolineaceae family was formally proposed in 2006 and comprises heterotrophic and anaerobic bacteria inhabiting many environments, including sulfur springs and sulfide-rich anaerobic reactors (Yamada et al., 2006; Youssef et al.,
2012; Dias et al., 2016). It is still unclear whether these bacteria can use sulfur compounds in their metabolic reactions (Youssef et al., 2012), but the presence of this taxon, in addition to other OTUs related to bacteria involved in sulfur cycling, e.g., Thiotrichales and Desulfuromonadales, may be an indication of sulfur compound transformations coupled to iron redox reactions in the CS sediment. The functional analysis of the CS metagenome revealed dominance of clustering-based and protein metabolism subsystems, whose genes are thought to be related and linked functionally (Hu et al., 2010). Hence it is quite difficult to comment on the genes assigned to these subsystems or to speculate on their roles. We detected high abundance of contigs related to protein metabolism, including biosynthesis, degradation, folding, processing, and modification of proteins. This result reflects the high diversity, abundance, and metabolic activity of microorganisms in the CS sediment. Although the Carrapatos freshwater sample showed low concentrations of inorganic forms of N, in contrast to the arsenic-rich freshwater (MSS) analyzed by Costa et al. (2015), the CS metagenome over-represents the nitrogen metabolic pathway. Reads related to N2 fixation were obtained here, possibly pointing to an activity of Sideroxydans, neutrophilic FeOB that possess three clusters of nif genes. This feature may allow these bacteria to occupy niches depleted of nitrogen and may confer a competitive advantage over other organisms. Furthermore, although ammoniaoxidizing bacteria were well represented in the shotgun results, only a few reads were detected in the 16S rRNA dataset (222/ 229,995 reads). This discrepant result may be due to the fact that shotgun sequencing does not depend on PCR amplification; thus, it is not affected by primer bias. Additionally, in a previous study on ammonia oxidizing bacteria in tropical stream sediments by Reis et al. (2015), the number of copies of the amoA gene on the same CS sediment sample was determined. Environments that have high Fe concentrations may also have relatively high concentrations of other metals and metalloids,
272
M.P. Reis et al. / Chemosphere 161 (2016) 266e273
including toxic ones (Gibbs, 1977). The resistance to antibiotics and toxic compounds was highlighted in the category of virulence, disease, and defense. Indeed, this resistance ensures survival of the microbial community in the contaminated environment of the CS sediments. Fluoroquinolones and methicillin are among the antibiotics whose resistance genes were detected in the CS sediment. These genes are common in environments that receive human and animal waste as well as heavy metals and metalloid (e.g., cobalt, zinc, cadmium, copper, and arsenic) (Borr as et al., 2012; Li et al., 2012). Probably, antibiotic resistance and heavy-metal resistance genes were coselected, thus contributing to survival in this sediment. Emerson et al. (2013) reported genes responsible for arsenic resistance and genes that encode a heavy-metal efflux pump of the cation diffusion facilitator (CDF) family. This efflux pump is responsible for removal of structurally unrelated toxic metals like cadmium, cobalt, silver, zinc, and copper from the cytoplasm. These genes were found in the genomes of Sideroxydans lithotrophicus and Gallionella capsiferriformans. Functional comparisons between the CS and MSS metagenomes and between the FER and CS metagenomes revealed that the functional categories recovered from the CS metagenome were more similar to those from the FER metagenome than to functional categories from the MSS metagenome. This finding suggests that the CS sediment harbors a metabolic network close to that of the reference sediment. Moreover, these comparisons unveiled overrepresentation of the virulence, disease, and defense subsystem (MSS). Our results also showed overrepresentation of stress response subsystems and subsystems of phages, prophages, transposable elements, plasmids (CS), all which contribute to survival of the microbial community in mining-impacted environments. The diverse, abundant, and functionally complex bacterial community in the CS sediment thus represents a well-adapted community. This finding contradicts the ecological models that predict that community diversity decreases in response to stressors associated with toxic compounds (Odum, 1985). This apparent discrepancy may be due to the historical contamination of the Iron Quadrangle region. Indeed, other studies suggest that the composition and stability of bacterial communities in historically metalcontaminated sediments become diverse after approximately one century of exposition (Bouskill et al., 2010; Reis et al., 2013).
5. Conclusion Both shotgun data and 16S analysis point to a diverse community in CS sediment, where many metabolic processes take place, as confirmed by detection of genes related to iron, methane, nitrogen, and sulfur pathways. Moreover, our data highlight the occurrence of rare taxa, like Leadbetterella, and to our knowledge, unprecedented findings about Woodsholea and Mariprofundus, previously recovered from marine environments. These results provide insights into the interactions and metabolic abilities of bacteria in mining-impacted sediments and may contribute to elucidate the role of bacteria in environments exposed to long-term metal stress.
Acknowledgments We are grateful to Dr. Roscoe Stanyon for his valuable contririo de Ana lises Químicas/DEMET/UFMG of butions, to the Laborato ^ncia e Tecnologia em Recursos Minthe Instituto Nacional de Cie erais, Agua e Biodiversidade (INCTeACQUA) for technical support, and to the funding agencies CNPq nu472411/ 2012-8, CNPq/INCT no 15206-7, and FAPEMIG APQ 00801/12 and APQ-00463-13.
Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.chemosphere.2016.06.097. References , R., Prat, M.D., 2012. Analysis of fluoBorr as, S., Ríos-Kristj ansson, J.G., Companyo roquinolones in animal feeds by liquid chromatography with fluorescence detection. J. Sep. Sci. 35, 2048e2053. Bouskill, N.J., Barker-Finkel, J., Galloway, T.S., Handy, R.D., Ford, T.E., 2010. Temporal bacterial diversity associated with metal-contaminated river sediments. Ecotoxicology 19, 317e328. pez-Gracía, P., 2007. Archaeal and bacterial community Briee, C., Moreira, D., Lo composition of sediment and plankton from a suboxic freshwater pond. Res. Microbiol. 158, 213e227. Brown, C., Hug, L.A., Thomas, B.C., Sharon, I., Castelle, C.J., Singh, A., Wilkins, M.J., Wrighton, K.C., Williams, K.H., Banfield, J.F., 2015. Unusual biology across a group comprising more than 15% of domain Bacteria. Nature 523, 208e211. Byrne, J.M., Klueglein, N., Pearce, C., Rosso, K.M., Appel, E., Kappler, A., 2015. Redox cycling of Fe(II) and Fe(III) in magnetite by Fe-metabolizing bacteria. Science 347, 1473e1476. Chen, Y.Q., Ren, G.J., An, S.Q., Sun, Q.Y., Liu, C.H., Shuang, J.L., 2008. Changes in bacterial community structure in copper mine tailings after colonisation of reed (Phragmites communis). Pedosphere 18, 731e740. Coetser, S.E., Cloete, T.E., 2005. Biofouling and biocorrosion in industrial water systems. Crit. Rev. Microbiol. 31, 213e232. Costa, P.S., Reis, M.P., Avila, M.P., Leite, L.R., Araújo, F.M.G., Salim, A.C.M., Oliveira, G., Barbosa, F., Chartone-Souza, E., Nascimento, A.M.A., 2015. Metagenome of a microbial community inhabiting a metal-rich tropical stream sediment. PLOS ONE. http://dx.doi.org/10.1371/journal.pone.0119465. Dias, M.F., Colturato, L.F., Oliveira, J.P., Leite, L.R., Oliveira, G., Chernicharo, C.A., Araújo, J.C., 2016. Metagenomic analysis of a desulphurisation system used to treat biogas from vinasse methanisation. Biores. Technol. 205, 58e66. Edwards, K.J., Bach, W., McCollom, T.M., Rogers, D.R., 2004. Neutrophilic ironoxidizing bacteria in the ocean: their habitat, diversity, and roles in mineral deposition, rock alteration, and biomass production in the deep-sea. Geomicrobiol. J. 21, 393e404. Emerson, D., Field, E.K., Chertkov, O., Davenport, K.W., Goodwin, L., Munk, C., Nolan, M., Woyke, T., 2013. Comparative genomics of freshwater Fe-oxidizing bacteria: implications for physiology, ecology, and systematics. Front. Microbiol. 4, 1e17. Emerson, D., Scott, J.J., Benes, J., Bowden, W.B., 2015. Microbial iron oxidation in the Arctic tundra and its implications for biogeochemical cycling. Appl. Environ. Microbiol. 81, 8066e8075. Emerson, D., Weiss, J.V., 2004. Bacterial iron oxidation in circumneutral freshwater habitats: findings from the field and the laboratory. Geomicrobiol. J. 21, 405e414. Emerson, D., Fleming, E.J., McBeth, J.M., 2010. Iron-oxidizing bacteria: an environmental and genomic perspective. Annu. Rev. Microbiol. 64, 561e583. Fisher, W.D., 1958. On grouping for maximum homogeneity. J. Am. Stat. Assoc. 53, 789e798. Ghiorse, W.C., 1984. Biology of iron-and manganese-depositing bacteria. Ann. Rev. Microbiol. 38, 515e550. Gibbs, R.J., 1977. Transport phases of transition metals in the Amazon and Yukon rivers. Soc. Amer. J. 59, 1773e1781. Gillan, D.C., Roosa, S., Kunath, B., Billon, G., Wattiez, R., 2014. The long-term adaptation of bacterial communities in metal-contaminated sediments: a metaproteogenomic study. Environ. Microbiol. http://dx.doi.org/10.1111/14622920.12627. Gorra, R., Webster, G., Martin, M., Celi, L., Mapelli, F., Weightman, A.J., 2012. Dynamic microbial community associated with iron-arsenic co-precipitation products from a groundwater storage system in Bangladesh. Microb. Ecol. 64, 171e186. Hahn, M.W., Schauer, M., 2007. ‘Candidatus Aquirestis calciphila’ and ‘Candidatus Haliscomenobacter calcifugiens’, filamentous, planktonic bacteria inhabiting natural lakes. Int. J. Syst. Evol. Microbiol. 57, 936e940. €mann, M., Johnson, D.B., 2011. The iron-oxidizing proteobacteria. Hedrich, S., Schlo Microbiology 157, 1551e1564. Horner-Devine, M.C., Carney, K.M., Bohannan, B.J.M., 2004. An ecological perspective on bacterial biodiversity. Proc. R. Soc. Lond 271, 113e122. Hu, Y., Fu, C., Yin, Y., Cheng, G., Lei, F., Yang, X., Li, J., Ashforth, E.J., Zhang, L., Zhu, B., 2010. Construction and preliminary analysis of a deep-sea sediment metagenomic fosmid library from Qiongdongnan basin, South China sea. Mar. Biotechnol. 12, 719e727. Hug, L.A., Castelle, C.J., Wrighton, K.C., Thomas, B.C., Sharon, I., Frischkorn, K.R., et al., 2013. Community genomic analyses constrain the distribution of metabolic traits across the Chloroflexi phylum and indicate roles in sediment carbon cycling. Microbiome 1, 1e13. Jiao, Y., Kappler, A., Croal, L.R., Newman, D.K., 2005. Isolation and characterization of a genetically tractable photoautotrophic Fe(II)-oxidizing bacterium, Rhodopseudomonas palustris strain TIE-1. Appl. Environ. Microbiol. 71, 4487e4496. Kantor, R.S., Kelly, C., Wrighton, K.M., Handley, I.S., Hug, L.A., Castelle, C.J.,
M.P. Reis et al. / Chemosphere 161 (2016) 266e273 Thomas, B.C., Banfield, J.F., 2013. Small genomes and sparse metabolisms of sediment-associated bacteria from four candidate phyla. MBio 4, 708e713. Kato, S., Krepski, S., Chan, C., Itoh, T., Ohkuma, M., 2014. Ferriphaselus amnicola gen. nov., sp. nov., a neutrophilic, stalk-forming, iron-oxidizing bacterium isolated from an iron-rich groundwater seep. Int. J. Syst. Evol. Microbiol. 64, 921e925. Klindworth, A., Pruesse, E., Schweer, T., Peplles, J., Quast, C., et al., 2013. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and nextgeneration sequencing-based diversity studies. Nucleic Acids Res. 41 (1). Li, J., Wang, T., Shao, B., Shen, J., Wang, S., et al., 2012. Plasmid-mediated quinolone resistance genes and antibiotic residues in wastewater and soil adjacent to swine feedlots: potential transfer to agricultural lands. Environ. Health Perspect. 120, 1144e1149. Lovley, D.R., 2000. Fe(III) and Mn(IV) reduction. In: Lovley, D.R. (Ed.), Environmental Microbeemetal Interactions. ASM Press, Washington, pp. 3e30. Lovley, D.R., Baedecker, M.J., Lonergan, D.J., Cozzarelli, I.M., Phillips, E.J.P., Siegel, D.J., 1989. Oxidation of aromatic contaminants coupled to microbial iron reduction. Lett. Nat. 339, 297e299. Lozupone, C.A., Knight, R., 2007. Global patterns in bacterial diversity. Proc. Natl. Acad. Sci. 104, 11436e11440. Meyer, F., Paarmann, D., D’Souza, M., Olson, R., Glass, E.M., Kubal, M., Paczian, T., Rodriguez, A., Stevens, R., Wilke, A., Wilkening, J., Edwards, R.A., 2008. The metagenomics RAST server e a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinforma. 9, 1e8. Mitra, S., Rupek, P., Richter, D.C., Urich, T., Gilbert, J.A., Meyer, F., Wilke, A., Huson, D.H., 2011. Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG. BMC Bioinforma. 12, 1e8. Mortatti, J., Hissler, C., Probst, J.L., 2010. Heavy metal distribution in the bottom ^ river basin. Rev. do Inst. Geocie ^ncias e USP 10, 3e11. sediments along Tiete Muyzer, G., De Waal, E.C., Uitterlinden, A.G., 1993. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microbiol. 59, 695e700. Namiki, T., Hachiya, T., Tanaka, H., Sakakibara, Y., 2012. MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads. Nucleic Acids Res. 1, 155. Odum, E.P., 1985. Trends expected in stressed ecosystems. Bioscience 35, 419e422. Parks, D.H., Beiko, R.G., 2010. Identifying biologically relevant differences between metagenomic communities. Bioinformatics 26, 715e721. €nen, H., Tiirola, M., Jones, R.I., 2012. Distinct Peura, S., Eiler, A., Bertilsson, S., Nyka and diverse anaerobic bacterial communities in boreal lakes dominated by candidate division OD1. ISME J 6, 1640e1652. Picardal, F., Cooper, D.C., 2005. Microbially mediated changes in the mobility of contaminant metals in soils and sediments. In: Ahmad, I., Hayat, S., Pichtel, J. (Eds.), Heavy Metal Contamination in Soil. Science Publishers Inc., Enfield, pp. 43e88. Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., € ckner, F.O., 2013. The SILVA ribosomal RNA gene database project: improved Glo data processing and web-based tools. Nucl. Acids Res. 41, 590e596. Raskin, L., Stromley, J.M., Rittmmann, B.E., Stahl, D.A., 1994. Group-specific 16S rRNA hybridization probes to describe natural communities of methanogens. Appl. Environ. Microbiol. 60, 1232e1240. Reis, M.P., Avila, M.P., Costa, P., Barbosa, F., Laanbroek, H.J., Chartone-Souza, E., Nascimento, A.M.A., 2014. The influence of anthropogenic settlement on the distribution and diversity of iron-oxidizing bacteria belonging to the Gallionellaceae in tropical streams. Front. Microbiol. 5, 1e8. Reis, M.P., Avila, M.P., Keijzer, R.M., Barbosa, F.A.R., Chartone-Souza, E., Nascimento, A.M.A., Laanbroek, H.J., 2015. The effect of human settlement on the abundance and community structure of ammonia oxidizers in tropical stream sediments. Front. Microbiol. 6, 1e11. Reis, M.P., Barbosa, F.A.R., Chartone-Souza, E., Nascimento, A.M.A., 2013. The
273
prokaryotic community of a historically mining-impacted tropical stream sediment is as diverse as that from a pristine stream sediment. Extremophiles 17, 301e309. Rinke, C., Schwientek, P., Sczyrba, A., et al., 2013. Insights into the phylogeny and coding potential of microbial dark matter. Nature 499, 431e437. Roden, E.E., Emerson, D., 2007. Microbial metal cycling in aquatic environments. In: Hurst, C.J., Crawford, R.L., Garland, J.L., Lipson, D.A., Mills, A.L., Stetzenbach, L.D. (Eds.), Manual of Environmental Microbiology. ASM Press, Washington, pp. 540e562. Santos, A.B., Reis, M.P., Costa, P.S., Avila, M.P., Lima-Bittencourt, C.I., Barbosa, F.A.R., Chartone-Souza, E., Nascimento, A.M.A., 2015. Environmental diversity of bacteria in a warm monomictic tropical freshwater lake. Ann. Microbiol. http:// dx.doi.org/10.1007/s13213-015-1048-7. Schloss, P.D., Westcott, S.H., Ryabin, T., Hall, J.R., Hartmann, M., et al., 2009. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537e7541. Sekiguchi, Y., Akiko, O., Donovan, H., Parks, et al., 2015. First genomic insights into members of a candidate bacterial phylum responsible for wastewater bulking. PeerJ 3, e740. Shipunov, A.B., 2015. http://ashipunov.info/shipunov/os/os-en.htm. Systema Naturae v.6.11, 17. Last accessed on May 2, 2016. Singer, E., Emerson, D., Webb, E.A., Barco, R.A., Kuenen, J.G., et al., 2011. Mariprofundus ferrooxydans PV-1 the first genome of a marine Fe(II) oxidizing Zetaproteobacterium. PLOS ONE 6, e25386. € ning, B., Nielsen, P.H., Nielsen, J.L., Baranyi, C., Stoecker, K., Bendinger, B., Scho Toenshoff, E.R., Daims, H., Wagner, M., 2006. Cohn’s Crenothrix is a filamentous methane oxidizer with an unusual methane monooxygenase. Proc. Natl. Acad. Sci. 103, 2363e2367. €sch, P., Popp, J., Wang, J., Sickinger, M., Ciobota, V., Herrmann, M., Rasch, H., Ro Küsel, K., 2014. Revealing the microbial community structure of clogging materials in dewatering wells differing in physic-chemical parameters in an opencast mining area. Water Res. 63, 222e233. Weon, H.Y., Kim, B.Y., Kwon, S.W., Park, I.C., Cha, I.B., Tindall, B.J., Stackebrandt, E., Trüper, H., Go, S.J., 2005. Leadbetterella byssophila gen. nov., sp. nov., isolated from cotton-waste composts for the cultivation of oyster mushroom. Int. J. Syst. Evol. Microbiol. 55, 2297e2302. Wrighton, K.C., Castelle, C.J., Wilkins, M.J., Hug, L.A., Sharon, I., Thomas, B.C., Handley, K.M., Mullin, S.W., Nicora, C.D., Singh, A., Lipton, M.S., Long, P.E., Williams, K.H., Banfield, J.F., 2014. Metabolic interdependencies between phylogenetically novel fermenters and respiratory organisms in an unconfined aquifer. ISME J. 8, 1452e1463. Wrighton, K.C., Thomas, B.C., Sharon, I., Miller, C.S., Castelle, C.J., VerBerkmoes, N.C., Wilkins, M.J., Hettich, R.L., Lipton, M.S., Williams, K.H., Long, P.E., Banfield, J.F., 2012. Fermentation, hydrogen, and sulfur metabolism in multiple uncultivated bacterial phyla. Science 337, 1661e1665. Yamada, T., Sekiguchi, Y., Hanada, S., Imachi, H., Ohashi, A., Harada, H., Kamagata, Y., 2006. Anaerolinea thermolimosa sp. nov., Levilinea saccharolytica gen. nov., sp. nov. and Leptolinea tardivitalis gen. nov., sp. nov., novel filamentous anaerobes, and description of the new classes Anaerolineae classis nov. and Caldilineae classis nov. in the bacterial phylum Chloroflexi. Int. J. Syst. Evol. Microbiol. 56, 1331e1340. Yellishetty, M., Ranjith, P.G., Tharumarajah, A., 2010. Iron ore and steel production trends and material flows in the world: is this really sustainable? Res. Conserv. Recycl 54, 1084e1094. Youssef, N., Steidley, B.L., Elshahed, M.S., 2012. Novel high-rank phylogenetic lineages within a sulfur spring (Zodletone Spring, Oklahoma), revealed using a combined pyrosequencing-sanger approach. Appl. Environ. Microbiol. 78, 2677e2688.
Contents lists available at ScienceDirect
Chemosphere journal homepage: www.elsevier.com/locate/chemosphere
Metagenomic signatures of a tropical mining-impacted stream reveal complex microbial and metabolic networks a Mariana P. Reis a, Marcela F. Dias a, Patrícia S. Costa a, Marcelo P. Avila , Laura R. Leite b, ^ nica Bucciarelli-Rodriguez a, vio M.G. de Araújo b, Anna C.M. Salim b, Mo Fla b, c a M.A. Nascimento a, * Guilherme Oliveira , Edmar Chartone-Souza a, Andre a gicas, Universidade Federal de Minas Gerais, Av. Anto ^nio Carlos 6627, Belo Horizonte, Minas Departamento de Biologia Geral, Instituto de Ci^ encias Biolo Gerais 31270-901, Brazil b ~o Oswaldo Cruz, Av. Augusto de Lima 1715, Belo Horizonte, Minas Gerais 30190-002, Brazil Centro de Pesquisas Ren e Rachou Fundaça c 66055-090, Brazil gico Vale, Rua Boaventura da Silva 955, Bel Instituto Tecnolo em, Para
h i g h l i g h t s The microbiome of a metal-rich tropical stream sediment was studied by metagenomics. The metagenome showed complexity of microbial interaction and metabolic processes. Functional analysis highlighted nitrogen and methane metabolic pathways. Metal stress seems to contribute to distinct life strategies in this environment.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 1 March 2016 Received in revised form 24 June 2016 Accepted 26 June 2016
Bacteria from aquatic ecosystems significantly contribute to biogeochemical cycles, but details of their community structure in tropical mining-impacted environments remain unexplored. In this study, we analyzed a bacterial community from circumneutral-pH tropical stream sediment by 16S rRNA and shotgun deep sequencing. Carrapatos stream sediment, which has been exposed to metal stress due to gold and iron mining (21 [g Fe]/kg), revealed a diverse community, with predominance of Proteobacteria (39.4%), Bacteroidetes (12.2%), and Parcubacteria (11.4%). Among Proteobacteria, the most abundant reads were assigned to neutrophilic iron-oxidizing taxa, such as Gallionella, Sideroxydans, and Mariprofundus, which are involved in Fe cycling and harbor several metal resistance genes. Functional analysis revealed a large number of genes participating in nitrogen and methane metabolic pathways despite the low concentrations of inorganic nitrogen in the Carrapatos stream. Our findings provide important insights into bacterial community interactions in a mining-impacted environment. © 2016 Elsevier Ltd. All rights reserved.
Handling Editor: Shane Snyder Keywords: Shotgun metagenome Iron-rich sediment 16S rRNA Freshwater
1. Introduction Freshwater ecosystems are threatened by a myriad of pollutants, especially those generated by mining activities. Among these, iron ores mining is of great importance since iron is the most mined metal worldwide (Edwards et al., 2004). Brazil is one of the world’s largest producers of iron ores (Yellishetty et al., 2010), which considerably impact Brazilian ecosystems. Iron exists in different oxidation states, but in biological
* Corresponding author. E-mail address: [email protected] (A.M.A. Nascimento). http://dx.doi.org/10.1016/j.chemosphere.2016.06.097 0045-6535/© 2016 Elsevier Ltd. All rights reserved.
systems, it can be found as Fe(II) or Fe(III). Redox cycling of iron in aquatic ecosystems can be closely tied to biogeochemical transformations of other elements, including carbon, nitrogen, and sulfur. In addition, iron cycling is important for pollutant transformation and mobility, meaning great environmental relevance of iron (Ghiorse, 1984; Lovley, 2000; Emerson and Weiss, 2004; Picardal and Cooper, 2005; Roden and Emerson, 2007). Iron redox reactions are often driven by microorganisms. Fereducing bacteria (FeRB), e.g., Geobacter sulfurreducens, couple reduction of Fe(III) with oxidation of organic matter or molecular hydrogen, while Fe-oxidizing bacteria (FeOB), which occur in many phyla (e.g., Nitrospirae, Firmicutes, Actinobacteria, Chlorobi, and
M.P. Reis et al. / Chemosphere 161 (2016) 266e273
especially Proteobacteria), can use Fe(II) or molecular hydrogen as electron donors (Lovley et al., 1989; Jiao et al., 2005; Emerson et al., 2010; Byrne et al., 2015). Recent advances in metagenomic technologies allow for more comprehensive assessment of the microbial biodiversity and functioning in aquatic environments. Sediments are important for metal cycling in these ecosystems because they can retain over 90% of metals and metalloid pollutants and have higher microbial diversity than bulk water (Lozupone and Knight, 2007; Mortatti et al., 2010). To our knowledge, only a few other studies that involved pyrosequencing and denaturing gradient gel electrophoresis have shown the structure of microbial communities in iron-rich environments at circumneutral pH (Gorra et al., 2012; Wang et al., 2014; Emerson et al., 2015). Although FeOB are present under oxic and anoxic conditions, from extremely acidic to circumneutral pH, only a few genera of neutrophilic FeOB have been identified to date, e.g., Gallionella, Sideroxydans, Ferriphaselus, Ferritrophicum, and Mariprofundus (Emerson et al., 2010; Kato et al., 2014). Therefore, ironrich sediments of tropical freshwater streams are not sufficiently explored. In this study, we performed a taxonomical and functional analysis of a microbial community from mining-impacted stream sediment by means of metagenomics and compared our results with different datasets in order to improve the current understanding of the role of bacteria in iron-rich environments. 2. Materials and methods 2.1. The study site The Carrapatos stream (CS; 19 580 15.400 S and 43 270 50.700 W) is located in the Iron Quadrangle (Minas Gerais state, Brazil), a region rich in ores, especially iron ones, explored by mining since the 19th century. One study (Reis et al., 2014) showed that CS can be classified as a mesotrophic, circumneutral (pH 7.9), and mesothermal (17 C) environment. Furthermore, its sediment contains high metal concentrations, including magnesium (1416 mg kg1), manganese (2319 mg kg1) and especially iron (21,850 mg kg1) (Reis et al., 2014). 2.2. DNA extraction A composite sample from CS sediment (100 g) was collected, and DNA was extracted using the PowerSoil DNA Extraction Kit (MoBio Laboratories, USA). Quantification of total DNA was performed using a Qubit fluorometer (InvitrogeneLife Technologies, USA). 2.3. Sequencing and analysis of the V3 and V4 regions of the 16S rRNA gene For taxonomic profiling, primers S-D-Bact-0341-b-S-17 (50 CCTACGGGNGGCWGCAG30 ) and S-D-Bact-0785-a-A-21 (50 GACTACHVGG-GTATCTAATCC30 ) (Klindworth et al., 2013), targeting the V3 and V4 hypervariable regions of the bacterial 16S rRNA gene were used, with Illumina adapters added. The amplification step was performed with the KAPA HiFi HotStart ReadyMix (KAPA, Woburn, MA, USA), followed by purification with AMPure XP beads (Agencourt Bioscience, Beverley, MA, USA). Library construction was performed according to the manufacturer’s instructions for paired-end sequencing on the MiSeq platform (Illumina, Inc., USA). Bioinformatic analysis was carried out in the Mothur software, version 1.33.0 (http://www.mothur.org). Sequences of low quality
267
(Q 20, with ambiguities and homopolymers above eight) or outside the range of 422e466 bp were discarded. Filtered reads were aligned and classified against the Silva v.223 16S rRNA database (Quast et al., 2013). Mitochondrial and chloroplast reads and reads that did not match any reference sequence were discarded. Chimeric reads were identified and removed by means of Uchime (http://drive5.com/uchime). The remaining reads were grouped into operational taxonomic units (OTUs), with 97% similarity, by the average neighbor method. Construction of the rarefaction curve, calculated for the evolutionary distance of 0.03, was conducted according to the Mothur pipeline (Schloss et al., 2009). The nucleotide sequences were deposited to GenBank [GenBank ID: SRR2087753]. 2.4. Shotgun sequencing and analysis To obtain a functional profile of the metagenome, wholecommunity shotgun sequencing from the CS sediment was performed on a SOLiD™ v.4 sequencer (Applied Biosystems), according to the manufacturer’s protocol. Briefly, 10 mg of total DNA was randomly fragmented using the Covaris™ S2 system (Life Technologies, USA). DNA fragment library (200e250 bp long) was then constructed. Emulsion PCR was carried out to clonally amplify fragments on sequencing beads, followed by enrichment, deposition in a plate, and sequencing. The DNA fragment size of the library was verified using the Agilent 2100 Bioanalyzer. Bioinformatic analysis comprised an initial step of error screening in the SOLiD™ Accuracy Enhancer Tool (SAET) software (http://solidsoftwaretools.com/gf) and conversion of sequences from color space to letter space format by means of the encodeFasta.py software (http://gnome.googlecode.com/svn/trunk/ pyGenotypeLearning/src/pytools/encodeFasta.py). Filtered reads were assembled in the Metavelvet software, as described by Namiki et al. (2012). Contigs were analyzed by means of the Metagenomics RAST Server (MG-RAST v3.3) (Meyer et al., 2008), which provides quality control (duplicate removal and size or base quality screening) prior to annotation. Functional analysis was conducted using SEED and KEEG subsystems (available on the MG-RAST Server), with an e-value of 105 and identity of 60% (Mitra et al., 2011; Costa et al., 2015). Sequencing data of the present study are available at MG-RAST (code 4520540.3). The metagenome from the CS sediment was compared with two other metagenomes, one from an arsenic-rich sediment (Mina stream sediment [MSS]: MG-RAST ID 4519449.3) and another from rin [FER]: NCBI a low-metal-concentration freshwater sediment (Fe Bioproject ID 246439), described by Costa et al. (2015) and Gillan et al. (2014), respectively. These metagenomes were chosen for the comparative analysis on the basis of the study region similarity with CS (MSS) and availability of pristine-environment data (FER). To identify possible significant differences with CS in the functional composition of each metagenome, we performed two-sided Fisher’s exact test (Fisher, 1958), with the p value > 0.05, using the Statistical Analysis of Metagenomic Profiles (STAMP; Parks and Beiko, 2010). Metagenomic recruitment plots were used to identify the abundant species in the CS metagenome, by comparing the CS contigs with individual bacterial genomes available in the National Center for Biotechnology Information (NCBI) database, as reported by Costa et al. (2015). 2.5. Quantitative real-time PCR (qPCR) qPCR was used to estimate the absolute number of copies of bacterial 16S rDNA in the sediment. The primers were 338F (50 TACGGGAGGCAGCAG30 ) (Raskin et al., 1994) and 518R
268
M.P. Reis et al. / Chemosphere 161 (2016) 266e273
(50 ATTACCGCGGCTGCTGG30 ) (Muyzer et al., 1993). The procedure was performed on an ABI PRISM 7900HT sequence detection system (Applied Biosystems, Foster City, CA) as described by Reis et al. (2013). Standard curves were generated from seven dilutions of 16S amplicons from Escherichia coli ATCC 25922. A control reaction without template DNA was included in the qPCR assay. Triplicate reactions were run for standards, DNA samples, and negative control. The quantification procedure yielded a slope of 3.23 and R2 value greater than 0.99 (Fig. S1).
3. Results 3.1. An overview of the taxonomic composition and abundance based on 16S rRNA and qPCR data The high-quality reads were grouped into 34,827 OTUs. Table S1 summarizes the quality control dataset from the CS library. The number of reads and taxonomic assignments of these OTUs are presented in Table S2. The rarefaction curve (Fig. S2) and high Good’s coverage value (90%) indicated that the sequencing depth was sufficient to capture overall diversity. qPCR revealed the bacterial concentration of 1.04 108 cells/g in the CS sediment (Fig. S1). The taxonomic data indicated high diversity in CS, spanning a wide spectrum of phyla (Fig. 1 and Table S2) in addition to unclassified bacteria (5.9% of all the reads). Proteobacteria (39.4%), Bacteroidetes (12.2%), and Parcubacteria (candidate division OD1: 11.4%) were the predominant phyla, accounting for 63% of all reads. Other phyla such as Cyanobacteria, Chloroflexi, Acidobacteria, Nitrospirae, Verrucomicrobia, Microgenomates, Candidate division OP3, and Planctomycetes were present in lower proportions, ranging from 2% to 4%. Although Chloroflexi accounted for only 3.5% of all the bacterial reads, this phylum harbored the fourth most abundant OTU (Anaerolineaceae) of the CS metagenome. OTUs with abundance below 2% were grouped into “Other bacteria”, which included Chlamydiae, Actinobacteria, Elusimicrobia, Chlorobi, Spirochaetes, Gemmatimonadetes, Armatimonadetes,
Fig. 1. Taxonomic composition of bacterial taxa from Carrapatos stream (CS) sediment, on the basis of the Silva database. The more representative taxa of each phylum are highlighted in colors. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Saccharibacteria, and Gracilibacteria, representing 8.9% of all reads (Table S2). Proteobacteria comprised broad diversity, with five identified classes: Beta- (44.61%), Delta- (19.8%), Alpha- (17.1%), Gamma(13.1%), and Zetaproteobacteria (2.7%) (Fig. 1). The 50 top proteobacterial OTUs accounted for 48.4% of all the proteobacterial reads, comprising 46% of representatives of various orders or families, such as Gallionellaceae (15.3%), Comamonadaceae (7.5%), Methylococcales (4.7%), Desulfuromonadales (3.7%), Mariprofundaceae (2.5%), Rhodobacteraceae (2.5%), Hyphomonadaceae (2.4%), Rhizobiales (2.3%), Bdellovibrionaceae (1.1%), Sphingomonadaceae (1%), Rhodocyclaceae (0.9%), and Caulobacteraceae (0.5%). The remaining OTUs were classified only at phylum (0.05%) or class levels (Betaproteobacteria, 3.1%). Among the 23 most frequent OTUs (>1000 reads), 10 OTUs were affiliated at the genus level, namely Gallionella, Mariprofundus, Sideroxydans, and Woodsholea (Proteobacteria); Haliscomenobacter, Sediminibacterium, and Leadbetterella (Bacteroidetes); and Nitrospira (Nitrospirae).
3.2. Functional analysis of the metagenome The whole-community shotgun sequencing generated approximately 14.5 Gbp, corresponding to 289,015,844 reads 50 bp long. After MG-RAST quality filtering, 278,993,472 reads remained for further analysis (~13.9 Gbp of reads). Assembly of individual reads by means of Metavelvet resulted in 439,778 contigs, with the length of 157 ± 98 bp (mean ± SD). Functional annotation of the CS metagenome revealed that 48% of the contigs (183,032/380,882) can be classified against protein databases available at MG-RAST. Among these, approximately 73% (133,356 contigs) were assigned to functional categories. The comparison between the CS metagenome and reference genomes showed that the majority of contigs were assigned to Sideroxydans lithotrophicus ES-1 (24,064 contigs) and Gallionella capsiferriformans ES-2 (19,255 contigs), which are neutrophilic FeOB (Fig. 2). Other bacterial species were reasonably well recruited, such as Leptothrix cholodnii, which is also a FeOB, Nitrosospira multiformis ATCC 25196 and Nitrosomonas europaea ATCC 19718, both ammonia-oxidizing bacteria (Fig. S3). A broad range of functional categories was detected by SEED subsystems (Fig. 3). The highest proportions of functional genes were categorized into the clustering-based subsystem (unknown function) and protein metabolism subsystem (10% each), followed by the miscellaneous subsystem (9%); RNA metabolism subsystem (7%); and virulence, disease, and defense subsystem (4%). The latter harbored heavy-metal (and metalloid) resistance genes, mainly cobalt-zinc-cadmium resistance (20%), copper homeostasis (12%), and arsenic resistance (9%). The Kyoto Encyclopedia of Genes and Genomes (KEGG) Mapper tool, available at MG-RAST, produced an integrated view of the CS microbiome metabolism. KEGG pathway analysis revealed that the CS metagenome contigs were distributed among the majority of the metabolic pathways, including 33 of 68 genes involved in nitrogen metabolism and 13 of 34 genes related to methane metabolism (Figs. S4 and S5). The SEED subsystem classification ranked the most abundant functions in the nitrogen metabolism subsystem, such as nitrate and nitrite ammonification, ammonia assimilation, and nitrogen fixation. Functional comparative analysis between CS metagenome and MSS metagenome (arsenic-rich stream; Fig. 4) and between CS metagenome and FER metagenome (low-metal freshwater sediment; Fig. 5) revealed different profiles.
M.P. Reis et al. / Chemosphere 161 (2016) 266e273
269
Fig. 2. Fragment recruitment plots of CS contigs for (A and B) Sideroxydans lithotrophicus ES-1 (CP001965.1) and (C and D) Gallionella capsiferriformans ES-2 (NC_014394.1). The comparison was made using BLASTn. (A and C) Recruitment by means of the R software; the vertical axis shows the percentage of identity of the metagenomic contigs to the respective bacterial genome. (B and D) Recruitment by means of MG-RAST.
4. Discussion The main purpose of this study was to enlighten the connections between microbial functions and community structure of a tropical stream sediment exposed to long-term mining activities. The metagenomic data revealed a diverse bacterial community characterized by a complex metabolic network. Our results are consistent with other studies that showed the dominance of Proteobacteria in communities from metal-rich environments (Horner-Devine et al., 2004; Chen et al., 2008; Reis et al., 2013). Members of FeOB are distributed among a variety of phyla, such as Nitrospirae and Firmicutes, but Proteobacteria, especially all neutrophilic iron-oxidizing Beta- and Zetaproteobacteria, are the most numerous (Hedrich et al., 2011). Noticeably, the assignment of Mariprofundus (iron-oxidizing Zetaproteobacteria), which was detected in our sample, contradicts the findings of Hedrich et al. (2011). Our results do not support the separation of neutrophilic Fe oxidizing Beta- and Zetaproteobacteria by their habitat: freshwater (Betaproteobacteria) versus marine water (Zetaproteobacteria). This marine genus is a representative of a group that has the ability to produce an Fe-oxyhydroxide-encrusted helical stalk:
apparently a requirement for the use of ferrous iron, Fe(II), as an energy source (Singer et al., 2011). The enrichment of the CS community in various neutrophilic FeOB groups points to their importance for iron cycling in this ecosystem. Other studies on the phylogenetic diversity and ecological properties of Gallionella-related FeOB revealed that this group is highly adapted to metal-rich environments because its genome harbors several resistance genes to different metals and metalloid, such as arsenic, mercury, cadmium, cobalt, silver, zinc, and copper (Emerson et al., 2013; Reis et al., 2014). Furthermore, Emerson et al. (2013) reported that both Gallionella and Sideroxydans have a rich genetic repertory for adaptation to stressful conditions, such as environmental sensing, motility, and chemotaxis; this repertory may allow them to survive in this miningimpacted ecosystem. Crenothrix-related OTUs (3566 reads) represented an abundant genus in the CS community. These filamentous FeOB are closely related to methanotrophs known to promote steel corrosion (Coetser and Cloete, 2005; Stoecker et al., 2006). Twentythree OTUs (417 reads) were found to be affiliated with the Ferriphaselus genus, novel stalk-forming neutrophilic FeOB, recently described by Kato et al. (2014).
270
M.P. Reis et al. / Chemosphere 161 (2016) 266e273
Fig. 3. Distribution of SEED subsystems of the Carrapatos stream sediment on the basis of the MG-RAST annotation. The cutoff parameters were as follows: e-value 105 and identity 60%.
Fig. 4. Significant SEED subsystem differences as a result of Fisher’s exact test between the Carrapatos stream (CS) sediment and Mina stream sediment (MSS), conducted in the STAMP software. Enrichment of the CS metagenome with the SEED subsystem has a negative difference between proportions (orange circles), while enrichment of the MSS metagenome with the SEED subsystem has a positive difference between proportions (blue circles). Bars on the left represent the proportion of each subsystem in the data. Subsystem differences with a p value of >0.05 were considered significant. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
The most abundant genera affiliated with Bacteroidetes were Haliscomenobacter, Sediminibacterium, and Leadbetterella, which are not well studied at present. Members of this phylum are widely distributed in nature, especially in aquatic environments (Weon et al., 2005). The Haliscomenobacter genus comprises filamentous
species that inhabit the pelagic zone of natural freshwater lakes and ponds (Hahn and Schauer, 2007; Santos et al., 2015). Sediminibacterium is rarely found in mining-impacted environments although it was previously recovered from metal-rich sediment (Reis et al., 2013). Leadbetterella was originally isolated from cotton-
M.P. Reis et al. / Chemosphere 161 (2016) 266e273
271
Fig. 5. Significant SEED subsystem differences as a result of Fisher’s exact test between the Carrapatos stream (CS) sediment and low-metal-concentration freshwater sediment rin, FER) conducted in the STAMP software. Enrichment of the CS metagenome with the SEED subsystem has a negative difference between proportions (orange circles), while (Fe enrichment of the FER metagenome with the SEED subsystem has a positive difference between proportions (blue circles). Bars on the left represent the proportion of each subsystem in the data. Subsystem differences with a p value of >0.05 were considered significant. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
waste composts (Weon et al., 2005), and the knowledge about its metabolism and ecology is limited. The third most abundant phylum in the CS community is Parcubacteria. Members of this phylum have been identified in lakes, mainly under suboxic conditions, but they were barely studied. High abundance of this phylum was reported only in a few studies e et al., 2007; Peura et al., 2012), including the present one. (Brie According to Peura et al. (2012), this finding could be explained by insufficient coverage of Parcubacteria members in previous bacterial PCR surveys, due to the use of general bacterial primers, which do not target this group. The primers used by Peura et al. (2012) and in our study span variable regions V3 and V4 of the 16S rRNA gene, thereby promoting much greater coverage of known sequences of Parcubacteria. Recently, Parcubacteria (OD1), Microgenomates (OP11), Saccharibacteria (GN02), and Gracilibacteria (TM7) phyla, represented by 15% of all the reads here, were proposed to form a new superphylum called Patescibacteria (Rinke et al., 2013; Sekiguchi et al., 2015; Shipunov, 2015). Genomic and metagenomic studies showed that members of this superphylum have reduced metabolic capabilities, which likely limit their cultivation (Kantor et al., 2013; Rinke et al., 2013; Brown et al., 2015). They are also involved in hydrogen production, sulfur cycling (Wrighton et al., 2012, 2014), and anaerobic methane oxidation (Peura et al., 2012). Because of the high proportion of members of this superphylum and genes related to the methane oxidation pathway, it is possible that these bacteria are responsible for methane oxidation in the CS sediment. Anaerolineaceae-related reads represented a majority in the Chloroflexi phylum (77.6% of all the Chloroflexi reads). This phylum was previously identified as green nonsulfur bacteria and includes relatively unstudied organisms with diversified metabolism (Hug et al., 2013). The Anaerolineaceae family was formally proposed in 2006 and comprises heterotrophic and anaerobic bacteria inhabiting many environments, including sulfur springs and sulfide-rich anaerobic reactors (Yamada et al., 2006; Youssef et al.,
2012; Dias et al., 2016). It is still unclear whether these bacteria can use sulfur compounds in their metabolic reactions (Youssef et al., 2012), but the presence of this taxon, in addition to other OTUs related to bacteria involved in sulfur cycling, e.g., Thiotrichales and Desulfuromonadales, may be an indication of sulfur compound transformations coupled to iron redox reactions in the CS sediment. The functional analysis of the CS metagenome revealed dominance of clustering-based and protein metabolism subsystems, whose genes are thought to be related and linked functionally (Hu et al., 2010). Hence it is quite difficult to comment on the genes assigned to these subsystems or to speculate on their roles. We detected high abundance of contigs related to protein metabolism, including biosynthesis, degradation, folding, processing, and modification of proteins. This result reflects the high diversity, abundance, and metabolic activity of microorganisms in the CS sediment. Although the Carrapatos freshwater sample showed low concentrations of inorganic forms of N, in contrast to the arsenic-rich freshwater (MSS) analyzed by Costa et al. (2015), the CS metagenome over-represents the nitrogen metabolic pathway. Reads related to N2 fixation were obtained here, possibly pointing to an activity of Sideroxydans, neutrophilic FeOB that possess three clusters of nif genes. This feature may allow these bacteria to occupy niches depleted of nitrogen and may confer a competitive advantage over other organisms. Furthermore, although ammoniaoxidizing bacteria were well represented in the shotgun results, only a few reads were detected in the 16S rRNA dataset (222/ 229,995 reads). This discrepant result may be due to the fact that shotgun sequencing does not depend on PCR amplification; thus, it is not affected by primer bias. Additionally, in a previous study on ammonia oxidizing bacteria in tropical stream sediments by Reis et al. (2015), the number of copies of the amoA gene on the same CS sediment sample was determined. Environments that have high Fe concentrations may also have relatively high concentrations of other metals and metalloids,
272
M.P. Reis et al. / Chemosphere 161 (2016) 266e273
including toxic ones (Gibbs, 1977). The resistance to antibiotics and toxic compounds was highlighted in the category of virulence, disease, and defense. Indeed, this resistance ensures survival of the microbial community in the contaminated environment of the CS sediments. Fluoroquinolones and methicillin are among the antibiotics whose resistance genes were detected in the CS sediment. These genes are common in environments that receive human and animal waste as well as heavy metals and metalloid (e.g., cobalt, zinc, cadmium, copper, and arsenic) (Borr as et al., 2012; Li et al., 2012). Probably, antibiotic resistance and heavy-metal resistance genes were coselected, thus contributing to survival in this sediment. Emerson et al. (2013) reported genes responsible for arsenic resistance and genes that encode a heavy-metal efflux pump of the cation diffusion facilitator (CDF) family. This efflux pump is responsible for removal of structurally unrelated toxic metals like cadmium, cobalt, silver, zinc, and copper from the cytoplasm. These genes were found in the genomes of Sideroxydans lithotrophicus and Gallionella capsiferriformans. Functional comparisons between the CS and MSS metagenomes and between the FER and CS metagenomes revealed that the functional categories recovered from the CS metagenome were more similar to those from the FER metagenome than to functional categories from the MSS metagenome. This finding suggests that the CS sediment harbors a metabolic network close to that of the reference sediment. Moreover, these comparisons unveiled overrepresentation of the virulence, disease, and defense subsystem (MSS). Our results also showed overrepresentation of stress response subsystems and subsystems of phages, prophages, transposable elements, plasmids (CS), all which contribute to survival of the microbial community in mining-impacted environments. The diverse, abundant, and functionally complex bacterial community in the CS sediment thus represents a well-adapted community. This finding contradicts the ecological models that predict that community diversity decreases in response to stressors associated with toxic compounds (Odum, 1985). This apparent discrepancy may be due to the historical contamination of the Iron Quadrangle region. Indeed, other studies suggest that the composition and stability of bacterial communities in historically metalcontaminated sediments become diverse after approximately one century of exposition (Bouskill et al., 2010; Reis et al., 2013).
5. Conclusion Both shotgun data and 16S analysis point to a diverse community in CS sediment, where many metabolic processes take place, as confirmed by detection of genes related to iron, methane, nitrogen, and sulfur pathways. Moreover, our data highlight the occurrence of rare taxa, like Leadbetterella, and to our knowledge, unprecedented findings about Woodsholea and Mariprofundus, previously recovered from marine environments. These results provide insights into the interactions and metabolic abilities of bacteria in mining-impacted sediments and may contribute to elucidate the role of bacteria in environments exposed to long-term metal stress.
Acknowledgments We are grateful to Dr. Roscoe Stanyon for his valuable contririo de Ana lises Químicas/DEMET/UFMG of butions, to the Laborato ^ncia e Tecnologia em Recursos Minthe Instituto Nacional de Cie erais, Agua e Biodiversidade (INCTeACQUA) for technical support, and to the funding agencies CNPq nu472411/ 2012-8, CNPq/INCT no 15206-7, and FAPEMIG APQ 00801/12 and APQ-00463-13.
Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.chemosphere.2016.06.097. References , R., Prat, M.D., 2012. Analysis of fluoBorr as, S., Ríos-Kristj ansson, J.G., Companyo roquinolones in animal feeds by liquid chromatography with fluorescence detection. J. Sep. Sci. 35, 2048e2053. Bouskill, N.J., Barker-Finkel, J., Galloway, T.S., Handy, R.D., Ford, T.E., 2010. Temporal bacterial diversity associated with metal-contaminated river sediments. Ecotoxicology 19, 317e328. pez-Gracía, P., 2007. Archaeal and bacterial community Briee, C., Moreira, D., Lo composition of sediment and plankton from a suboxic freshwater pond. Res. Microbiol. 158, 213e227. Brown, C., Hug, L.A., Thomas, B.C., Sharon, I., Castelle, C.J., Singh, A., Wilkins, M.J., Wrighton, K.C., Williams, K.H., Banfield, J.F., 2015. Unusual biology across a group comprising more than 15% of domain Bacteria. Nature 523, 208e211. Byrne, J.M., Klueglein, N., Pearce, C., Rosso, K.M., Appel, E., Kappler, A., 2015. Redox cycling of Fe(II) and Fe(III) in magnetite by Fe-metabolizing bacteria. Science 347, 1473e1476. Chen, Y.Q., Ren, G.J., An, S.Q., Sun, Q.Y., Liu, C.H., Shuang, J.L., 2008. Changes in bacterial community structure in copper mine tailings after colonisation of reed (Phragmites communis). Pedosphere 18, 731e740. Coetser, S.E., Cloete, T.E., 2005. Biofouling and biocorrosion in industrial water systems. Crit. Rev. Microbiol. 31, 213e232. Costa, P.S., Reis, M.P., Avila, M.P., Leite, L.R., Araújo, F.M.G., Salim, A.C.M., Oliveira, G., Barbosa, F., Chartone-Souza, E., Nascimento, A.M.A., 2015. Metagenome of a microbial community inhabiting a metal-rich tropical stream sediment. PLOS ONE. http://dx.doi.org/10.1371/journal.pone.0119465. Dias, M.F., Colturato, L.F., Oliveira, J.P., Leite, L.R., Oliveira, G., Chernicharo, C.A., Araújo, J.C., 2016. Metagenomic analysis of a desulphurisation system used to treat biogas from vinasse methanisation. Biores. Technol. 205, 58e66. Edwards, K.J., Bach, W., McCollom, T.M., Rogers, D.R., 2004. Neutrophilic ironoxidizing bacteria in the ocean: their habitat, diversity, and roles in mineral deposition, rock alteration, and biomass production in the deep-sea. Geomicrobiol. J. 21, 393e404. Emerson, D., Field, E.K., Chertkov, O., Davenport, K.W., Goodwin, L., Munk, C., Nolan, M., Woyke, T., 2013. Comparative genomics of freshwater Fe-oxidizing bacteria: implications for physiology, ecology, and systematics. Front. Microbiol. 4, 1e17. Emerson, D., Scott, J.J., Benes, J., Bowden, W.B., 2015. Microbial iron oxidation in the Arctic tundra and its implications for biogeochemical cycling. Appl. Environ. Microbiol. 81, 8066e8075. Emerson, D., Weiss, J.V., 2004. Bacterial iron oxidation in circumneutral freshwater habitats: findings from the field and the laboratory. Geomicrobiol. J. 21, 405e414. Emerson, D., Fleming, E.J., McBeth, J.M., 2010. Iron-oxidizing bacteria: an environmental and genomic perspective. Annu. Rev. Microbiol. 64, 561e583. Fisher, W.D., 1958. On grouping for maximum homogeneity. J. Am. Stat. Assoc. 53, 789e798. Ghiorse, W.C., 1984. Biology of iron-and manganese-depositing bacteria. Ann. Rev. Microbiol. 38, 515e550. Gibbs, R.J., 1977. Transport phases of transition metals in the Amazon and Yukon rivers. Soc. Amer. J. 59, 1773e1781. Gillan, D.C., Roosa, S., Kunath, B., Billon, G., Wattiez, R., 2014. The long-term adaptation of bacterial communities in metal-contaminated sediments: a metaproteogenomic study. Environ. Microbiol. http://dx.doi.org/10.1111/14622920.12627. Gorra, R., Webster, G., Martin, M., Celi, L., Mapelli, F., Weightman, A.J., 2012. Dynamic microbial community associated with iron-arsenic co-precipitation products from a groundwater storage system in Bangladesh. Microb. Ecol. 64, 171e186. Hahn, M.W., Schauer, M., 2007. ‘Candidatus Aquirestis calciphila’ and ‘Candidatus Haliscomenobacter calcifugiens’, filamentous, planktonic bacteria inhabiting natural lakes. Int. J. Syst. Evol. Microbiol. 57, 936e940. €mann, M., Johnson, D.B., 2011. The iron-oxidizing proteobacteria. Hedrich, S., Schlo Microbiology 157, 1551e1564. Horner-Devine, M.C., Carney, K.M., Bohannan, B.J.M., 2004. An ecological perspective on bacterial biodiversity. Proc. R. Soc. Lond 271, 113e122. Hu, Y., Fu, C., Yin, Y., Cheng, G., Lei, F., Yang, X., Li, J., Ashforth, E.J., Zhang, L., Zhu, B., 2010. Construction and preliminary analysis of a deep-sea sediment metagenomic fosmid library from Qiongdongnan basin, South China sea. Mar. Biotechnol. 12, 719e727. Hug, L.A., Castelle, C.J., Wrighton, K.C., Thomas, B.C., Sharon, I., Frischkorn, K.R., et al., 2013. Community genomic analyses constrain the distribution of metabolic traits across the Chloroflexi phylum and indicate roles in sediment carbon cycling. Microbiome 1, 1e13. Jiao, Y., Kappler, A., Croal, L.R., Newman, D.K., 2005. Isolation and characterization of a genetically tractable photoautotrophic Fe(II)-oxidizing bacterium, Rhodopseudomonas palustris strain TIE-1. Appl. Environ. Microbiol. 71, 4487e4496. Kantor, R.S., Kelly, C., Wrighton, K.M., Handley, I.S., Hug, L.A., Castelle, C.J.,
M.P. Reis et al. / Chemosphere 161 (2016) 266e273 Thomas, B.C., Banfield, J.F., 2013. Small genomes and sparse metabolisms of sediment-associated bacteria from four candidate phyla. MBio 4, 708e713. Kato, S., Krepski, S., Chan, C., Itoh, T., Ohkuma, M., 2014. Ferriphaselus amnicola gen. nov., sp. nov., a neutrophilic, stalk-forming, iron-oxidizing bacterium isolated from an iron-rich groundwater seep. Int. J. Syst. Evol. Microbiol. 64, 921e925. Klindworth, A., Pruesse, E., Schweer, T., Peplles, J., Quast, C., et al., 2013. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and nextgeneration sequencing-based diversity studies. Nucleic Acids Res. 41 (1). Li, J., Wang, T., Shao, B., Shen, J., Wang, S., et al., 2012. Plasmid-mediated quinolone resistance genes and antibiotic residues in wastewater and soil adjacent to swine feedlots: potential transfer to agricultural lands. Environ. Health Perspect. 120, 1144e1149. Lovley, D.R., 2000. Fe(III) and Mn(IV) reduction. In: Lovley, D.R. (Ed.), Environmental Microbeemetal Interactions. ASM Press, Washington, pp. 3e30. Lovley, D.R., Baedecker, M.J., Lonergan, D.J., Cozzarelli, I.M., Phillips, E.J.P., Siegel, D.J., 1989. Oxidation of aromatic contaminants coupled to microbial iron reduction. Lett. Nat. 339, 297e299. Lozupone, C.A., Knight, R., 2007. Global patterns in bacterial diversity. Proc. Natl. Acad. Sci. 104, 11436e11440. Meyer, F., Paarmann, D., D’Souza, M., Olson, R., Glass, E.M., Kubal, M., Paczian, T., Rodriguez, A., Stevens, R., Wilke, A., Wilkening, J., Edwards, R.A., 2008. The metagenomics RAST server e a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinforma. 9, 1e8. Mitra, S., Rupek, P., Richter, D.C., Urich, T., Gilbert, J.A., Meyer, F., Wilke, A., Huson, D.H., 2011. Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG. BMC Bioinforma. 12, 1e8. Mortatti, J., Hissler, C., Probst, J.L., 2010. Heavy metal distribution in the bottom ^ river basin. Rev. do Inst. Geocie ^ncias e USP 10, 3e11. sediments along Tiete Muyzer, G., De Waal, E.C., Uitterlinden, A.G., 1993. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microbiol. 59, 695e700. Namiki, T., Hachiya, T., Tanaka, H., Sakakibara, Y., 2012. MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads. Nucleic Acids Res. 1, 155. Odum, E.P., 1985. Trends expected in stressed ecosystems. Bioscience 35, 419e422. Parks, D.H., Beiko, R.G., 2010. Identifying biologically relevant differences between metagenomic communities. Bioinformatics 26, 715e721. €nen, H., Tiirola, M., Jones, R.I., 2012. Distinct Peura, S., Eiler, A., Bertilsson, S., Nyka and diverse anaerobic bacterial communities in boreal lakes dominated by candidate division OD1. ISME J 6, 1640e1652. Picardal, F., Cooper, D.C., 2005. Microbially mediated changes in the mobility of contaminant metals in soils and sediments. In: Ahmad, I., Hayat, S., Pichtel, J. (Eds.), Heavy Metal Contamination in Soil. Science Publishers Inc., Enfield, pp. 43e88. Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., € ckner, F.O., 2013. The SILVA ribosomal RNA gene database project: improved Glo data processing and web-based tools. Nucl. Acids Res. 41, 590e596. Raskin, L., Stromley, J.M., Rittmmann, B.E., Stahl, D.A., 1994. Group-specific 16S rRNA hybridization probes to describe natural communities of methanogens. Appl. Environ. Microbiol. 60, 1232e1240. Reis, M.P., Avila, M.P., Costa, P., Barbosa, F., Laanbroek, H.J., Chartone-Souza, E., Nascimento, A.M.A., 2014. The influence of anthropogenic settlement on the distribution and diversity of iron-oxidizing bacteria belonging to the Gallionellaceae in tropical streams. Front. Microbiol. 5, 1e8. Reis, M.P., Avila, M.P., Keijzer, R.M., Barbosa, F.A.R., Chartone-Souza, E., Nascimento, A.M.A., Laanbroek, H.J., 2015. The effect of human settlement on the abundance and community structure of ammonia oxidizers in tropical stream sediments. Front. Microbiol. 6, 1e11. Reis, M.P., Barbosa, F.A.R., Chartone-Souza, E., Nascimento, A.M.A., 2013. The
273
prokaryotic community of a historically mining-impacted tropical stream sediment is as diverse as that from a pristine stream sediment. Extremophiles 17, 301e309. Rinke, C., Schwientek, P., Sczyrba, A., et al., 2013. Insights into the phylogeny and coding potential of microbial dark matter. Nature 499, 431e437. Roden, E.E., Emerson, D., 2007. Microbial metal cycling in aquatic environments. In: Hurst, C.J., Crawford, R.L., Garland, J.L., Lipson, D.A., Mills, A.L., Stetzenbach, L.D. (Eds.), Manual of Environmental Microbiology. ASM Press, Washington, pp. 540e562. Santos, A.B., Reis, M.P., Costa, P.S., Avila, M.P., Lima-Bittencourt, C.I., Barbosa, F.A.R., Chartone-Souza, E., Nascimento, A.M.A., 2015. Environmental diversity of bacteria in a warm monomictic tropical freshwater lake. Ann. Microbiol. http:// dx.doi.org/10.1007/s13213-015-1048-7. Schloss, P.D., Westcott, S.H., Ryabin, T., Hall, J.R., Hartmann, M., et al., 2009. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537e7541. Sekiguchi, Y., Akiko, O., Donovan, H., Parks, et al., 2015. First genomic insights into members of a candidate bacterial phylum responsible for wastewater bulking. PeerJ 3, e740. Shipunov, A.B., 2015. http://ashipunov.info/shipunov/os/os-en.htm. Systema Naturae v.6.11, 17. Last accessed on May 2, 2016. Singer, E., Emerson, D., Webb, E.A., Barco, R.A., Kuenen, J.G., et al., 2011. Mariprofundus ferrooxydans PV-1 the first genome of a marine Fe(II) oxidizing Zetaproteobacterium. PLOS ONE 6, e25386. € ning, B., Nielsen, P.H., Nielsen, J.L., Baranyi, C., Stoecker, K., Bendinger, B., Scho Toenshoff, E.R., Daims, H., Wagner, M., 2006. Cohn’s Crenothrix is a filamentous methane oxidizer with an unusual methane monooxygenase. Proc. Natl. Acad. Sci. 103, 2363e2367. €sch, P., Popp, J., Wang, J., Sickinger, M., Ciobota, V., Herrmann, M., Rasch, H., Ro Küsel, K., 2014. Revealing the microbial community structure of clogging materials in dewatering wells differing in physic-chemical parameters in an opencast mining area. Water Res. 63, 222e233. Weon, H.Y., Kim, B.Y., Kwon, S.W., Park, I.C., Cha, I.B., Tindall, B.J., Stackebrandt, E., Trüper, H., Go, S.J., 2005. Leadbetterella byssophila gen. nov., sp. nov., isolated from cotton-waste composts for the cultivation of oyster mushroom. Int. J. Syst. Evol. Microbiol. 55, 2297e2302. Wrighton, K.C., Castelle, C.J., Wilkins, M.J., Hug, L.A., Sharon, I., Thomas, B.C., Handley, K.M., Mullin, S.W., Nicora, C.D., Singh, A., Lipton, M.S., Long, P.E., Williams, K.H., Banfield, J.F., 2014. Metabolic interdependencies between phylogenetically novel fermenters and respiratory organisms in an unconfined aquifer. ISME J. 8, 1452e1463. Wrighton, K.C., Thomas, B.C., Sharon, I., Miller, C.S., Castelle, C.J., VerBerkmoes, N.C., Wilkins, M.J., Hettich, R.L., Lipton, M.S., Williams, K.H., Long, P.E., Banfield, J.F., 2012. Fermentation, hydrogen, and sulfur metabolism in multiple uncultivated bacterial phyla. Science 337, 1661e1665. Yamada, T., Sekiguchi, Y., Hanada, S., Imachi, H., Ohashi, A., Harada, H., Kamagata, Y., 2006. Anaerolinea thermolimosa sp. nov., Levilinea saccharolytica gen. nov., sp. nov. and Leptolinea tardivitalis gen. nov., sp. nov., novel filamentous anaerobes, and description of the new classes Anaerolineae classis nov. and Caldilineae classis nov. in the bacterial phylum Chloroflexi. Int. J. Syst. Evol. Microbiol. 56, 1331e1340. Yellishetty, M., Ranjith, P.G., Tharumarajah, A., 2010. Iron ore and steel production trends and material flows in the world: is this really sustainable? Res. Conserv. Recycl 54, 1084e1094. Youssef, N., Steidley, B.L., Elshahed, M.S., 2012. Novel high-rank phylogenetic lineages within a sulfur spring (Zodletone Spring, Oklahoma), revealed using a combined pyrosequencing-sanger approach. Appl. Environ. Microbiol. 78, 2677e2688.