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Environmental Microbiology (2015) 17(5), 1574–1585

doi:10.1111/1462-2920.12582

Expanding our view of genomic diversity in Candidatus Accumulibacter clades Connor T. Skennerton,1,2† Jeremy J. Barr,2‡ Frances R. Slater,2 Philip L. Bond2 and Gene W. Tyson1,2* 1 Australian Centre for Ecogenomics, School of Chemistry and Molecular Bioscience, and 2 Advanced Water Management Centre, University of Queensland, St Lucia, QLD 4072, Australia. Summary Enhanced biological phosphorus removal (EBPR) is an important industrial wastewater treatment process mediated by polyphosphate-accumulating organisms (PAOs). Members of the genus Candidatus Accumulibacter are one of the most extensively studied PAO as they are commonly enriched in lab-scale EBPR reactors. Members of different Accumulibacter clades are often enriched through changes in reactor process conditions; however, the two currently sequenced Accumulibacter genomes show extensive metabolic similarity. Here, we expand our understanding of Accumulibacter genomic diversity through recovery of eight population genomes using deep metagenomics, including seven from phylogenetic clades with no previously sequenced representative. Comparative genomic analysis revealed a core of shared genes involved primarily in carbon and phosphorus metabolism; however, each Accumulibacter genome also encoded a substantial number of unique genes (> 700 genes). A major difference between the Accumulibacter clades was the type of nitrate reductase encoded and the capacity to perform subsequent steps in denitrification. The Accumulibacter clade IIF genomes also contained acetaldehyde dehydrogenase that may allow ethanol to be used as carbon source. These differences in metabolism between Accumulibacter genomes provide a molecular basis for niche differentiation observed in lab-scale reactors and may offer new opportunities for process optimization. Received 11 March, 2014; revised 20 July, 2014; accepted 27 July, 2014. *For correspondence. Email [email protected]; Tel. +61 7 336 53829; Fax +61 7 336 54511. Present address: †Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA; ‡Department of Biology, San Diego State University, San Diego, CA 92182, USA.

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd

Introduction Enhanced biological phosphorus removal (EBPR) is a widely used process for the efficient removal of phosphorus during wastewater treatment. EBPR is mediated by diverse groups of microorganisms (Hesselmann et al., 1999; Kong et al., 2005; Nguyen et al., 2012; Kristiansen et al., 2013) known as polyphosphateaccumulating organisms (PAOs), which assimilate large amounts of phosphate that are stored intracellularly as polyphosphate. One of the first PAOs identified belonged to the Betaproteobacteria family Rhodocyclacae and was subsequently named Candidatus Accumulibacter (Bond et al., 1995; Hesselmann et al., 1999) (hereafter referred to as Accumulibacter). Accumulibacter, and other PAOs, use internal storage compounds to survive in conditions where their electron acceptor and electron donor are not present at the same time. During oxygenlimited, carbon-rich conditions, Accumulibacter take up volatile fatty acids (VFA) and store this carbon as polyhydroxyalkanoates (PHAs) using the energy produced from the hydrolysis of intracellular polyphosphate and the reducing power from either the TCA cycle or glycolysis of intracellular glycogen (Oehmen et al., 2007; Wexler et al., 2009; Zhou et al., 2009). Under aerobic, carbon-limited conditions, PHA is degraded to support Accumulibacter growth and supports the regeneration of polyphosphate using phosphate from the environment (Oehmen et al., 2007). The phylogeny of Accumulibacter has been resolved to the strain level by sequencing the polyphosphate kinase gene (ppk1), which groups Accumulibacter into two major divisions (type I and II) each with multiple subdivisions (type IA–IE, IIA–IIF) (He et al., 2007; McMahon et al., 2007; Peterson et al., 2008). Currently there are two Accumulibacter genomes available, designated Accumulibacter UW-1 (Garcia Martin et al., 2006) and Accumulibacter UW-2 (Flowers et al., 2013), which are members of types IIA and IA respectively. The metabolism of Accumulibacter inferred from reactor process data has largely been supported by in silico analysis of these two genomes and subsequent meta’omic analyses; however, there are some discrepancies relating to carbon and nitrogen metabolism (Garcia Martin et al., 2006; Wilmes et al., 2008; Wexler et al., 2009; He et al., 2010). For example, initial nuclear magnetic resonance studies suggest that

Metabolic plasticity in Accumulibacter clades the Entner–Doudoroff (ED) pathway is used for glycolysis (Maurer et al., 1997; Hesselmann et al., 2000); however, both Accumulibacter UW-1 and UW-2 only encode the Embden–Meyerhof–Parnas (EMP) pathway. Reactor process data also suggest that type IA Accumulibacter are capable of using nitrate as the terminal electron acceptor, whereas type IIA cannot (Flowers et al., 2009; Oehmen et al., 2010; Kim et al., 2013). However, genomic analysis of Accumulibacter UW-1 and UW-2 revealed identical complements of nitrate reduction genes, suggesting that respiratory nitrate reduction may be transcriptionally controlled (Flowers et al., 2013). Respiratory nitrate reduction has also been observed in reactors enriched with Accumulibacter type IIC (Kim et al., 2013); however, there is no currently sequenced genome available for this clade. Here, we set out to expand our understanding of Accumulibacter diversity by obtaining genome representatives of novel ppk1 types to determine the genomic foundation for phenotypic differences observed in the reactor process data. Metagenomic sequencing of five lab-scale EBPR reactors was used to recover eight Accumulibacter population genomes from four ppk1 types (IA, IC, IIC, IIF). Our results demonstrate that the carbon and phosphorus metabolism required for the PAO phenotype is conserved among all sequenced Accumulibacter clades. However, Accumulibacter contain variations in their nitrogen metabolism and some carbon metabolic pathways, which may form the basis for niche differentiation in wastewater treatment systems. Results and discussion Reactor operation and sampling Over a period of 3.5 years, four floccular and one granular lab-scale-sequencing batch reactors (SBRs) were operated for EBPR (hereafter referred to as SBR1–SBR5) for up to 22 months (see Methods; Fig. S1). It has previously been hypothesized that Accumulibacter IIC has a higher affinity for phosphate compared with Accumulibacter IA, as this clade dominated in SBRs where there was increased competition for phosphate from flanking populations (Slater et al., 2010). We hypothesized that reducing the concentration of nutrients could replicate these conditions and select for Accumulibacter type IIC. The floccular reactors were operated with variable rates of nutrient feeding; SBR3 and SBR5 were fed at one-tenth the rate of SBR1 and SBR4 to reduce the concentration of nutrients. A total of 13 samples were chosen for metagenomic sequencing to facilitate accurate binning of metagenomic contigs using differential coverage. SBR2 and SBR3 were sampled at a single time points, while SBR1, SBR4 and SBR5 were sampled at two, six and three time points respectively (Table S1; Fig. S1).

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Sequencing of these 13 samples using Illumina HiSeq2000 resulted in ∼ 226 Gbp of metagenomic sequence data. All samples were assembled and ∼ 300 contig bins were identified using GROOPM (Imelfort et al., 2014) or tetranucleotide frequency-based ESOMs (Dick et al., 2009). Accumulibacter genome phylogeny The ppk1 gene was used as a marker to identify Accumulibacter genome bins. Eight genome bins with high sequence similarity to Accumulibacter ppk1 genes were identified and subsequently classified as representatives of type IIC (strains BA-91, SK-01, SK-02), IIF (strains SK-11, SK-12, BA-94), IA (strain BA-93) and IC (strain BA-92) (Fig. S2). While some genome bins contained partial 16S rRNA genes, which verified their affiliation with Accumulibacter, most bins did not contain this gene. A genome tree constructed from a concatenated protein alignment of 38 universally distributed single-copy marker genes (Wu et al., 2013) confirmed that these genomes belonged to Accumulibacter (Fig. S3). In order to accurately estimate completeness and fidelity of the Accumulibacter genome bins, a set of 427 protein families found in a single copy and common to Betaproteobacteria were used to provide a robust estimation of completeness and contamination. The genome bins contained between 75% and 99% of these single-copy genes with minimal gene duplication (1–8%) (Table 1). Single nucleotide polymorphisms (SNP) in the genome bins were low in most cases; however, some genes contained significantly higher SNP frequencies (Fig. S4). Accumulibacter BA-94, SK-01 and SK-02 contained higher SNP frequencies overall (mean 10 per kbp of gene length) suggesting that they are composite genomes of a number of different strains (Fig. S4). Average nucleotide sequence identity (ANI) between Accumulibacter genomes was used to determine whether they represented distinct species as this method has been shown to correlate well with previously defined 16S rRNA gene species boundaries (Konstantinidis and Tiedje, 2005a,b). The calculated ANI for the Accumulibacter genomes (including the previously sequenced UW-1 and UW-2 genomes) showed that they are likely representatives of different species (ANI < 94%; Konstantinidis and Tiedje, 2005a), with the exception of the two type IA genomes (ANI = 99.0%; Fig. 1A). The clade boundaries defined using ppk1 broadly agree with the ANI results, as genomes from the same ppk1 type share a higher ANI and have a greater shared complement of genes (Figs 1A and 2). Furthermore, the topologies of the ppk1 and genome trees are in broad agreement, with genomes from the same ppk1 type found to be monophyletic with each other in the genome tree (Figs S2 and S3). These

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 1574–1585

1576 C. T. Skennerton et al. Table 1. Summary statistics for draft Accumulibacter genomes. Genome name

ppk1 Type

Completeness (%)

Contamination (%)

Number of scaffolds

Genome size (Mbp)

NCBI accession

SK-11 SK-12 BA-94 BA-91 SK-01 SK-02 BA-93 BA-92

IIF IIF IIF IIC IIC IIC IA IC

76.18 93.12 75.82 74.41 94.26 97.17 99.53 92.74

0.56 0.52 8.25 1.29 1.81 2.23 0.97 1.42

94 61 299 737 53 155 85 218

4.70 4.44 3.09 4.32 4.53 4.35 4.62 4.94

JFAW00000000 JFAX00000000 JEMZ00000000 JDVG00000000 JDSS00000000 JDST00000000 JEMY00000000 JEMX00000000

Completeness and contamination estimates were based on 427 single-copy genes (SCGs) common to all Betaproteobacteria. Completeness was determined by the number of SCGs present in each assembled population genome; contamination was determined by the number of SCGs that were present in more than a single copy.

using the fast-feed floccular conditions (Fig. S1) but seeded at different times (Fig. S1) and enriched type IA or IIF respectively. Similarly, SBR3 and SBR5 were also seeded at different times, operated using the slow-feed floccular conditions and enriched with type IIC or IIF respectively (Table S1). Conversely, SBR4 and SBR5 were inoculated with the same source-activated sludge, but run under different operational conditions, yet still enriched for type IIF Accumulibacter. SBR4 was initially enriched in both Accumulibacter SK-11 and SK-12; however, during the course of operation the Accumulibacter SK-11, population decreased (Fig. S5).

results support the use of ppk1 as a robust marker gene for Accumulibacter phylogeny. It is hypothesized that varying reactor operational conditions favor different Accumulibacter clades due to differences in their metabolic properties (Carvalho et al., 2007; Flowers et al., 2009; Oehmen et al., 2010; Slater et al., 2010; Gonzalez-Gil and Holliger, 2011; Acevedo et al., 2012; Kim et al., 2013). However, changes in nutrient feed rate did not appear to select for different Accumulibacter clades; instead, the inoculum appeared to be the major factor influencing the Accumulibacter enriched in each reactor. For example, SBR1 and SBR4 were operated

A

B

SK-02 (IIC) SK-01 (IIC) BA-91 (IIC) SK-12 (IIF) SK-11 (IIF) BA-94 (IIF) UW-1 (IIA) UW-2 (IA) BA-93 (IA) ALDH

NIF

NOS

NOR

NIR

NAP

NAR

SK-02

SK-01

BA-91

0.96

0.92

0.88

0.84

Percent Identity 0.80

0.64

0.48

0.32

0.16

Percent Alignment

SK-12

SK-11

BA-94

UW-1

WU-2

BA-93

BA-92

BA-92 (IC)

Fig. 1. Comparison of the average nucleotide identity and variable metabolic properties for the Accumulibacter genomes. The heatmaps are ordered phylogenetically based on the genome tree (Fig. S2). A. Heatmap showing the average nucleotide identity (upper diagonal) and the percentage of the genomes that aligned (lower diagonal). B. Presence (dark grey) of different gene/operons that were variable between the Accumulibacter genomes. NAR: respiratory nitrate reductase; NAP: periplasmic nitrate reductase; NIR: nitrite reductase; NOR: nitric oxide reductase; NOS: nitrous oxide reductase; NIF: nitrogenase; ALDH: acetaldehyde dehydrogenase.

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 1574–1585

Metabolic plasticity in Accumulibacter clades

A

B

C

SK-01

SK-02 860

1483 3160

1364

SK-12 892

773 820

SK-11

1914

1278

2369 1324

377

167

1228

1936 BA-93

BA-91

UW-2

Fig. 2. The number of shared and unique genes for all 10 Accumulibacter genomes. A. Pairwise comparison of the two type IA genomes. B. Three-way venn of the type IIC genomes. C. Three-way venn of the type IIF genomes. D. Five-way venn diagram comparing each of the different ppk1 types. Where a type contained multiple genome references, the non-redundant union of orthologues from all genomes was used for the comparison.

BA-94

BA-91 SK-01 SK-02

IIC

D

380

165

1577

3807

226

70

IA

BA-93 UW-2

123

16

81 89

1768

IIA UW-1

261

64

146

732

23

33 2047

35

36

141 211 37 108

55

326

62

132

96 291

33

69

918

3915

IC IIF

BA-93

SK-11 SK-12 BA-94

There were no major metabolic differences between the two strains (see below), which suggests that other factors such as phage pressure or altered community composition may have caused the succession of Accumulibacter SK-12. The granular reactor, SBR2, contained the most diverse Accumulibacter populations with three genomes, BA-91 (type IIC), BA-92 (type IC) and BA-94 (type IIF). Distinct bacterial communities have been shown to develop at different depths or locations in granules (De Kreuk et al., 2005; Xavier et al., 2007; Barr et al., 2010) and these extra niches may allow multiple Accumulibacter species to coexist. Accumulibacter pangenome The Accumulibacter pangenome was determined using a hierarchical approach where the genomic diversity within each ppk1 type with multiple representatives (type IA, IIC and IIF) was determined and compared with the ppk1 types with only a single genome representative (type IC

and IIA). The ‘core’ Accumulibacter genome consisted of 2047 orthologous proteins, and most genomes encoded an equivalent number of unique genes (Fig. 2). There was no enrichment in shared proteins between genomes from type II clades compared with type I genomes, reinforcing the observation of the ANI analysis that the type II Accumulibacter clades are as distant from each other as to the type I Accumulibacter (Fig. 2). The type IA genomes, Accumulibacter UW-2 and BA-93 had an ANI score of 99.0%, which indicates that they are likely strains of the same species; however, both genomes contained large sets of unique genes (820 and 1364 genes respectively; Fig. 2A). The number of unique genes between these two genomes is greater than reported for strains of Propionibacterium acnes (Tomida et al., 2013), Legionella pneumophila (D’Auria et al., 2010), Escherichia coli (Rasko et al., 2008) and Yersinia pestis (Eppinger et al., 2010), suggesting that the diversity in gene complement can be hidden when using ANI as the only similarity measure. True similarity between two genomes requires

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 1574–1585

1578 C. T. Skennerton et al. both the ANI and shared gene complement. Within type IIC, Accumulibacter SK-01 and SK-02 shared a higher fraction of genes compared with Accumulibacter BA-91 (Fig. 2B), which is in agreement with the higher ANI and alignment between these two genomes (Fig. 1). In the type IIF genomes, Accumulibacter SK-11 and SK-12 share a higher fraction of genes compared with Accumulibacter BA-94 (Fig. 2C); however, there was no difference between either the ANI nor the genome alignments (Fig. 1). Accumulibacter BA-94 was the most incomplete genome, which may explain why there were fewer shared orthologues. The functionality encoded in the unique genes for each Accumulibacter genome was assessed using the Eggnog database (Powell et al., 2012). Consistent with pangenomes from other organisms, the unique set of Accumulibacter genes were overrepresented in genesencoding signal transduction and hypothetical proteins (Fig. S6). Accumulibacter BA-91, SK-01, SK-02 (type IIC) and UW-2 (type IA) contained many more unique genes associated with replication, recombination and repair (Clusters of Orthologous Groups category L) than the other Accumulibacter genomes; however, most of these unique genes encoded transposases. A small number of the unique genes were associated with ‘core’ metabolic processes, such as the alpha subunit of DNA polymerase III in Accumulibacter BA-91. As all of the genomes analyzed here are draft sequences (with the exception of Accumulibacter UW-1), it is possible that individual genes may be missing in the assembly. Accumulibacter core metabolism All Accumulibacter genomes share common carbon and phosphorus metabolism pathways that are essential for the PAO metabolic properties of this lineage (Fig. 3). The phosphate metabolism is shared in all Accumulibacter genomes with both low (pitA) and high (pstABC) affinity phosphate transporters, as well as genes involved in the production (ppk1) and hydrolysis (ppx) of polyphosphate. All Accumulibacter types share the same metabolic pathways for PHAs synthesis from propionyl-CoA or acetyl-CoA, glycolysis and the TCA cycle. One of the debated features of Accumulibacter carbon metabolism is whether the EMP or ED pathway is used during anaerobic glycolysis (Oehmen et al., 2007). The presence of the EMP pathway in all currently sequenced Accumulibacter genomes suggests that the ED pathway (Maurer et al., 1997; Hesselmann et al., 2000) is not used by Accumulibacter; however, it is possible that it may be found in other strains currently without genomic representation. Another debated feature is the source of reducing power to generate PHA in the anaerobic phase. Reducing

power has been hypothesized to originate from either glycogen (Mino et al., 1987) or the anaerobic operation of the TCA cycle (Comeau et al., 1986; Wentzel et al., 1986). The anaerobic operation of the TCA cycle can only occur if reduced quinones produced by succinate dehydrogenase can be re-oxidized, which may be catalyzed by a novel cytochrome linked to a quinol reductase discovered in the Accumulibacter UW-1 genome (Garcia Martin et al., 2006). Homologues of this protein were found in all other Accumulibacter genomes, with the exception of Accumulibacter BA-91 and BA-92, which may be missing because of incomplete assemblies. Alternative proposals for the source of reducing power have also considered the glycoxylate shunt (Louie et al., 2000) or a split TCA cycle (Pramanik et al., 1999; Hesselmann et al., 2000; Kortstee et al., 2000). The split TCA cycle operates in the conventional direction from citrate to succinyl-CoA (right branch) or in the reverse direction from oxaloacetate to succinyl-CoA (left branch) (Fig. 3; dashed line), using the methylmalonyl-CoA pathway as a sink for succinyl-CoA. Either of these proposals may be correct as the Accumulibacter core genome contained the methylmalonyl-CoA mutase (mcm) and isocitrate lyase (icl), key enzyme in the methylmalonyl-CoA pathway and glycoxylate shunt respectively. One of the surprising features first uncovered in the Accumulibacter UW-1 genome was the presence of carbon fixation pathways, given that EBPR reactors are very carbon-rich environments (Garcia Martin et al., 2006). Analysis of the core Accumulibacter genome revealed that all genomes contained the Calvin cycle utilizing a type II RuBisCO; however, Accumulibacter UW-2 lacked this gene. The Accumulibacter UW-2 and BA-93 genomes are highly syntenous and a comparison between these genomes in the region containing RuBisCO in Accumulibacter BA-93 revealed that there was a gap in Accumulibacter UW-2 where the RuBisCO gene should reside (Fig. S7). Given the similarity between these two genomes, it is likely that RuBisCO is also encoded in the Accumulibacter UW-2 genome. The presence of carbon fixation, along with high-affinity transporters for nutrients such as phosphate, suggests a lifestyle that is suited to low-nutrient environments. Apart from EBPR systems, Accumulibacter are also found in freshwater sediments, where the fluctuating boundaries between aerobic and anaerobic zones may provide an analogous environment to the operational conditions of EBPR reactors (Peterson et al., 2008). Differences in carbon metabolism The carbon metabolism associated with Accumulibacter is restricted to low molecular weight substrates such as VFAs. The core Accumulibacter genome supports these

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 1574–1585

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 1574–1585 phaA phaB phaC

Quinol Reductase

H+

MQH2

MMD

Propionyl-CoA

phaA phaB phaC

phaZ

PHV

+

MQ

H+

PCC

phaZ

phaA phaB phaC

phaZ

Acetylaldehyde

malDH

Malate

MCM

Succinate

FR

malS

Oxalo acetate

H+

Methyl Malonyl-CoA

Na+

SDH

Acetate

Acetyl-CoA

Fumarate

fumC

ALDH

Calvin-Benson-Bassham Cycle

SCS

ICL

Periplasm

NOS

Cytoplasm

acn

IDH

N2O N2

N2O

NOR

Oxoglutarate

OGDC

ppk1

ATP

pap

2NO

ATP

NAP

NO

H+

NIR

-

NO3-

2H+

NO2-

2H+ MQH2

ADP Pi

NO2

+

MQ

NO3-

NAR

NO2-

NO2

MQH2

2H+

ADP

adk

AMP

Pst

Polyphosphate

ppx

Pi

Isocitrate

Embden Meyerhof Parnas

Citrate

Succinyl CoA

Glycoxylate

CS

Pyruvate

Glycerate-3P

Fructose-6P

Glucose-6P

ADP

Pi

+

MQ

H+

NO3-

2H+

F-ATPase

Fig. 3. Carbon, phosphate and nitrogen metabolism in the Accumulibacter genomes. The aerobic and anaerobic metabolic processes are shown with blue and red arrows respectively. Features that are common to all sequenced Accumulibacter genomes are shown as white with black borders. These features include the phosphate, PHA and central carbon metabolic pathways. Coloured genes/energy sources are found only in a subset of the genomes. The production of reducing power in the anaerobic phase may be attributed to glycogen degradation, anaerobic operation of the TCA cycle using a novel quinol reductase, the split TCA cycle (red dashed arrow) or through the glycoxylate shunt. Abbreviations: PHB, polyhydroxybutyrate; PHV, polyhydroxyvalerate; PH2MV, polyhydroxy-2-methylvalerate; CS, citrate synthase; ACN, aconitase; OGDC, 2-oxoglutarate dehydrogenase; SCS, succinyl-CoA synthetase; FR, fumarate reductase; SDH, succinate dehydrogenase; PPC, propionyl-CoA carboxylase; MMD, biotin carboxyl carrier protein in methylmalonyl-CoA decarboxylase; MCM, methylmalonyl-CoA mutase; ADH, alcohol dehydrogenase; ALDH, acetaldehyde dehydrogenase; ICL, isocitrate lyase; NAR, respiratory nitrate reductase; NAP, periplasmic nitrate reductase; NIR, nitrite reductase; NOR, nitric oxide reductase; NOS, nitrous oxide reductase.

SK-11, SK-12, UW-1, UW-2, BA-93

BA-91, UW-1, UW-2, BA-92, BA-93

SK-11, SK-12, BA-92, BA-93, BA-94, UW-1, UW-2

SK-01, SK-02, BA-91

SK-11, SK-12

Universal

Anaerobic phase

Aerobic phase

Legend

prpE

Propionate

PH2MV

ADH

PHB

Ethanol

CO2

Glycogen

Pit H+ Mg2+ Pi

Metabolic plasticity in Accumulibacter clades 1579

1580 C. T. Skennerton et al. findings; however, Accumulibacter SK-11 and SK-12 (type IIF) may also be able to utilize ethanol as a carbon source. All Accumulibacter genomes contained alcohol dehydrogenase (ADH) that converts ethanol to acetaldehyde; however, Accumulibacter SK-11 and SK-12 also contain acetaldehyde dehydrogenase (ALDH), which generates acetate. Previous metabolic tests using fluorescence in situ hybridization and microautoradiography have shown that some Accumulibacter types could not use ethanol as a carbon source (Kong et al., 2004); however, the clade of Accumulibacter analyzed in that study was unknown. The use of ethanol as a carbon source may offer a new opportunities to cultivate members of type IIF. Differences in nitrogen metabolism Previous functional studies of EBPR reactors have inferred that nitrogen metabolism is a defining ecological difference between Accumulibacter clades (Flowers et al., 2009; Oehmen et al., 2010; Kim et al., 2013). It has been shown through reactor performance that Accumulibacter IA can utilize nitrate as a terminal electron acceptor, whereas Accumulibacter IIA cannot (Flowers et al., 2009; Oehmen et al., 2010). Contradictory to these results, the Accumulibacter UW-1 genome (type IIA) encodes a full denitrification pathway (Garcia Martin et al., 2006). One hypothesis for this difference between genotype and phenotype has been the periplasmic nitrate reductase (napABCDFGH) in the Accumulibacter UW-1 genome. Periplasmic nitrate reductases have a dual role as a redox-balancing complex or in anaerobic respiration depending on the organism (Moreno-Vivián et al., 1999; Stewart et al., 2002; Delgado et al., 2003), and in some cases, its activity is not sufficient to support anaerobic respiration (Kerber and Cardenas, 1982; Bell et al., 1993). It is possible that the nap genes in Accumulibacter UW-1 are being using for redox balancing and cannot support anaerobic respiration. For this hypothesis to be valid, Accumulibacter IA should encode a respiratory nitrate reductase (narGHIJ); however, both Accumulibacter IA genomes (Accumulibacter BA-93 and UW-2) only encode a periplasmic nitrate reductase similar to that found in Accumulibacter UW-1. The presence of identical gene complements for nitrate reduction in both of Accumulibacter type IA and IIA suggests that gene regulation may be responsible for the functional differences in reactors enriched with Accumulibacter IA or IIA (Flowers et al., 2013). The three genomes from type IIF (SK-11, SK-12, BA-94) and IC (BA-92) also contain nap-type nitrate reductases; however, it is unclear whether they can perform respiratory nitrate reduction. Interestingly, Accumulibacter SK-01, SK-02 and BA-91 (type IIC) carry a respiratory nitrate reductase (narGHI).

Accumulibacter type IIC has previously been enriched during the transition from an anaerobic-oxic to an anaerobic-anoxic-oxic SBR operation (Kim et al., 2013). The presence of a respiratory nitrate reductase may allow Accumulibacter IIC to dominate under these conditions. A phylogenetic tree of NarG revealed that the Accumulibacter proteins were most closely related to members of the Comamonadaceae, suggesting that the nar-type nitrate reductase has been laterally transferred (Fig. S8). The presence of subsequent steps of the denitrification pathway varied between the different Accumulibacter clades (Fig. 1B). The majority of Accumulibacter genomes contain an assimilatory (nirBD) and dissimilatory (nirS) nitrite reductase. Nitric oxide reductase (norZ) was found in Accumulibacter BA-92 (type IC), BA-93 (type IA), UW-1 (type IIA) and UW-2 (type IA). Nitrous oxide reductase (nosDFLZ) was found in Accumulibacter BA-93, UW-2 (type IA), UW-1 (type IIA), SK-11 and SK-12 (type IIF). The variation in the denitrification pathway is probably due to both biological and informatic reasons. Genome incompleteness is the most likely reason for Accumulibacter SK-11 lacking nitric oxide reductase, as this genome contained all other components of the denitrification pathway (Fig. 1B). Conversely, Accumulibacter SK-01 lacked most of the denitrification pathway (Fig. 1B); however, it was one of the most complete genomes (Table 1), which suggests that it has lost this metabolic capability. Nitric oxide reductase and nitrous oxide reductase are important sinks for electrons when the electron donor is in excess and are important for the reduction of toxic intermediate products (nitric oxide). Without these two components, Accumulibacter SK-01 may have reduced capability to use nitrate or nitrite as the terminal electron acceptor. As additional genomes are sequenced, the distribution of denitrification genes and their possible effects on niche partitioning will continue to be refined within and between the various Accumulibacter clades. Nitrogen fixation appears widely distributed among the Accumulibacter genomes, with only Accumulibacter UW-2 lacking the genes required for nitrogenase or the biosynthesis molybdenum-iron cofactor. However, Accumulibacter UW-2 does contain the Rnf electron transport complex (rnfABCDEG) that is required for nitrogen fixation in some organisms (Biegel et al., 2010) and may suggest that Accumulibacter UW-2 has recently lost its nitrogen fixation capability. Conclusions Metagenomic sequencing of lab-scale EBPR reactors enriched in Accumulibacter resulted in the assembly of eight new draft genomes from four different ppk1 types. Metabolic inferences from these new genomes

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 1574–1585

Metabolic plasticity in Accumulibacter clades representative of the Accumulibacter genus support many of the observations made from the sequencing of Accumulibacter UW-1 and UW-2. The type IIC genomes (SK-01, SK-02 and BA-91) contained different nitrate reductases to members of the other clades and some of the type IIF genomes (SK-11 and SK-12) appear capable of using ethanol as a carbon source. The source of reducing power for the anaerobic operation of the TCA is still unresolved; however, these newly sequenced genomes can provide the basis of future transcriptomic or proteomic studies investigating this aspect of Accumulibacter metabolism. The metabolic differences observed between Accumulibacter clades suggest avenues for selective enrichment of particular types within EBPR reactors and support the hypotheses that these clades occupy different ecological niches. Experimental procedures Reactor operation All of the SBRs used in this study were seeded from Thorneside Wastewater Treatment Plant, Queensland, Australia (− 27.485973, 153.190699). Each of the SBRs was fed with a mix of synthetic wastewater containing 800 mg l−1 of acetate and 40 mg l−1 phosphate (20:1 chemical oxygen demand:phosphate) (described in detail in Lu et al., 2006). SBR1 and SBR4 were operated on 6 h reaction cycles of 120 min anaerobic, 180 min aerobic phase, 20 min settling, 20 min decant (1 l volume removed from the reactor supernatant) and 14 min pre-feed oxygen purge. At the end of each cycle 1 l of synthetic wastewater was added at a rate of 166 ml min−1. SBR3 and SBR5 were operated on 6 h reaction cycles of 60 min anaerobic, 180 min aerobic phase, 20 min settling, 20 min decant (1 l volume removed from the reactor supernatant) and 14 min pre-feed oxygen purge. These two reactors were fed 1 l of synthetic wastewater at a rate of 16.6 ml min−1 under anaerobic conditions. SBR2 was operated for granule formation as previously described (Barr et al., 2010). Three SBRs (SBR1, SBR4 and SBR5) were sampled at multiple time points. SBR1 was sampled twice 22 months apart; SBR4 was sampled six times over a period of 9 months; and SBR5 was sampled three times over a period of 6 months. This produced 13 samples for metagenomic sequencing (see Fig. S1).

Sequencing, assembly and genome binning Microbial DNA was extracted by centrifuging flocs to remove the supernatant and using ∼ 500 mg of biomass for the MP-BIO FASTSPIN® spin kit for soil and approximately 2 μg of extracted DNA was sequenced. Sample M92408 and M82408, originating from SBR1 and SBR3, respectively, were sequenced at Emory University (Georgia, USA) using the Illumina GAIIx genome analyzer using a paired end 100 × 100 library with a 300 bp insert size. Sample F2411 and G2411, originating from SBR1 and SBR2, respectively, were sequenced at the Beijing Genome Institute (Beijing, China) using

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the Illumina HiSeq2000 with a paired end 100 × 100 library with a 300 bp insert size. Sample M90108, M92511, M92705, from SBR4 and M81706, M81612, M80509 from SBR5 were sequenced at Aalborg University (Alborg, Denmark) using the Illumina HiSeq2000 using a paired end 150 × 150 library with a 300 bp insert size. The remaining three samples (M92206, M90809, M91801), originating from SBR4 were sequenced at the Institute for Molecular Biosciences (Brisbane, Australia) using the Illumina HiSeq2000 with a paired end 150 × 150 library and a 300 bp insert size. Raw metagenomic data from samples was assembled and binned using two different approaches to recover the best possible draft Accumulibacter population genome assemblies. See Table S2 for binning approach used for each genome and Table S3 for the assembly conditions. The Accumulibacter SK-02 genome was produced by an assembly from sample M82408 using VELVET 1.0.19 (Zerbino and Birney, 2008) with all other genomes originating from assemblies produced using CLC genomics workbench 5.5 (Table S3). Raw data were loaded into CLC GENOMICS WORKBENCH 5.5 (http://www.clcbio.com), specifying the lower and upper bounds of the insert size as 180–400 bp. Reads were trimmed using the CLCBio trimming algorithm using a quality threshold of 0.01. Trimmed reads were assembled using the CLCBio de novo genome assembler using an automatic kmer and bubble size (see Table S3 for their values). The raw metagenomic reads were mapped onto the scaffolds using BWA 0.7.5A using the ‘mem’ algorithm (Li, 2013). Genomes were extracted from the combined assembly using spatiotemporal binning with GROOPM 0.1 (Imelfort et al., 2014) using a 3000 bp contig size limit during the ‘core’ step and subsequently recruiting smaller contigs using a 1500 bp limit. Binning of the separate assemblies was performed according to the method of Dick and colleagues (2009) using the DATABIONICS ESOM software package (http://databionicesom.sourceforge.net/). Tetranucleotide frequencies from contigs greater than 5 kbp were determined using KMER_ COUNTER.RB 1.2 (http://github.com/wwood/bioruby-kmer_ counter) using the default settings. These frequencies were used for the ‘train’ command in ESOM. Bins were extracted manually from ESOM.

Genome fidelity estimates Genome completeness and contamination was estimated using single-copy marker genes using CHECKM 0.7.1 (http:// ecogenomics.github.io/CheckM/). CHECKM used a set of 427 marker genes found in completed Betaproteobacteria genomes currently in public databases. HMMER 3.1 (http:// hmmer.org) was used to identify markers in the translated ORFs for genome bin. The contig coverage was determined as an average across the entire contig length and in adjoining 10 kbp blocks, those that did not match the coverage of other contigs in the genome bin were removed as contaminants (Fig. S9).

Genome coverage and SNP analysis The genome coverage analysis shown in Figs S5 and S9 were generated by using all available Accumulibacter genomes (the eight genomes sequenced in this study plus

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 1574–1585

1582 C. T. Skennerton et al. strains UW-1 and UW-2) as the reference sequences for using BWA 0.7.5A. All of the raw metagenomic sequencing reads were aligned against this reference set allowing average coverage and 10 kbp window coverage to be calculated using BAMDEPTH (https://github.com/ctSkennerton/ bamdepth). SNP analysis was performed using BCFTOOLS 0.1.19-44428CD with the BAM files generated for the genome coverage analysis. SNPs that were within genes identified in all 10 Accumulibacter genomes are shown in Fig. S4. The SNP frequency was calculated as an average for each 1 kbp of gene length.

Genome annotation Genomes were automatically annotated using the PROKKA 1.8 annotation pipeline (http://www.vicbioinformatics.com/). PROKKA used the following as components: PRODIGAL 2.5 (Hyatt et al., 2010), ARAGORN 1.2.34 (Laslett and Canback, 2004), RNAMMER 1.2 (Lagesen et al., 2007), SIGNALP 4.0 (Petersen et al., 2011), HMMER 3.0 (http://hmmer.org) and BLAST+ 2.2.26 (Camacho et al., 2009). As a supplement to this annotation, the KAAS web server was used to determine Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologues and map genes onto KEGG pathways (Moriya et al., 2007).

Genome tree construction The genome tree was constructed from a concatenated alignment of 38 protein-coding genes that are universally distributed and in a single copy in both archaea and bacteria (Wu et al., 2013). The basis for the tree was the finished genomes publicly available in IMG 4.0. The marker genes were discovered in each finished genome and in the genome bins using PHYLOGENETICM (http://github.com/Ecogenomics/ PhylogeneticM). A maximum likelihood tree was created using PHYML 3.1 using the default settings for amino acids and 100 bootstraps.

ppk1 Phylogenetic analysis An in silico PCR was performed on polyphosphate kinase genes identified in assembled contigs using ACCppk1-254F and ACCppk1-1376R (McMahon et al., 2007) using GENEIOUS 6.1.4 (http://www.geneious.com) allowing for two mismatches between the primer and the target sequence. The ppk1 gene fragments were added to a list of representative ppk1 gene fragments previously sequenced (He et al., 2007; McMahon et al., 2007; Peterson et al., 2008). The sequences were aligned using MAFFT 6.864B (Katoh and Toh, 2008) using the ‘auto’ option. A maximum likelihood tree was created using PHYML 3.1 (Guindon et al., 2010) using the generalized time reversible substitution model and 100 bootstraps. The ppk1 sequence from Rhodocyclus tenius was used as the outgroup.

NarG phylogenetic analysis NarG sequences from Proteobacteria were downloaded from National Center for Biotechnology Information (NCBI). A

non-redundant set of these proteins was created by clustering sequences together at 97% identity using CD-HIT 4.5.4 (Li and Godzik, 2006). This representative set was combined with NarG sequences from Accumulibacter SK-01, SK-02 and BA-91 and aligned using MAFFT 6.864B using the ‘auto’ option. A maximum likelihood tree was created using PHYML 3.1 using 100 bootstrap iterations, all other options left default for amino acids.

Pangenome comparison The Accumulibacter pangenome was determined using PROTEINORTHO 4.26 (Lechner et al., 2011) with the amino acid sequences of predicted ORFs. The ANI of Accumulibacter genomes was determined using the method described by Richter and Rosselló-Móra (2009) as implemented in the python script ‘CALCULATE_ANI.PY’ available at https://github .com/ctSkennerton/scriptShed/blob/master/calculate_ani.py.

Data availability The draft genome sequences have been deposited in the NCBI whole genome shotgun database under the accession numbers: JDSS00000000 (SK-01), JDST00000000 (SK-02), JFAW00000000 (SK-11), JFAX00000000 (SK-12), JDVG00000000 (BA-91), JEMX00000000 (BA-92), JEMY00000000 (BA-93), JEMZ00000000 (BA-94). Links to the raw sequencing data, sample metadata and genomes described in this study can be accessed through the NCBI Bioproject database using the accession: PRJNA231882.

Acknowledgements We wish to thank Eva Andresen, Stan Chan and Sharifah Syed Mohamed for assistance with the reactor operation. This work was supported by the Australian Research Council (ARC) grant number ARC-DP0773857. GWT was supported by an ARC Queen Elizabeth II fellowship (ARC-DP1093175). CTS was supported by an Australian Research Council Postgraduate Award (APA).

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Supporting information Additional Supporting Information may be found in the online version of this article at the publisher’s web-site: Fig. S1. Reactor operation and sampling timeline. Each shaded box represents the time frame for reactor operation over a period of ∼ 3 years. Each reactor is shaded based on its broad operational conditions and vertical lines through each box represent sampling points for metagenomic sequencing. Fig. S2. Maximum likelihood phylogenetic tree of Accumulibacter ppk1 nucleotide sequences. Monophyletic Accumulibacter clades are shown mostly as compressed wedges; however, clade IIC, IIF, IA and IC are expanded to show the placement of genomes sequenced in this study (coloured in red) and the two other Accumulibacter genome representatives, coloured blue. Black circles on node branches indicate > 70% bootstrap support. Fig. S3. Maximum likelihood phylogenetic tree of Rhodocyclaceae constructed from the concatenated alignment of 38 single-copy genes; Neisseria meningitides was used as the outgroup. Node labels refer to bootstrap support and Accumulibacter genomes are annotated by their ppk1 type. Fig. S4. Boxplots of SNP frequencies for core Accumulibacter genes for each of the genomes for the metagenome samples in which they were present. Genes that contained a SNP frequency greater than 1.5 × the

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interquartile range plus the third quartile are marked as outliers using a blue plus symbol. The median SNP frequency for all of the genes is marked with a red line. Fig. S5. Mean coverage per contig for all Accumulibacter strains sequenced in these studies in SBR4 and SBR5, which were sampled at six and three timepoints respectively. Fig. S6. Comparison of COG categories for unique genes in each Accumulibacter genome. COG categories: C, energy production and conservation; D, cell cycle control, cell division, chromosome partitioning; E, amino acid transport and metabolism; F, nucleotide transport and metabolism; G, carbohydrate transport and metabolism; H, coenzyme transport and metabolism; I, lipid transport and metabolism; J, translation, ribosome structure and biogenesis; K, transcription; L, replication, recombination and repair; M, cell wall/membrane/ envelope biogenesis; N, cell motility; O, postranslational modification, protein turnover, chaperones; P, inorganic ion transport and metabolism; Q, secondary metabolites biosynthesis, transport and catabolism; R, general function prediction; S, function unknown; T, signal transduction; U, intracellular trafficking, secretion and vesicular transport; V, defence mechanisms. Fig. S7. Alignment of Accumulibacter UW-2 and BA-93 genomes around the position of RuBisCO. Both genomes are syntenous at this region, represented by the grey shading between genes; however, there is a gap in the scaffold for the UW-2 genome that has removed the majority of RuBisCO and two other genes. The IMG gene IDs are given for the genes in the Accumulibacter UW-2 genome. Fig. S8. Maximum likelihood phylogenetic tree of Accumulibacter NarG with NarG proteins from representative Burkholderiales genomes. Pseudogulbenkiania (Neisseriales) used as the outgroup. Fig. S9. Contig coverage per 10 kbp block for each of the Accumulibacter strains sequenced in the study. All contigs from each draft genome are shown concatenated in order across the x-axis for each subplot. If contigs were less than 10 kbp in length then the average coverage across the total length of that contig was used. For each genome, only the samples for which they were identified are shown. Table S1. Summary EBPR performance characteristics and genome presence for all 13 metagenomic samples. Table S2. Binning strategy used to recover each of the Accumulibacter genome sequenced in this study. Table S3. Assembly parameters used to recover each Accumulibacter draft genome.

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 1574–1585