Development and application of primers for the class Dehalococcoidia (phylum Chloroflexi) enables deep insights into diversity and stratification of subgroups in the marine subsurface.

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Environmental Microbiology (2015) 17(10), 3540–3556

doi:10.1111/1462-2920.12510

Development and application of primers for the class Dehalococcoidia (phylum Chloroflexi) enables deep insights into diversity and stratification of subgroups in the marine subsurface

Kenneth Wasmund,1*† Camelia Algora,1 Josefine Müller,1 Martin Krüger,2 Karen G. Lloyd,3‡ Richard Reinhardt4 and Lorenz Adrian1 1 Helmholtz Centre for Environmental Research – UFZ, Permoserstraße 15, Leipzig, D-04318, Germany. 2 Federal Institute for Geosciences and Natural Resources (BGR), Hannover, Germany. 3 Center for Geomicrobiology, Department of Bioscience, Aarhus University, Aarhus, Denmark. 4 Max Planck Genome Centre Cologne, Cologne, Germany. Summary Bacteria of the class Dehalococcoidia (DEH) (phylum Chloroflexi ) are widely distributed in the marine subsurface and are especially prevalent in deep marine sediments. Nevertheless, little is known about the specific distributions of DEH subgroups at different sites and depths. This study therefore specifically examined the distributions of DEH through depths of various marine sediment cores by quantitative PCR and pyrosequencing using newly designed DEH 16S rRNA gene targeting primers. Quantification of DEH showed populations may establish in shallow sediments (i.e. upper centimetres), although as low relative proportions of total Bacteria, yet often became more prevalent in deeper sediments. Pyrosequencing revealed pronounced diversity co-exists within single biogeochemical zones, and that clear and sometimes abrupt shifts in relative proportions of DEH subgroups occur with depth. These shifts indicate varying metabolic properties exist among DEH subgroups. The distributional changes in DEH subgroups with depth may be related to a combination of biogeochemical factors including the availability of electron acceptors Received 19 January, 2013; accepted 11 May, 2014. *For correspondence. E-mail [email protected]; Tel. +43 1 4277 76606; Fax +43 1 4277 876601. Present addresses: †Division of Microbial Ecology, Faculty of Life Sciences, University of Vienna, Vienna, Austria; ‡Department of Microbiology, University of Tennessee, Knoxville, TN, USA.

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

such as sulfate, the composition of organic matter and depositional regimes. Collectively, the results suggest DEH exhibit wider metabolic and genomic diversity than previously recognized, and this contributes to their widespread occurrence in the marine subsurface. Introduction Marine subsurface sediments harbour massive numbers of microbial cells with estimates up to 2.9 × 1029 cells (Kallmeyer et al., 2012). This marine subsurface ‘biosphere’ is considered to contribute substantially to the Earth’s biogeochemical cycles, especially over geological timescales (D’Hondt et al., 2002; 2004; Wellsbury et al., 2002). Although this biosphere may contain a large proportion of Earth’s prokaryotes, only recently have attempts been made to understand the distribution and metabolic properties of subsurface prokaryotes. While a highly diverse range of Bacteria and Archaea have been identified in the marine subsurface, various groups such as the phylum Chloroflexi are frequently present as high proportions of microbial communities and sometimes dominate communities at geographically dispersed locations and depths (Teske, 2006; Fry et al., 2008; Parkes and Sass, 2009). In some cases, Chloroflexi represent up to 80% of the bacterial 16S rRNA phylotypes amplified from deep sediments (Parkes et al., 2005; Inagaki et al., 2006), and average around 17% of total bacterial 16S rRNA phylotypes recovered (Fry et al., 2008). Members of the Chloroflexi may therefore be one of the most abundant groups of bacteria on Earth considering the massive scale of the subsurface, yet very little is known about their ecological distributions, diversity and/or metabolic properties. The phylum Chloroflexi, formerly known as the ‘Green Non-Sulfur Bacteria’ (Woese, 1987), is a deeply branching phylum of the Bacteria that harbours extensive intraphylum diversity, and isolated strains of the Chloroflexi show a wide range of physiological properties (Oyaizu et al., 1987; Sekiguchi et al., 2003; Hugenholtz and Stackebrandt, 2004; Sorokin et al., 2012; Kawaichi et al.,

Dehalococcoidia distribution and diversity 2013). In the marine subsurface, numerous 16S rRNA gene sequences have been detected by PCR-based methods that are affiliated with clades such as the Chloroflexi ‘subphylum I’ (i.e. classes Anaerolineae and Caldilineae) (Blazejak and Schippers, 2010) or ‘subphylum II’ (i.e. class Dehalococcoidia, previously known as class ‘Dehalococcoidetes’) (Hugenholtz and Stackebrandt, 2004; Löffler et al., 2012). The formal name class ‘Dehalococcoidia’ (DEH) was only recently given to accommodate a collection of isolates belonging to the genera Dehalococcoides and Dehalogenimonas, as well as strain ‘Dehalobium chlorocoercia’ DF-1 (Löffler et al., 2012). In addition to these strains, diverse 16S rRNA gene sequences derived from uncultivated environmental bacteria form a large clade with the cultivated DEH strains, which is clearly distinct from other currently described class-level clades of the Chloroflexi, such as the Anaerolineae, Caldilineae and the pelagic ‘SAR202’ (Morris et al., 2004; Yamada et al., 2006). These DEH affiliated 16S rRNA sequences appear to be the most frequently detected Chloroflexi-affiliated sequences in the marine subsurface (Coolen et al., 2002; Inagaki et al., 2003; 2006; Parkes et al., 2005; Webster et al., 2006; Wilms et al., 2006b; Nunoura et al., 2009; Durbin and Teske, 2011; Jorgensen et al., 2012). Furthermore, metagenomic studies of marine subsurface sediments have identified numerous DNA sequence reads with high similarities to genes of known DEH (Biddle et al., 2008; 2011), and therefore substantiate the presence of DEH in the marine subsurface while bypassing the biases associated with PCR-based approaches. Currently, an emerging understanding of the metabolic properties of the DEH is based on the physiology and biochemistry of a collection of closely related strictly anaerobic isolates, as well as on data from a single-cell genome and metagenomic analyses. The isolated strains of Dehalococcoides mccartyi (Löffler et al., 2012), Dehalogenimonas lykanthroporepellens (Moe et al., 2009), Dehalogenimonas alkenigignens (Bowman et al., 2012) and ‘Dehalobium chlorocoercia’ strain DF-1 (May et al., 2008) all grow exclusively via organohalide respiration, i.e. they use halogenated organic compounds as terminal electron acceptors (Tas et al., 2010). Other phylogenetically related DEH have also been implicated in organohalide respiration by detection of 16S rRNA genes from enrichment or stable isotope probing experiments (Fagervold et al., 2005; 2007; Watts et al., 2005; Bedard et al., 2007; Kittelmann and Friedrich, 2008a). Additionally, the detection of genes encoding terminal respiratory reductases used for organohalide respiration in the marine subsurface, i.e. reductive dehalogenase homologues (rdh), has also led to speculation other DEH may also catalyse reductive dehalogenation in the marine subsurface because most rdh genes were related to those

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derived from cultured DEH (Adrian, 2009; Futagami et al., 2009; 2013). Recent cultivation-independent genomic analyses have improved our understanding of the metabolic potential of subsurface DEH. A partial genome from a marine sediment-derived single DEH cell (‘DEH-J10’) (Wasmund et al., 2014), two terrestrial aquifer sediment-derived metagenome assembled DEH ‘pan-genomes’ (‘RBG-2’ and ‘RBG-1351’) and various Chloroflexi-related metagenomic scaffolds (Hug et al., 2013) have been recovered. Collectively, the genomic data indicated metabolic versatility in these uncultured DEH, for instance, the potential to oxidize complex organics such as fatty acids or aromatics, to fix CO2, to use electron acceptors such as dimethyl sulfoxide for respiration or to conserve energy as ATP via substrate-level phosphorylation (i.e. acetate formation) (Hug et al., 2013; Wasmund et al., 2014). Notably, no genetic content related to reductive dehalogenase enzymes required for organohalide respiration was detected in the single-cell genome or pangenomes, while only few reductive dehalogenases were linked to metagenomic scaffolds that were related to Chloroflexi (Hug et al., 2013; Wasmund et al., 2014). Since the marine subsurface harbours a diverse range of DEH 16S rRNA gene sequences that are only distantly related to isolated DEH or single-cell and pan-genomes (Inagaki et al., 2006; Durbin and Teske, 2011), it is hence problematic to make assumptions about the metabolic properties of the many other uncultivated DEH groups. It is therefore clear that the acquisition of deeper insights into the distributions, diversity and metabolic properties of DEH depends on the development and application of novel tools and approaches. In this study, we developed and applied 16S rRNA gene-targeting primers to specifically detect, quantify and analyse the distribution and diversity of DEH in subsurface sediments via quantitative real-time PCR (qPCR) and pyrosequencing of amplicons. We investigated samples from very shallow cores of 8 cm Wadden Sea (Germany), medium depth cores of 3–5 m from the Baffin Bay (Greenland) and Aarhus Bay (Denmark), deep cores of 40–120 m from Porcupine Seabight and Peru Margin, and samples from different terrestrial sites. In addition, we specifically analysed a publicly available microbial community dataset from two cores from the Arctic Mid-Ocean Ridge for DEH 16S rRNA genes (Jorgensen et al., 2012). We aimed to gain insights into the ecological distribution of DEH with respect to: (i) the depths at which the DEH were most abundant and if this was related to biogeochemical properties; (ii) the degree of DEH diversity and how this DEH diversity changed with depth in the marine subsurface, e.g. if diverse communities developed in the shallow subsurface and how diversity depended on depth, and (iii) how subgroups of the DEH were

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3540–3556

3542 K. Wasmund et al. distributed throughout the sediments, and if the distribution provided insights into metabolic properties. Results Phylogeny and designation of DEH subgroups The phylogenetic ‘breadth’ of the class DEH is ambiguously defined in the literature, and therefore we provide an operational definition for the purposes of this study. In this study, we include environmental 16S rRNA gene sequences that are phylogenetically affiliated with isolated DEH and that are delineated from other currently described classes of the Chloroflexi, within our definition of the class DEH (Fig. 1). Our definition of the class DEH is largely in line with the most recent update of the widely used SILVA databases (i.e. version 115), which uses a broad definition of the class DEH (Quast et al., 2013). One major difference is that in our study, we included the subsurface ‘SO85’ clade in the DEH, based on the fact that it is phylogenetically related to isolated DEH, yet distinct from other classes of the Chloroflexi. Comparative phylogenetic analyses of DEH 16S rRNA genes using a SILVA-based alignment revealed the formation of 24 subgroups within the DEH that were largely consistent between the two phylogenetic treeing methods applied, i.e. maximum-likelihood (ML) and neighbourjoining (NJ) (Fig. 1). Average sequence identities within subgroups ranged from 86.3–95.9% (Fig. 1). The phylogenetic analyses were also important to conduct because no phylogenetic analyses for the DEH exist in which the naming of subgroups given in the widely used SILVA database has been checked for reproducibility and consistency. Our phylogenetic analyses proved important because ‘re-treeing’ sequences originally placed into named clades by the SILVA database (Supporting Information Fig. S1) showed that many sequences from certain clades did not re-cluster together (Fig. 1), even when using the exact same alignment and/or various treeing approaches. Only the ‘GIF3’ (here divided into two groups named ‘DSC-GIF3-A’ and ‘DSC-GIF-3B’) and deep-branching clades spanning SO85B-MSBL5 revealed consistent clustering of sequences after re-treeing (Fig. 1). The names for the clades in which sequences re-clustered together were kept in line with original SILVA naming, while others belonging mostly to the ‘Dehalococcoides sister clades’ (‘DSC’) (Durbin and Teske, 2011) were arbitrarily renamed. A literature search was also conducted in order to identify 16S rRNA genes of DEH (especially marine-derived) that have strong evidence for the use of halogenated organic compounds as electron acceptors (Wu et al., 2002; Watts et al., 2005; Kittelmann and Friedrich, 2008a,b; May et al., 2008; Manchester et al., 2012), and

to analyse these phylogenetically. This phylogenetic analysis showed marine sediment-derived DEH known to use halogenated organics as electron acceptors are affiliated with a distinct branch within the DEH (here denoted ‘Ord-DEH’), which contains all so far described isolates of the DEH (Fig. 1). It also shows most subsurface phylotypes are divergent from this clade. Additionally, the single-cell genome of DEH-J10 (Wasmund et al., 2014) and pan-genome of RBG-2 (Hug et al., 2013) fall within the subgroup ‘GIF9-A’. No 16S rRNA gene sequence is available for the pan-genome RBG-1351, although data indicated it is likely affiliated with the ‘Dehalococcoides sister clades’ (Hug et al., 2013). Detection and quantification of DEH in the subsurface by real-time PCR To investigate the distribution and diversity of DEH in environmental samples, primers were designed to cover a wide range of DEH 16S rRNA gene sequences (Supporting Information Appendix S1 and Supporting Information Tables S1 and S2). The primers proved highly specific for the DEH and could amplify a phylogenetically diverse range of DEH 16S rRNA genes, as shown by classification of DEH 16S rRNA gene pyrosequence data (Fig. 2), as well as by random sequencing and phylogenetic analysis of cloned DEH 16S rRNA genes generated in this study during testing of the primers (Supporting Information Fig. S2). The primers could also be applied in a qPCR assay (see Experimental procedures and Supporting Information Appendix S1 for details). DEH were detected and quantified in various marine subsurface sediment cores obtained from geographically distinct locations and through various sediment depths (Fig. 3 and Table 1). In addition, DEH were detected in a selection of terrestrial environments including contaminated groundwater, as well as freshwater stream and pond sediments (Supporting Information Fig. S2). In contaminated groundwater samples from Bitterfeld (Germany), sequences divergent from known organohalide-respiring DEH strains dominated the clone library, suggesting phylotypes other than wellcharacterized and regularly monitored phylotypes are more prevalent (Supporting Information Fig. S2). In marine sediments, DEH were detected in the uppermost depths of all sediment cores examined (Fig. 3). DEH were also detectable in very shallow sediments from a tidal flat of the Wadden Sea at a depth of 2 cm below seafloor (cmbsf), although at relatively low numbers and as a very low proportion (0.003%) of total Bacteria. In this Wadden Sea core, DEH numbers gradually increased with depth down the 8 cm core and made up to 0.13% of total Bacteria at the bottom of the core. In longer cores from Aarhus Bay and the Baffin Bay (3–4 m in depth),


Dehalococcoidia distribution and diversity

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Fig. 1. Phylogenetic tree showing major subgroups of the DEH determined by phylogenetic analyses. The tree shown is based on maximum-likelihood analysis of full-length or near full-length (i.e. > 1170 bp) DEH 16S rRNA genes derived from the SILVA database (Quast et al., 2013). Sequences presented are a selection of representatives from all sequences used in the phylogenetic analyses, in order to reduce tree complexity while maintaining the overall structure of the tree. Sequences of cultured DEH and sequences derived from other marine-DEH implicated in organohalide respiration by stable isotope probing (SIP) or enrichment experiments are highlighted in red. Sequences derived from single-cell genome DEH-J10 (Wasmund et al., 2014) and an aquifier sediment metagenome-derived genome RBG-2 (Hug et al., 2013) are highlighted in blue. The 16S rRNA gene sequence of RBG-2 was edited to match the closest aligned relative in the middle of the gene sequence where a stretch of five N’s were observed. Subgroup names are presented with a % number in parenthesis representing the ‘average evolutionary similarities’, i.e. the average of all pairwise sequence similarities within each clade (using all sequences). These were calculated using the ‘Compute Overall Mean Distance’ function within MEGA5. Leaves are labelled with (i) the subgroup name according to the SILVA database version 111, (ii) the SILVA sequence accession number, (iii) the sequence length and (iv) the Genbank accession number. The red ‘circle’ symbol at the root of the DSC clades constrains a section of the tree that showed strong multifurcating branch ordering between different phylogenetic algorithms. Sequences from the SAR202 clade are included as an out-group. The scale bar represents 1% sequence divergence.

DEH often increased slightly or stayed constant in numbers with depth in the upper sections of the cores, e.g. from 0–75 cmbsf in Baffin Bay cores 363 and 365, and the Aarhus Bay core A, before decreasing in numbers

in deeper sediment depths. In samples taken from the two deep cores from Porcupine Seabight and Peru Margin, DEH were detectable down to depths of 40 m and 120 m below seafloor (mbsf) respectively.


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Fig. 2. DEH community structuring as determined by Fast UniFrac analyses (results of unweighted analyses are presented as dendrograms) and classification of 16S rRNA gene pyrosequence data into different DEH subgroups (bar graphs) in various marine sediment cores. Samples from Aarhus Bay cores A and B are presented in one graph and are indicated in parentheses for each sediment depth. Major subgroups mentioned in the text are labelled in the bar graph. SMTZ = sulfate-methane transition zone.


Fig. 3. Quantification of DEH (○) and total bacterial (●) 16S rRNA gene copy numbers in various marine sediment cores as determined by real-time PCR. The cross symbols represent the proportion of DEH relative to total Bacteria. Note the very low percentage scale for ‘% DEH relative to total Bacteria’ in the Wadden Sea plot, and the different depth scale in the Porcupine Seabight and Peru Margin plots.

Dehalococcoidia distribution and diversity


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(Jorgensen et al., 2012)

(Blumenberg et al., 2009) (Treude et al., 2005) (Webster et al., 2009) (D’Hondt et al., 2004) (Schippers et al., 2010)

a. Values from the given references and are presented for sites examined by real-time PCR and/or pyrosequencing. n.a., not applicable; n.d., not determined; n.k., not known.

3280.0 3250.0

M72/1 SO156/3 IODP Leg 307 ODP Leg 201 SO189-2

Black Sea Chile margin Ireland (Porcupine Seabight) Peru (Peru margin) Sumatra, Indonesia Wadden Sea tidal flat, Germany Bitterfeld groundwater, Germany Stream sediments, Leipzig, Germany Pond sediments, Leipzig, Germany Arctic Mid-Ocean Ridge

n.k. n.k.

2010 ARK-XXV/3 Baffin Bay, Greenland

2007 2001 2005 2002 2006 2010 2010 2010 2010 n.k. n.k.

GC6 GC12

12°21.49′E 12°25.49′E 7°33.90′E, 8°27.83′E 51°20.47′N, 51°21.07′N, 73°21.39′N, 73°45.80′N,

2.96 3.10

3.0–0.8% n.d. 2.11–1.4% 1.93–0.36% 2.99–0.87% 1.19–1.13% 1.22–0.16% n.a. n.a. 1.2–0.25% 11.0–2.0% 1.67–1.18% n.d. n.a. n.d. n.d. 1.2–0.1% 1.3-0.2% 3.00 3.00 4.69 3.67 4.05 4.24 4.69 3.60 4.42 41.50 121.20 6.12 0.01 16.3 16.3 938.0 658.0 598.0 1716.0 2300.0 1680.0 2744.0 423.0 427.0 1855.0 0.0 56°09.60′N, 10°28.10′E 56°09.60′N, 10°28.10′E 76°52.92′N, 71°34.01′W 76°39.04′N, 71°18.79′W 75°58.24′N, 70°34.86′W 74°37.05′N, 69°13.75′W 73°19.37′N, 64°58.11′W 44°24.10′N, 32°51.27′E 34°35.00′S, 72°53.18′W 51°26.16′N, 11°33.0′W 8°59.5′S, 79°57.40′W 1°45.57′N, 96°46.44′E 54°06.61′N, 08° 56.06′E Mimosa A Mimosa B 363 365 371 389 453 214SL 7155-4 U1318 1227 140KL 2011

Site Cruise

Aarhus Bay, Denmark

Total organic carbona (wt%) Deepest sample (mbsf) Water depth (m) Co-ordinates (latitude, longitude) Sample source

Taxonomic classification of 16S rRNA gene sequences obtained by pyrosequencing revealed distinct, clear and consistent differences in the relative abundances of different DEH subgroups at different sediment depths and sites (Fig. 2). In Aarhus Bay sediments, 23 out of 24 subgroups present in our taxonomic reference database were detected. The shallowest sample analysed (10 cmbsf) contained the most diverse range of subgroups and appeared the most evenly structured, in line with measures of alpha diversity described above. In general, the GIF9-A subgroup was the most dominant subgroup throughout the core, except in the shallowest sample and deep into the methanogenic zone. In the upper layers of the sediments (10–160 cmbsf), high relative abundances of subgroups DSC-L, MSBL5 and SO85-A were present. MSBL5 and SO85-A appeared to decrease in relative proportions with depth and were

Collection date

DEH subgroup distributions

Table 1. Details of sample sites in which DEH were detected and/or analysed.

Alpha diversity of DEH was examined in various sediment cores based on 16S rRNA gene pyrosequencing that resulted in 270 base pair sequences after stringent quality controls and after bioinformatic removal of non-DEH sequences from the obtained data. Substantial diversity was detected in various marine sediment cores (Table 2), with 367 operational taxonomic units (OTUs) being the maximum number of DEH OTUs detected in the uppermost sample (10 cmbsf) from Aarhus Bay core A. The highest diversity was generally detected in the uppermost sediments of the organic-rich sites (Tables 1 and 2). Coverage estimates and rarefaction analyses indicated the sequencing effort was sufficient to capture the majority of DEH diversity within most of the samples (Table 2 and Supporting Information Fig. S3). In sediments of Aarhus Bay, richness and evenness was highest in the shallowest sediment obtained (10 cmbsf), and displayed a gradual reduction over the length of the core, from 367 OTUs at 10 cmbsf to 136 OTUs at 300 cmbsf. A slight increase in DEH OTU diversity was detected in the Aarhus Bay sample from the sulfate-methane transition zone (SMTZ) at 160 cmbsf (Supporting Information Fig. S4) compared with depths above and below this zone. A selection of samples from a replicate Aarhus Bay core (core B) substantiated the loss of diversity with depth. In the deep core from Peru Margin, a strong loss of diversity with depth was observed, with DEH OTU numbers decreasing from 185 in the upper most sediment sample to only 30 at a depth of 65 mbsf. DEH diversity values in Baffin Bay core 453 were different from the general trend in other cores, as diversity decreased within the first 1.2 m into the sediment and then increased to a maximum in the deepest sample at 453 cmbsf with 59 DEH OTUs.

Related references

DEH diversity as determined by pyrosequencing

(Jensen and Bennike, 2009) (Holmkvist et al., 2011) (Algora et al., 2013)

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Dehalococcoidia distribution and diversity barely detectable below 160 cmbsf, where sulfate was depleted. On the other hand, DSC-L stayed somewhat prevalent below 160 cmbsf. Relative abundances of DEH subgroups were particularly distinct below the SMTZ, for example, a substantially greater relative proportion of subgroup DSC-I appeared in the methanogenic zone. Relative proportions of DEH subgroups were not particularly different in the actual SMTZ sample at 160 cmbsf. In sediments of Baffin Bay, analysis of core 371 also showed a broad range of subgroups with all but two subgroups being detected in the core. Subgroups DSC-L, MSBL5 and GIF9-B were dominant in the shallowest sample and these all generally decreased in relative proportions with depth. Below the uppermost sample, subgroups GIF9-A and SO85-A constituted considerable relative proportions of DEH. The relative proportions of DEH subgroups at the deepest depth of 323 cmbsf were also highly distinct from all the shallower sediments. In Baffin Bay core 453, the two most upper sediment samples (22 and 123 cmbsf) harboured completely different subgroup distributions than the deeper sediments. The upper two samples were dominated by subgroup SO85-A, while deeper sediments were dominated by

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subgroups DSC-A and DSC-C. Here, the deepest sample harboured the most diverse range of subgroups, as already observed with alpha-diversity statistics. In sediments of Peru Margin, DEH were dominated by subgroup DSC-F in upper sediments and GIF9-B throughout the core. Subgroup GIF9-B persisted deep into the sediment where they further increased their dominance up to about 95% of all DEH sequences. Other subgroups that were present in the upper sediments of this core decreased gradually with depth. Additionally, a publicly available 16S rRNA gene pyrosequence dataset derived from two sediment cores from the Arctic Mid-Ocean Ridge (Jorgensen et al., 2012) was specifically analysed for the relative abundance of DEH subgroups (Fig. 2). This dataset was generated by PCR amplification using ‘universal’ 16S rRNA genetargeted primers that cover both bacterial and archaeal domains. Other publicly available 16S rRNA gene pyrosequence datasets from marine sediment samples were not suitable for our analysis because they had neither high enough numbers of sequences per sample, sequences < 250 bp in length, and/or sequences only from few sites or depths. In Arctic Mid-Ocean Ridge core GC6, subgroup SO85-A completely dominated the upper-

Table 2. Alpha diversity statistics for DEH in marine sediment cores. OTU thresholds were set at a 97% sequence identity cut-off.

Core

Depth

No. of sequences analyseda

Coverageb

OTUs observed

Inverse Simpson’s index

Chao1

Aarhus Bay core A

10 cmbsf 40 cmbsf 70 cmbsf 100 cmbsf 160 cmbsf 220 cmbsf 300 cmbsf 30 cmbsf 70 cmbsf 235 cmbsf 300 cmbsf 22 cmbsf 72 cmbsf 122 cmbsf 173 cmbsf 221 cmbsf 271 cmbsf 323 cmbsf 22 cmbsf 123 cmbsf 224 cmbsf 374 cmbsf 421 cmbsf 453 cmbsf 0.25 mbsf 7.96 mbsf 31.10 mbsf 40.20 mbsf 60.04 mbsf

1051 1051 1051 1051 1051 1051 1051 690 690 690 690 353 353 353 353 353 353 353 242 242 242 242 242 242 577 577 577 577 577

0.80 0.80 0.87 0.92 0.88 0.92 0.95 0.78 0.89 0.87 0.91 0.84 0.87 0.88 0.88 0.89 0.86 0.93 0.93 0.98 0.91 0.90 0.95 0.91 0.81 0.84 0.95 0.95 0.99

367 308 214 130 189 153 136 223 133 160 124 92 81 82 69 70 86 70 23 10 28 41 31 59 185 150 68 41 30

71.33 9.37 3.57 2.16 4.75 6.35 15.10 16.70 5.13 24.20 11.51 8.34 13.72 16.96 8.95 14.99 11.76 21.18 2.63 1.18 1.69 4.53 5.24 17.39 17.09 11.02 3.80 3.88 2.42

668.64 702.83 466.39 300.05 415.88 319.14 181.12 583.48 230.33 308.33 202.79 123.32 189.00 154.71 294.60 156.10 183.59 89.12 29.00 8.50 40.50 58.10 75.00 78.25 323.32 344.13 97.00 149.33 34.00

Aarhus Bay core B

Baffin Bay, core 371

Baffin Bay, core 453

Peru Margin

a. After normalization to the lowest number of sequences obtained from a sample of each core. b. Based on ‘Good’s coverage’ statistic.


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most sample at 16 cmbsf, similar to the 22 cmbsf sample from the Baffin Bay core 453, but then represented only 8% of DEH at 29 cmbsf from where it progressively increased until 174 cmsbf. This group was then almost absent or as a low relative proportion below 174 cmbsf. Subgroup DSC-K showed a sporadic relative distribution, where it was not detected in the uppermost sample, but then dominated at 29 cmbsf, prevailed until 95 cmbsf and then completely dominated at 296 cmbsf. Subgroups DSC-L and DSC-M were also present in significant relative abundances especially in deeper sediments and noticeably increased or decreased in relative abundances at different depths in a similar manner to each other. In Arctic Mid-Ocean Ridge core GC12, subgroup DSC-K was also dominant in upper sediment layers but absent in deeper layers, while SO85-A dominated in deeper sediments, and SO85-B was also prevalent in deeper sediments. Shifts in the phylogenetic composition of DEH at different sediment depths were confirmed using the Fast UniFrac analysis, which revealed distinct clustering of samples with depth (Fig. 2). In Aarhus Bay sediments, the shallowest sample displayed a highly distinct DEH community in comparison with deeper depths, and the other communities clustered distinctly above and below the SMTZ. Similarities in DEH community structuring were confirmed in a replicate core taken from the same site (core B), since DEH communities from similar depths from both cores clustered closely. Fast UniFrac analysis of Baffin Bay cores 371 and 453 also suggested the uppermost sample harboured a highly distinct DEH community compared with deeper depths, and that the communities in the deeper sediments structured distinctly through the different depths. Fast UniFrac analyses of data from the Arctic Mid-Ocean Ridge cores was not performed because of variable and sometimes low numbers of 16S rRNA gene sequences that could be assigned to the DEH. Discussion Development and applications of a DEH-specific PCR assay The DEH-specific molecular approach developed and applied in this study is a novel tool to investigate the distributions and diversity of the enigmatic DEH in the subsurface in high resolution. Although DEH have been previously detected and studied through the use of general ‘broad-range’ bacterial or universal primers (Inagaki et al., 2003; 2006; Webster et al., 2006; Wilms et al., 2006a,b; Fry et al., 2008; Nunoura et al., 2009; Durbin and Teske, 2011; Jorgensen et al., 2012), the application of DEH-specific primers has several advantages. Most notably, it allows sensitive detection of DEH in

various environments and sampling of DEH diversity with deep coverage using sequencing efforts much less than what would be required using ‘broad-range’ bacterial or universal primers. This approach therefore enables comprehensive investigations into DEH distributions and diversity in a range of samples from different sites and depths, which is critical for providing reliable insights into their ecological distributions. Such a DEH-specific approach is especially important when DEH distributions and diversity are studied in samples in which DEH constitute only low relative proportions of the total bacterial communities, as shown here for several shallow sediment layers where DEH were estimated to constitute less than 0.003% of total bacterial communities. The diversity and structure of these populations, which can be considered as ‘rare-biosphere’ populations in such cases (PedrósAlió, 2006; Sogin et al., 2006; Fuhrman, 2009), can therefore be examined in high detail within highly diverse microbial communities in which they might normally escape meaningful comparisons because of insufficient sequencing efforts. This in-depth analysis additionally enabled the first in-depth semi-quantitative examination of the relative abundances of the major subgroups of DEH in marine sediments. This importantly shows previously unrecognized high relative abundances of several deepbranching DEH clades, which may therefore make significant contributions to biogeochemical cycles in the marine subsurface and should be targets for future research. This study also highlights the importance of conducting phylogenetic analyses independent of those given as guides in commonly used databases (e.g. SILVA), which are often used as taxonomic reference templates for classification of 16S rRNA genes obtained by high-throughput sequencing approaches. A similar observation in which sequences of ‘named’ subgroups of the DEH did not re-cluster together after independent phylogenetic analysis was also observed by other researchers (Hug et al., 2013). Such considerations are especially important if sequences recovered from environmental sources that are affiliated with the DEH are dominant members of microbial communities (Ciobanu et al., 2014). DEH are diverse and composed of multiple subgroups suggesting intra-clade physiological diversity Previous investigations of bacterial communities in various marine subsurface samples have shown that multiple DEH 16S rRNA gene phylotypes occur in singlesediment layers (Inagaki et al., 2006; Durbin and Teske, 2011). In the present study, high coverage sequencing of DEH 16S rRNA genes shows expansive DEH diversity, in the order of hundreds of phylotypes and spanning various broadly related DEH subgroups exists, within some samples. This demonstrates the diversity of DEH in single


Dehalococcoidia distribution and diversity samples is remarkably greater than has been previously recognized. This has important implications because it suggests diverse and divergent co-occurring DEH populations exist that likely harbour varied genomic and therefore metabolic properties. The hypothesis that different DEH sub-clusters have different metabolic variations is strongly supported by the fact that changes in relative abundances of DEH subgroups occur with depth in all marine sediment cores examined (discussed further below). In many cases, the changes occur abruptly over relatively small scales even between closely related subgroups. This indicates varying biogeochemical conditions, e.g. differences in available substrates and/or electron acceptors select for different metabolic properties within different clades of the DEH. The recently described DEH single-cell genome ‘DEH-J10’ and DEH pan-genomes ‘RBG-2’ and ‘RBG-1351’ also demonstrate that the capacity for greater metabolic variation among the DEH exists than has been previously described (Hug et al., 2013; Wasmund et al., 2014). DEH distributions may depend on biogeochemical gradients High diversity and distinct DEH community compositions were detected in the uppermost sediments of most sediment cores and especially in the most intensively sampled core from Aarhus Bay. The fact that there was a loss of diversity below the uppermost sediment layers is suggestive that diverse DEH exist that are specialized for the utilization of a range of organic carbon sources and possibly electron acceptors, as well as combinations of these, in the upper sedimentary zones. This is plausible because diverse organic substrates as well as electron acceptors would be available in upper sediment layers, but are often quickly depleted with depth especially in organic-rich sites like Aarhus Bay (Froelich et al., 1979; Thamdrup et al., 1994; Leri et al., 2010). Correspondingly, the depletion of various organic substrates and/or electron acceptors in deeper layers may explain the large reductions in the diversity of DEH able to utilize these. Within anoxic marine sediments, sulfate is a dominant electron acceptor for anaerobic respiration (Jørgensen, 1982) and is therefore a strong controlling factor for structuring whole microbial communities in the marine subsurface (Wilms et al., 2006a,b; Hamdan et al., 2011; Jorgensen et al., 2012). In the present study, analysis of DEH subgroup distributions in Aarhus Bay sediments revealed DEH communities shifted considerably between layers where sulfate was present or depleted. This could suggest some DEH are relying on sulfate as an electron acceptor, on by-products of sulfate reduction or on the activity of sulfate reducers, e.g. as syntrophic partners for growth. The strong increase in relative abundance of sub-

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group DSC-I in the methanogenic zone of Aarhus Bay sediments may indicate members of this subgroup are especially adapted or relatively competitive in sediments essentially devoid of sulfate. One possibility could be that they survive via syntrophic associations with methanogens. This would therefore also support the notion that varied metabolic strategies exist within the DEH. It could also be observed that several subgroups showed remarkably similar distributions in the sulfate zones of both the Baffin Bay core 371 and Aarhus Bay cores. For example, in both cores, subgroups DSC-L and MSBL5 were in high relative abundance in the uppermost sediment samples, while subgroups GIF9-A and SO85-A become more prevalent below the uppermost sediment samples. This may suggest similar biogeochemical conditions at geographically separated sites can select for similar subgroup distributions and that these geographically separated clades exhibit similar metabolic properties. In this case, both Aarhus Bay and Baffin Bay core 371 cores exhibited similar total organic carbon (TOC) ranges through the depths of the cores, and both had sulfate available in the upper layers. Site-specific distributions of DEH The data presented in this study suggest that depositional regimes from the overlying water column also affect distributions of DEH in the subsurface. Depositional regimes have previously been recognized to be a strong controlling factor for the compositions of subsurface microbial communities at other sites (Inagaki et al., 2003; Wilms et al., 2006a; Webster et al., 2007; Aiello and Bekins, 2010; Coolen et al., 2013). Factors such as productivity in the overlying water column, water depth, proximity to land and sedimentation rates can strongly influence the quantity and composition of the organic matter reaching the seabed (Berger and Wefer, 1990; Jahnke, 1996). These factors might therefore be one explanation for the difference in DEH subgroup distributions observed from the different sites analysed in this study. The large variations in DEH subgroup distributions in Baffin Bay core 453 and both cores of the Arctic Mid-Ocean Ridge with depth could be due to historical depositional differences, since relatively large variations in lithology, geochemistry and TOC (Supporting Information Fig. S5) were measured in these cores (Jorgensen et al., 2012; Algora et al., 2013). Considerable differences in sediment lithology and biogeochemistry through depths of the Arctic Mid-Ocean Ridge cores were previously attributed to an array of historical depositional differences from pelagic, volcanic and hydrothermal influences in the area, and were shown to dramatically influence whole microbial communities in these cores (Jorgensen et al., 2012).


3550 K. Wasmund et al. Phylogeny and diversity of marine subsurface DEH in relation to organohalide-respiring DEH It has been previously discussed that diverse marine subsurface DEH might be able to conserve energy by organohalide respiration because of their phylogenetic relatedness to isolates that grow via this mode of respiration (Adrian, 2009; Futagami et al., 2009; 2013; Durbin and Teske, 2011). Our comparative phylogenetic analysis of DEH known to respire halogenated organics shows these phylotypes all cluster within a single clade, here denominated subgroup ‘Ord-DEH’ (Fig. 1). This may indicate the ability to perform organohalide respiration is evolutionary conserved to this particular clade. Recent evidence from single-cell genomic and metagenomic analyses found no evidence for respiratory reductive dehalogenase related genetic content in reconstructed genomes (Hug et al., 2013; Wasmund et al., 2014), while a very minor proportion of Chloroflexi-affiliated scaffolds (which were not directly linked to DEH specifically) from the metagenomic dataset harboured genes for reductive dehalogenases (Hug et al., 2013). The authors therefore discuss a certain ‘phylogenetic boundry’ for organohalide respiration may exist (Hug et al., 2013). In this study, sequences classified as belonging to the subgroup ‘Ord-DEH’ were in low relative abundances at all sites and depths. This suggests DEH phylotypes belonging to subgroup Ord-DEH, which may be more confidently linked to organohalide respiration based on phylogenetic relatedness, are generally in low relative abundance in marine sediments. Based on the high sequence divergence of the many environmental DEH phylotypes from organohalide-respiring DEH and the low relative abundances of phylotypes closely related to organohaliderespiring DEH, we therefore caution against asserting that the general detection of DEH-related Chloroflexi in the marine subsurface could be interpreted as an indication for organohalide-respiring bacteria without thorough phylogenetic interpretation. Previous research has shown diverse genes encoding terminal reductases required for organohalide respiration, i.e. reductive dehalogenase homologues (rdhA), could be amplified from various marine subsurface sediments by PCR (after whole genome amplification using Phi29 DNA polymerase) and these were related to homologues from cultivated DEH (Futagami et al., 2009; 2013). The presence of various rdhA homologues could therefore be interpreted as an indication that diverse DEH may exist in the marine subsurface that harbour the genetic potential for organohalide respiration. However, we contend that it must be kept in mind that rdhA sequence diversity so far detected in marine sediments is not substantially greater than rdhA sequence diversity found within a single species of the DEH. For example, strains of

Dehalococcoides mccartyi are known to harbour multiple copies (up to 36) of divergent rdhA genes within single genomes (McMurdie et al., 2009). Therefore the presence of diverse rdhA genes in marine sediments does not necessarily correspond to the expansive sequence diversity of DEH-affiliated 16S rRNA genes detected in the subsurface, because if in analogy to cultivated DEH, the subsurface may simply harbour a few related species or genera that harbour diverse rdhA homologues. As discussed above, evidence for the widespread existence of reductive dehalogenases in a subsurface DEH single-cell genome or metagenomes is weak or lacking, and only future large-scale genome-based sequencing projects that can better link functional genes to 16S rRNA genes will enable a greater understanding of the distribution of genetic potential for organohalide respiration throughout the class DEH. Conclusions This study provides a novel tool for the detection and study of the ecological distributions of DEH, and shows that the specific examination of DEH provides deep insights into the distributions, diversity and community structures of this important subsurface bacterial group that may be missed by other general microbial community surveys. We found indications that DEH are widely distributed in shallow marine sediments in addition to the deep subsurface, and must therefore be considered as important components of biogeochemical cycles in both the shallow and deep marine subsurface, especially on a global scale. Furthermore, the results reveal that diversity of DEH can be remarkably high within single-sediment samples, and that various subgroups co-exist yet change in relative proportions with depth in marine sediments. This strongly suggests different DEH subgroups exhibit varied metabolic properties that enable them to inhabit different ecological niches. This study therefore provides important baseline ecological data regarding the distributions of DEH and DEH subgroups. Such data will be particularly useful for placing into context findings of future research involving metagenomics or single-cell genomics related to DEH, or for providing rationale for future targeted cell-sorting/genomic studies. Experimental procedures Sample collection Marine sediment samples were collected from various cruises and the sampling details can be obtained from the references in Table 1. For other samples, details are provided here: Samples from the Greenlandic side of the Baffin Bay were collected in August–September 2010 during cruise ARK-XXV/3 of RV Polarstern using a gravity corer (Algora


Dehalococcoidia distribution and diversity et al., 2013) (details of supplementary experimental procedures for biogeochemical measurements are presented in the Supporting Information Appendix S1, and results are presented in Supporting Information Fig. S5); samples from Aarhus Bay, Denmark, were collected on two occasions, in March 2011 (‘Core A’) and November 2011 (‘Core B’), using a gravity core sampler (details of supplementary experimental procedures for biogeochemical measurements are presented in the Supporting Information Appendix S1). The uppermost section of Aarhus Core B was not recovered; samples from tidal flat sediments of the Wadden Sea, Germany, were collected in October 2010 using cut-off 60 ml syringes during low tide. In addition to marine samples, a small number of terrestrial samples were collected: a 2 L sample of groundwater was collected in July 2010 from a contaminated groundwater site at Bitterfeld, Germany, and filtered onto a 0.2 μM filter; a sample of freshwater pond sediment was collected from an onsite pond at the Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany, in August 2010, using a 15 ml Falcon tube; a sample of sediment was collected from a freshwater stream in Leipzig, Germany, in August 2010, using a 15 ml Falcon tube. All samples were frozen at −80°C prior to DNA extractions, except samples from Chile, which were stored at 4°C.

DNA extractions DNA was extracted from marine sediments using a FastDNA Spin for Soil Kit (MP Biomedicals) following the manufacturer’s instructions with the exception of the following modifications: For each sample, 0.8 g of sediment was weighed and added to the initial tube containing the beads, and 780 μl of sodium phosphate buffer was added. After the DNA-binding step, the silica matrix and bound DNA were allowed to settle for 30 min. Each sample was eluted in 50–100 μl of supplied DNA elution solution (DES) water. DNA extractions from the different cores were performed either in triplicates, or in five replicates, and combined. DNA was concentrated when necessary using a Microcon YM-100 device (Millipore). For Peru Margin and Porcupine Seabight sediments, DNA was extracted in five replicates, concentrated and re-eluted in a total of 60 μl of deionized water. For Black Sea and Sumatran sediments, DNA was extracted in triplicate, concentrated and eluted in 40 μl of deionized water. For Baffin Bay, Wadden Sea, Aarhus Bay and terrestrial sediments, DNA was extracted in triplicate, combined and was not concentrated. All DNA extracts were diluted 1:10 prior to PCR analysis to reduce concentrations of co-extractable organics that may be PCR inhibitors depending on their concentration and thereby improving PCR efficiencies. DNA was extracted from the filtered groundwater samples by cutting the filter into small pieces prior to extraction following the above-described procedures.

Design and testing of DEH-specific PCR primers The ‘probe design’ function of the ARB software package (http://www.arb-home.de/) (Ludwig et al., 2004), in conjunction with the ARB compatible SILVA database (Pruesse et al., 2007) release ‘SILVA 100’, was used to design primers that were biased towards amplification of 16S rRNA genes from the DEH. Further details are described in the Supporting

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Information Appendix S1. The DEH-targeted primers were denominated DEH-Fa (5′–TACGGGAGGCAGCAGCDA–3′) and DEH-R (5′–GRRAGGGTCGATACYCC–3′). The primers cover positions 315–790 of the unaligned 16S rRNA gene of Dehalococcoides mccartyi strain 195, giving an amplicon size of about 475 bp in various DEH sequences. This size was appropriate for both qPCR analysis and amplicon pyrosequencing. The optimized PCR conditions are described below.

Quantitative real-time PCR All real-time PCR measurements were conducted using an ABI Prism 7000 Sequence Detection System (Applied Biosystems). Real-time PCR for the quantification of total Bacteria was performed using primers 341f (5′–CCTAC GGGAGGCAGCAG–3′) and 534r (5′–ATTACCGCGGC TGCTGGCA–3′), which target highly conserved regions in a very broad range of bacterial 16S rRNA genes (Wang and Qian, 2009). The primers DEH-Fa and DEH-R developed in this study were used for the quantification of DEH. PCR reactions (final volume of 20 μl) contained 10 μl of 2 × SensiMix SYBR Kit PCR Master Mix (Bioline), 1 and 5 μM of each primer for bacterial and DEH assays respectively, 1.0 μl of DNA template and deionized water up to 20 μl. PCR cycling conditions included an initial ‘enzyme activation’ step at 95°C for 15 min, a short touchdown program over 5 cycles consisting of 95°C for 30 s, 65°C (-0.4°C per cycle until a final temperature of 63°C was reached) for 30 s and 72°C for 45 s, and this was followed by an additional 33 cycles of 95°C for 30 s, 63°C for 30 s and 72°C for 45 s. Acquisition of fluorescence signal was performed during the 72°C extension step of each cycle. Melt-curve analyses were performed after each run and PCR products were analysed by standard agarose gel electrophoresis if irregularities in melt-curves were observed. The DNA standard used in real-time PCR assays for both DEH and Bacteria consisted of a serial dilution of purified PCR product derived from a cloned DEH 16S rRNA gene retrieved from sediments off the coast of Chile in a clone library previously generated in our laboratory using the general bacterial primers 27f and 1492r (Lane, 1991). This gene sequence was PCR-amplified directly from a colony using M13 vector-specific primers, checked using standard agarose gel electrophoresis, extracted and gel-purified using a Wizard SV Gel and PCR Clean-Up Kit (Promega) according to the manufacturer’s instructions. DNA concentrations were determined using a NanoDrop ND1000 (NanoDrop Technologies) in triplicate. Measured concentrations of purified PCR product were then converted to copies per microlitre, and the concentration was adjusted to 1 × 1011 copies μl-1 prior to performing 10-fold serial dilutions. A standard curve (1 × 106 to 1 × 102 copies per reaction) was generated and included in each run in triplicate. A dynamic range of 1 × 102 to 1 × 106 copies per reaction was determined for this assay, and therefore a detection limit was set at 1 × 102 copies per reaction. Data and copy numbers were analysed using the real-time PCR systems accompanying software (STEPONE version 2.0, Applied Biosystems) following the manufacturer’s guidelines. As a further control, we optimized the method using genomic DNA isolated from Dehalococcoides mccartyi strain


3552 K. Wasmund et al. CBDB1 to ensure that both general bacterial and DEH realtime PCR assays produced highly similar values for DNA concentrations within the dynamic range of the DEH and bacterial assays, i.e. values of ± 10% from each other. As a technical note, we found it necessary to store the primers targeting DEH in single-use aliquots at −80°C to ensure valid real-time PCR efficiency values.

PCR for amplicon generation and 454 pyrosequencing For 454 pyrosequencing, the DEH specific primers were extended as ‘fusion’ primers with respective A and B adapters, the key sequence and multiplex identifiers (MID) as recommended by 454/Roche (http://454.com/productssolutions/experimental-design-options/amplicon-sequencing .asp). PCR reactions contained 1 × Phusion HF Buffer (Finnzymes), 0.5 U of Phusion High-Fidelity DNA Polymerase (Finnzymes), 200 μM of each dNTP, 5 μM of each primer, 1.0 μl of DNA template and deionized water up to 20 μl. PCR cycling conditions included an initial denaturation step at 98°C for 30 s, a short touchdown program over five cycles consisting of 98°C for 10 s, 65°C (−0.4°C per cycle until a final temperature of 63°C was reached) for 30 s and 72°C for 30 s, and this was followed by an additional 33 cycles of 98°C for 10 s, 63°C for 30 s and 72°C for 30 s. Five replicate PCRs were performed for each sample analysed. These were then pooled, analysed by agarose gel electrophoresis, and the bands of expected size were excised from gels and purified using a Wizard SV Gel and PCR Clean-Up Kit (Promega). Emulsion PCR, emulsion breaking and sequencing were performed using the GS FLX Titanium chemistry following manufacturer’s protocols and using a 454 GS FLX pyrosequencer (Roche), as recommended by the developer. Quality filtering of the pyrosequencing reads was performed using the automatic amplicon pipeline of the GS Run Processor (Roche).

Bioinformatic quality control of 454 pyrosequence data Quality control of pyrosequence data generated in this study followed a general pipeline outlined previously (Schloss et al., 2011) (http://www.mothur.org/wiki/Schloss_SOP) using the MOTHUR software version 1.24.0. Processing of data included removal of sequences with more than two mismatches to the primer or one mismatch to the MID. Sequences were then subjected to the ‘shhh.flows’ command with default settings for GS FLX Titanium chemistry to remove ‘noise’ caused by PCR and sequencing errors. The ‘shhh.flows’ command is the MOTHUR implementation of AmpliconNoise (Quince et al., 2009). Barcode and primer sequences were removed from the sequences. Sequences with homopolymers of more than eight bases were removed. Sequence processing implicit within the shhh.flows, screen.seqs and filter.seqs steps resulted in trimming the sequences to a final length of about 270 bp, mainly from the 3′ ends, which is essential for reducing erroneous base calls that are more prevalent towards the 3′ ends of 454 pyrosequence reads. The data was then subjected to ‘single-linkage pre-clustering’ (Huse et al., 2010), using the ‘pre.cluster’ command with a ‘diff = 2’ setting. Error rates as low as 0.0002 have been estimated using the abovedescribed sequence processing protocols (Schloss et al.,

2011). Chimeric sequences were then detected using the ‘chimera.uchime’ command, which is the MOTHUR implementation of UCHIME (Edgar et al., 2011). Chimeric sequences were detected de novo by exploiting abundance data in each sample set, and chimeric sequences were removed. Quality control of pyrosequence data generated from a previous study of bacterial and archaeal 16S rRNA genes that were PCR-amplified from sediments of the Arctic MidOcean Ridge and sequenced using 454 pyrosequencing was performed differently to describe for the data generated in this study (Jorgensen et al., 2012). Quality control for Arctic Mid-Ocean Ridge samples was performed without the shhh.flows command because of deposition of only fastq files in the GenBank database. The fastq files were instead processed with the trim.seqs command within MOTHUR, in which sequences were discarded if they had > 2 differences to the primer/barcode, if they had > 1 ambiguous base call, if they had homopolymers of > 8 bases and if they were < 250 bp long.

Bioinformatic processing for DEH alpha diversity and community structure analyses Prior to sequence processing for alpha diversity and community structure analyses, non-DEH sequences were removed from all datasets. This was done by first running the ‘class.seqs’ command using an updated taxonomic reference database that included sequences from our custom taxonomic outline of the DEH subgroups (further information is detailed below), in addition to other non-target bacterial sequences included in the Ribosomal Database Project (RDP) reference files. Finally, DEH sequences were extracted from the datasets using the ‘get.lineage’ command. Examination of taxonomic classifications of non-target sequences revealed most belonged to the phyla Firmicutes or Actinobacteria. The final sequences encompassed the whole variable region 3 and ended in the variable region 4 of the 16S rRNA gene. Taxonomic assignments of pyrosequence reads were performed using the RDP-naïve Bayesian rRNA Classifier (Wang et al., 2007; Claesson et al., 2009), as implemented in MOTHUR. We updated the RDP reference files (version 6; http://www.mothur.org/wiki/Taxonomy_outline) with sequences from DEH subgroups that were determined after phylogenetic analyses. Phylogenetic analyses were performed with DEH sequences obtained from the SILVA 111 database using NJ and ML analyses using MEGA5 (version 5.10) (Tamura et al., 2011). The sequences subjected to phylogenetic analyses were obtained by exporting SILVAaligned DEH sequences from ARB ‘as aligned’, then ‘GapOnly Columns’ were removed, and the ends of the alignment were trimmed so all sequences had the same starting and end positions. Since the basis for our DEH phylogeny is based on the ML method, the following details are reported for ML analysis: The evolutionary history was inferred based on the General Time Reversible model, and a discrete Gamma distribution was used to model evolutionary rate differences among sites [two categories (+ G, parameter = 0.5173)]. All positions with less than 95% site coverage were eliminated from the analysis. There were a total of 1177 positions in the final dataset. DEH subgroups were manually defined based on major branching points within the obtained


Dehalococcoidia distribution and diversity trees that were consistent between the two methods. An alternative approach was also attempted in which subgroups were defined based on genetic relatedness, i.e. sequences were grouped into OTUs. This approach had the drawback that when sequences determined to belong to the ‘same’ OTU were subsequently identified in phylogenetic trees, they were often positioned in different clades within the tree. This indicated OTU-defining algorithms did not accurately take into account phylogenetic information and that this approach was therefore not suitable for this purpose. Therefore for the purpose of this study, in which first insights into possible distributional changes of major DEH subgroups within sediments was to be investigated, we deemed our approach of defining subgroups based on major branch points as appropriate. The accuracy of the pyrosequence classification and binning approach was confirmed by extracting sequences classified into selected subgroups, followed by treeing using parsimony analyses within ARB and then examining the placement of the sequences in relation to the reference sequences used within the taxonomic reference files. This testing approach revealed the classification accuracy was consistent when the lengths of the query sequences were longer than 250 bp. The final sequences used were around 270 bp in length. Rarefaction analyses (Heck et al., 1975), Ace and Chao1 non-parametric richness estimates (Chao, 1984) were generated using MOTHUR with an operational taxonomic unit (OTU) cut-off value of 0.03. Fast UniFrac was used to generate hypotheses about the dissimilarities of DEH ‘communities’ (Hamady et al., 2010). Datasets for input into Fast UniFrac were normalized by randomly sub-sampling to the number of the lowest amount of reads per sample set (i.e. for each separate sediment core). Sequences were aligned with stand-alone muscle version 3.8.31 (Edgar, 2004) with the flags ‘-maxiters 1 -diags’. Trees were generated from the resulting alignments using FastTree version 2.1.5 using the GTR+CAT model (Price et al., 2010). The trees were then ‘midpoint rooted’ within MEGA5. This data was then uploaded into the web-based Fast UniFrac for analyses (http:// unifrac.colorado.edu/).

Sequence accession number Nucleic acid sequences determined in this study have been deposited in the EMBL/GenBank/DDBJ databases. The accession number for the sequence archive in the European Nucleotide Archive (ENA) is ERP002131.

Acknowledgements We thank Axel Schippers (BGR, Hannover, Germany), the ODP/IODP, the Geomicrobiology Group, University of Aarhus, Denmark, and Marie Schmidt, Myriel Cooper and Christina Lachmann (Helmholtz Centre for Environmental Research, Leipzig, Germany) for providing help with obtaining samples. We are grateful to Benjamin Scheer for technical support and Doris Sonntag for performing TOC measurements (Helmholtz Centre for Environmental Research, Leipzig, Germany). This work was financially sup-

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ported by the European Research Council (ERC), Grant Number 202903 – MICROFLEX.

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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. Phylogenetic tree exported directly from ARB showing major DEH subgroups given in the SILVA database (release 115). The scale bar represents 10% sequence divergence. Fig. S2. Phylogenetic tree showing affiliations of DEH 16S rRNA gene sequences determined by Sanger sequencing of randomly picked DEH 16S rRNA gene clones. The tree was constructed in ARB using Randomized A(x)ccelerated Maximum-Likelihood analysis (RAxML) of DEH 16S rRNA genes (i.e. > 1170 bp) derived from the SILVA 111 database, with partial DEH sequences being inserted by the parsimony option. It is important to note that the exact same clustering of sequences as displayed in Fig. 1 in the main text was not achieved because of the different algorithm used for the supplementary figure, i.e. RAxML (a ‘fast’ implementation of the ‘full’ Maximum-Likelihood algorithm). This was most pronounced in branches of the tree which multifurcated in the tree of Fig. 1 in the main text, and these branches are coloured in black. Sequence of cultured DEH and sequences derived from other marine-DEH implicated in organohalide respiration by stable isotope probing (SIP) or enrichment experiments are highlighted in red. Sequences derived from single-cell genome ‘DEH-J10’ and an aquifier sediment metagenome-derived genome ‘RGB-2’ are also highlighted in red. The 16S rRNA gene sequence of RGB-2 was edited to match the closest aligned relative in the middle of the gene sequence where a stretch of five N’s were observed. The scale bar represents 10% sequence divergence. Fig. S3. Rarefaction curves for DEH diversity as determined by pyrosequencing of 16S rRNA genes. OTU thresholds were determined at a 97% sequence identity cut-off. Fig. S4. Depth profiles of sulfate (blue) and methane (red) concentrations in Aarhus Bay cores A and B. Fig. S5. Depth profiles of total organic carbon (TOC), sulfate and methane in cores from the Baffin Bay. The data is sourced from Algora et al. 2013. Table S1. Primer coverage summary for DEH-Fa. Table S2. Primer coverage summary for DEH-R. Appendix S1. Supplementary methods.


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