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Illuminating Microbial Dark Matter in Meromictic Sakinaw Lake Esther A. Gies,a Kishori M . K o n w ar ,3 J. T hom as B eatty ,3 S teven J. H a lla m 3'b’c D epartm ent o f M icro biolog y and Im m unology, University o f British Colum bia, Vancouver, British Colum bia, Canada8; Genom e Science and Technology Program, University o f British Colum bia, Vancouver, British Colum bia, Canada15; Graduate Program in Bioinformatics, University o f British Colum bia, Vancouver, British Colum bia, Canada8
Despite recent advances in metagenomic and single-cell genomic sequencing to investigate uncultivated microbial diversity and metabolic potential, fundamental questions related to population structure, interactions, and biogeochemical roles of candidate divisions remain. Numerous molecular surveys suggest that stratified ecosystems manifesting anoxic, sulfidic, and/or methanerich conditions are enriched in these enigmatic microbes. Here we describe diversity, abundance, and cooccurrence patterns of uncultivated microbial communities inhabiting the permanently stratified waters of meromictic Sakinaw Lake, British Colum bia, Canada, using 454 sequencing of the small-subunit rRNA gene with three-domain resolution. Operational taxonomic units (OTUs) were affiliated with 64 phyla, including more than 25 candidate divisions. Pronounced trends in community structure were observed for all three domains with eukaryotic sequences vanishing almost completely below the mixolimnion, followed by a rapid and sustained increase in methanogen-affiliated (—10%) and unassigned (-60% ) archaeal sequences as well as bacterial OTUs affiliated with Chloroflexi (-22% ) and candidate divisions (-28% ). Network analysis revealed highly correlated, depthdependent cooccurrence patterns between Chloroflexi, candidate divisions WWE1, OP9/JS1, OP8, and OD1, methanogens, and unassigned archaeal OTUs indicating niche partitioning and putative syntrophic growth modes. Indeed, pathway reconstruction using recently published Sakinaw Lake single-cell genomes affiliated with OP9/JS1 and OP8 revealed complete coverage of the Wood-Ljungdahl pathway with potential to drive syntrophic acetate oxidation to hydrogen and carbon dioxide under methanogenic conditions. Taken together, these observations point to previously unrecognized syntrophic networks in meromictic lake ecosystems with the potential to inform design and operation of anaerobic methanogenic bioreactors.
ver the past 2 decades, cultivation-independent approaches
O have identified at least 60 major branch points (phyla or di visions) in the bacterial and archaeal domains of life (1). Approx imately half of these branch points represent candidate divisions with no known cultivated representatives, so-called microbial dark matter (MDM) (2). Emerging lines of evidence suggest that bacteria affiliated with BD1-5, OP11, OP9/JS1, OP1, and WWE1 candidate divisions harbor genes encoding components of fer mentation, hydrogen or sulfur metabolic pathways supporting cometabolic or syntrophic growth modes under anaerobic condi tions (3-6). Consistent with this, a recent single-cell genomic study of OP9 bacteria from hot spring sediments suggested a de pendence on exogenous vitamins sourced from surrounding mi crobial community members (3). Such public good dynamics could represent a common organizing principle in structuring microbial community interaction networks (7, 8) and help ex plain the resistance of most environmental microorganisms, in cluding candidate divisions to clonal isolation (7, 9). While advances in metagenomic and single-cell genomic se quencing have begun to open a window on the metabolic potential of many candidate divisions, fundamental questions relating to their population structure, interactions, and biogeochemical roles remain. Recent molecular surveys indicate that natural and engi neered ecosystems with anoxic, sulfidic, and/or methane-rich conditions tend to harbor a diversity of candidate divisions (6, 10-12). Among ecosystems manifesting these biogeochemical conditions, permanently stratified meromictic lakes provide trac table models in which to study MDM population structure, func tion, and dynamics. In these aquatic ecosystems, the water column partitions into oxic fresh surface waters referred to as the mix olimnion, a redox transition zone, and anoxic, often hydrogen
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sulfide (H2S)- and methane (CH4)-rich bottom waters, referred to as the monimolimnion (13, 14). Previous studies have identified MDM in geographically iso lated meromictic lakes with different degrees of spatial and taxo nomic resolution. For example, small-subunit (SSU) rRNA gene clone library sequencing in Lake Pavin in France identified at least five bacterial candidate divisions, including OP1, OP3, OPIO, OP11, and WS5 that partitioned between the redox transition zone and monimolimnion (15). A more recent study using a com bination of SSU rRNA gene clone library sequencing and PhyloChip hybridization in Mahoney Lake in Canada identified at least eight bacterial candidate divisions, including OP1, OP8, OP9/JS1, OP11, TM6, WS1, WS3, and ZB2 (16). A higherthroughput study in Arctic Lake A identified SSU rRNA gene pyrotag sequences affiliated with at least four candidate divisions, including OP3, OP8, OP9, and OP 11 that increased in abundance within the monimolimnion (17). Here we describe the microbial community inhabiting Saki naw Lake, a meromictic lake, on the Sunshine Coast of British
Received 29 M ay 2014 A c c e p te d 22 A u g ust 2014 P ublished ahead o f p rin t 29 A ugust 2014 E ditor: F. E. Loffler Address correspondence to Steven J. Hallam, shallam@ mail.ubc.ca. Supplem ental m aterial fo r this article m ay be fo u n d at http://dx.doi.O rg/10.1128 /AEM.01774-14. C opyright © 2014, Am erican Society fo r M icrobiology. All Rights Reserved. doi: 10.1128/AEM .01774-14 The authors have paid a fee to allo w im m ed iate free access to this article.
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Columbia, Canada, using a pyrosequencing approach targeting the SSU rRNA gene, with specific emphasis on describing the pop ulation structure of MDM across defined redox transition zones. We apply hierarchical clustering and indicator species analyses to identify operational taxonomic units (OTUs) differentially asso ciated with the mixolimnion, upper and lower part of the transi tion zone, and monimolimnion in relation to environmental pa rameter data. Network analysis is then used to chart potential interactions between prevalent OTUs, including many candidate divisions. Metabolic reconstruction based on recently published near-complete Sakinaw Lake single-cell amplified genome se quences reinforces network connectivity patterns, revealing puta tive syntrophic relationships among candidate divisions and between candidate divisions and other microbial community members (2). MATERIALS AND METHODS Sampling. Water samples from Sakinaw Lake, British Columbia, Canada, were taken at deep basin station SI (49°40.968'N 124°00.119'W) using a combination of 12-liter Niskin and 8-liter GO-FLO bottles on 6 June 2007, 23 October 2007, 21 May 2008, 5 August 2009, 5 January 2010, 27 January2011,and 24 May 2011 (seeTableSl in th e supplemental mate rial). A total of 66 samples were collected from different depths covering the mixolimnion (5 m to 30 m), transition zone (33 m to 55 m), and monimolimnion (60 m to 120 m). Conductivity temperature and depth were measured with a Sea-Bird SBE19 CTD device measuring conductiv ity, temperature, and depth (Sea-Bird Electronics Inc., Bellevue, WA, USA). Samples were kept at 4°C in the dark and subsequently processed for environmental DNA extraction. Additionally, transition metal, sulfide (H2S), and sulfate (S042 -) concentrations were determined for samples collected on 24 May 2011. Environmental DNA. Approximately 6 h after sample collection, 2 liters of water was filtered through a 0.22-p.m Sterivex GV filter (Millipore) without a prefilter using a Masterflex L/S 7553-70 peristaltic pump (Cole-Parmer) for DNA extraction. DNA was extracted from Sterivex filters by the method of Zaikova and colleagues (18) and DeLong and colleagues (19). The DNA extraction protocol can be viewed as a visual ized experiment at http://www.jove.com/video/1352/ (20). Enumeration of cells by flow cytometry. Enumeration of total cells was performed by the method of Zaikova and colleagues (18). Briefly, water samples were fixed in 4% (wt/vol) formaldehyde and stored at 4°C in the dark for approximately 18 h prior to processing for flow cytometry. Nucleic acids were stained with SYBR green (Invitrogen), and cells were counted with a fluorescence-activated cell sorting (FACS) LSRII flow cy tometer equipped with an air-cooled argon laser (Becton Dickinson). Cell counts were estimated using a known concentration of 6-p.m fluorescent beads (Invitrogen). Chemical profiling. All salinities are on the TEOS-10 reference com position salinity scale, with the salinity anomaly assumed to be zero (21). Dissolved oxygen concentrations were determined by Winkler titrations (22). Hydrogen sulfide concentration was measured from water samples fixed with 2% final concentration of zinc acetate and analyzed in the lab using the methylene blue method of Cline et al. (23). The concentrations of transition metals, including iron (Fe), manganese (Mn), and arsenic (As), and S042- were determined at Maxxam Analytics (Burnaby, British Columbia, Canada) using Standard Methods for the Examination o f Water and Wastewater published by the American Public Health Association (24). Sulfate was determined with automated colorimetry according to the standard protocol SM 4500 S 0 42- within 24 h after sample collection (24). Samples for dissolved fractions of Fe, Mn, and As were preserved with nitric acid (H N 03) and filtered through a 0.45-p.m membrane filter prior to analysis with inductively coupled plasma mass spectrometry based on standard protocol EPA 200.8 (25) within 14 days of sampling. Dissolved methane (CH4) was measured from 13 samples (water depths
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of 5 m, 10 m, 25 m, 30 m, 33 m, 36 m, 40 m, 45 m, 50 m, 55 m, 60 m, 80 m, and 120 m) collected in June 2007 by gas chromatography coupled to mass spectrometry (GC-MS) using the static headspace equilibrium tech nique of Zaikova et al. (18). As CH4 concentrations in the monimolim nion of Sakinaw Lake exceed detection limits of the applied method, our data are estimates and indicate that CH4 concentrations in the deep Saki naw Lake waters exceed atmospheric saturation values. PCR amplification of SSU rRNA gene for pyrotag sequencing. Envi ronmental DNA extracts described above were amplified using previously published three-domain primers targeting the V6-V8 region of the SSU rRNA gene (26): 926F (5'-cct ate ccc tgt gtg cct tgg cag tet cag AAA CTY AAA KGA ATT GRC GG-3') and 1392R (5'-cca tet cat ccc tgc gtg tet ccg act cag-XXXXX-ACG GGC GGT GTG TRC-3')- Primer sequences were modified by the addition of 454 A or B adapter sequences (shown in lowercase type). In addition, the reverse primer included a 5-bp bar code designated XXXXX for multiplexing of samples during sequencing. Twenty-five microliter PCRs were performed in triplicate and pooled to minimize PCR bias. Each reaction mixture contained between 1 and 10 ng of target DNA, 0.5 p.1 Taq DNA polymerase (Bioshop Inc., Canada), 2.5 p.1 Bioshop 10X buffer provided in the Bioshop Taq-polymerase kit, 1.5 pi of 25 mM Bioshop MgCl2, 2.5 pi of 10 mM deoxynucleoside triphos phates (dNTPs) (Agilent Technologies), and 0.5 pi of 10 mM (each) primer. The thermal cycler protocol started with an initial denaturation at 95°C for 3 min and then 25 cycles, with 1 cycle consisting of 30 s at 95°C, 45 s at 55°C, 90 s at 72°C, and 45 s at 55°C. The final step, an extension step, was 10 min at 72°C. PCR products were purified using the QIAquick PCR purification kit (Qiagen), eluted in 20 mM Tris (pH 8), quantified using the Quant-it Picogreen dsDNA reagent. SSU rRNA amplicons were pooled at 30 ng DNA for each sample. Emulsion PCR and sequencing of the PCR amplicons were performed at the McGill University and G6nome Quebec Innovation Center on a Roche 454 GS FLX titanium platform according to the manufacturer’s instructions. Pyrotag sequence analysis. Pyrotag sequences were analyzed using the Quantitative Insights Into Microbial Ecology (QIIME) software pack age (27). To minimize the removal of false-positive singleton OTUs, 901,664 pyrotag sequences generated from 66 samples collected in Saki naw Lake between 2007 and 2011 were clustered together (see Table SI in the supplemental material). Reads with length shorter than 200 bases, ambiguous bases, and homopolymer sequences were removed prior to chimera detection. Chimeras were detected and removed using the chi mera slayer provided in the QIIME software package. Sequences were then clustered at 97% identity using uclust with furthest linkage algo rithm. Prior to taxonomic assignment, singleton OTUs (OTUs repre sented by one read) were omitted, leaving 23,231 OTUs (Table S2). To generate an OTU table specific for the May 2011 data set, the filter_otus_ by_sample.py script was used, leaving 181,464 sequences and 12,908 OTUs for downstream analysis. The average number of reads per sample in the Sakinaw Lake May 2011 data set was 16,323, with the exception of the 33-m-deep sample, which had ~50% fewer reads. Representative se quences from each nonsingleton OTU were queried against the SILVA database release 111 (28) and the Greengenes database (29) using BLAST (30). Overall, the comparison between SILVA and Greengenes databases revealed similar taxonomic assignments for bacterial OTUs. However, significant differences for archaeal OTU assignments were observed be tween the two databases. Approximately twice as many reads were as signed to Methanomicrobia using the Greengenes database than when the SILVA database was used. This difference could be mapped back to a single abundant OTU, approaching 10% of total sequences at a depth of 45 m that was assigned to Methanomicrobiales using the Greengenes da tabase and to Halobacteriales using the SILVA database. BLAST-based comparisons indicated that sequences comprising this OTU shared ~90% identity with Halobacteriales reference sequences in SILVA and 90% identity with Methanomicrobiales reference sequences in Green genes. Moreover, a number of putative archaeal OTUs associated with the
Applied and Environmental M icrobiology
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redox transition zone and m onim olim nion (depths of 33 m to 120 m) were identified with “no blast hits” using Greengenes b u t assigned to Halobacteriales using SILVA. Because both databases are not well anno tated for Halobacteria (31), the Sakinaw Lake data set was m apped onto the full-length Halobacteria SSU rRNA gene database generated by Youssef and colleagues (31) using the program CD -H IT (cd-hit-est-2d) (32). The data set could not be clustered with the Halobacteria reference sequences at 99%, 97%, or 95% identity. Even for 90% sequence identity, only 981 reads (0.5%) clustered with the Halobacteria reference se quences. Given these uncertainties in putative archaeal OTU affiliation, we designated them “unassigned Archaea” until full-length sequences or reference genomes becom e available to support m ore-in-depth phyloge netic analysis. Statistical analyses. M icrobial com m unity richness was determ ined using scripts im plem ented in the QIIM E package. OTU tables were rar efied starting with 10 sequences to a m axim um o f 6,000 sequences. Ten iterations per sam ple were calculated with 100 sequences between each step. Hierarchical cluster analysis o f m icrobial com m unity compositional profiles and environmental parameters was conducted in the R statistical environm ent (Development Core Team, 2011; http://www.R-project.org/) using M anhattan distance measures for clustering microbial comm unity compositional profiles (33) and Euclidean distance for environmental pa rameters (34). Prior to analysis, pyrotag data sets were normalized to the total num ber of reads per sample, and environmental parameter data were trans formed to the same order o f magnitude so that each variable had equal weight. Multilevel indicator species analysis (ISA) was perform ed to identify OTUs specifically associated with different water colum n com partm ents (m ixolim nion [5 m, 20 m ,an d 30 m ], upper part of the transition zone [33 m, 36 m, 40 m, and 45 m ], lower part o f the transition zone [50 m and 55 m ], and m onim olim nion [60 m, 80 m, and 120 m [). Dufrene and Leg endre established a m ethod to determ ine an indicator species by its relat edness to a user-defined environm ent, e.g., group o f samples, where each species is treated individually, and its indicator value is determ ined ac cording to its abundance value (35). De Caceres and colleagues recently extended the algorithm for ISA to include a multilevel pattern analysis (36). Arguing that species with higher adaptability for different environ m ental factors are indicative for specific environm ent com binations, this analysis additionally determ ines indicator species for com binations of environm ents. The ISA/multilevel pattern analysis calculates P values with M onte Carlo sim ulations and returns indicator values (IVs) and values with a :£ 0.05. The IVs fall between zero and one, where one is considered a true indicator. Cooccurrence network. To generate a robust network emphasizing cooccurrences betw een prevalent O TUs in w ater colum n c o m p a rt m ents ra th e r th an individual dep th intervals, S pearm an’s rank co rre lation was used. S pearm an’s rank correlation coefficients were calculated using a custom perl script, “correlation_netw ork.pl” (https://github.com /hallam lab/utilities/tree/m aster/correlation_netw ork).T he initial data set consisted of 12,900 OTUs. To simplify the network, we retained OTUs with at least 10 reads appearing in at least three samples leaving 1,528 OTUs with Spearm an’s rank correlations equal to or greater than 0.99. The resulting cooccurrence netw ork contained 130,101 edges, each with a positive correlation. The netw ork was visualized with a force-directed layout, using Cytoscape 2.8.3 (37). Network properties were calculated with the “Network Analysis” plug-in. Nodes in the cooccurrence netw ork corresponded to individual OTUs, and edges were defined by com puted correlations between corresponding O TU pairs. The layout revealed four distinct m odules, which persisted after lowering the correlation coeffi cient cutoff for edge creation to 0.90, reinforcing the robustness o f the network. Pathway reconstruction. To evaluate the metabolic potential for syn trophic acetate oxidation (SAO) am ong highly connected candidate divi sions, publicly available single-cell genomes from Sakinaw Lake affiliated with OP9/JS1, OP8, and WWE1 were searched for coverage o f the W ood-
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Ljungdahl pathw ay using the Joint Genom e Institute integrated m icrobial genomes expert review portal (IMG-ER) (http://im g.jgi.doe.gov/). Nucleotide sequence accession number. The sequences reported in this study have been deposited in the NCBI BioProject database (www .ncbi.nlm .nih.gov/bioproject) u n d e r B ioProject accession no. PRJNA 257655 (identification no. [ID] 257655). RESULTS
Site location and physicochemical properties. S ak in aw Lake is a p e rm a n e n tly stra tifie d lake lo c a te d o n th e S u n s h in e C o a st o f B rit ish C o lu m b ia , C a n a d a (4 9 °4 0 .8 'N , 1 2 4 °0 0 .3 9 'W ) a t a n e le v atio n o f ~ 5 m a b o v e sea level. O rig in a lly a fjo rd o p e n in g to th e S tra it o f G e o rg ia, S a k in aw L ake w as a lm o st c o m p le te ly iso late d fro m th e o c ea n d u e to c o asta l u p lift ~ 11,000 y ears ago fo llo w in g th e last ice age (38). F o r th o u s a n d s o f years, a sm all s tre a m n a m e d S ak in aw C re ek w as th e o n ly c o n d u it b e tw e e n S a k in aw Lake a n d th e S tra it o f G eo rg ia. In 1952, S a k in aw C re e k w as d a m m e d to b e tte r m an a g e w a te r levels fo r d e v e lo p m e n t p ro je c ts in th e s u r r o u n d in g w a te r sh e d p re v e n tin g se a w ate r ingress. T h e lake c o n sists o f tw o basins: a relatively sh a llo w fre sh w a te r b a sin 49 m d e e p a n d a m o re e x te n sive sa lt stra tifie d b a sin 140 m d e e p (39). A g ra d u a l sa lin ity i n crease in th e tr a n s itio n z o n e is fo llow ed b y a series o f th re e < 0 .5 g /kg sa lin ity ste p s p u n c tu a tin g th e m o n im o lim n io n . M e a s u re m e n ts o f a b so lu te sa lin ity (SA), te m p e ra tu re , a n d o x ygen b e tw e e n 2007 a n d 2011 re v ea le d a re m a rk a b le s ta b ility in th e sa lin ity g ra d ie n t t h r o u g h o u t th e w a te r c o lu m n as w ell as c o n sis te n t te m p e ra tu re s b e lo w a d e p th o f 36 m (see T ab le S3 in th e s u p p le m e n ta l m a te ria l). T e m p e ra tu re a n d o x y g e n ( 0 2) p ro files in th e m ix o lim n io n c h a n g e d b e tw e e n d iffe re n t sa m p lin g tim e p o in ts , w h ic h w as e x p ec te d d u e to sea so n al in flu en ces. T o d e te r m in e th e a v ailab ility o f te rm in a l e le c tro n a c c e p to rs (T E A s) fo r m ic ro b ia l e n e rg y m e ta b o lism in d iffe re n t w a te r c o lu m n c o m p a r t m e n ts , 0 2, iro n (F e), m a n g a n e s e (M n ), a rse n ic (A s), a n d S 0 42 m e a s u re m e n ts w ere p lo tte d as a fu n c tio n o f d e p th a n d c o m p a re d to m e a s u re m e n ts o f SA, su lfid e ( H 2S), a n d to ta l b a c te ria l cell c o u n ts (Fig. 1). T h e re su ltin g p rofiles w ere th e n c o m p a re d to g e o g ra p h ic a lly d is tin c t m e ro m ic tic lakes to d e te rm in e c o m m o n b a se lin e c o n d itio n s (T ab le S4). T h e m ix o lim n io n in S a k in aw Lake (5 m to 30 m ) w as c o n s titu te d o f e n tire ly fresh , o x y g e n -ric h w a ter. In th e tr a n s itio n z o n e (30 m to 55 m ), sa lin ity a n d th e c o n c e n tra tio n o f a lte rn a tiv e TEA s, in c lu d in g Fe, M n , a n d S 0 42 - g ra d u a lly i n crea se d , w h e rea s 0 2 c o n c e n tra tio n s d e cre ased . T h e o x ic -a n o x ic in te rfa c e w as lo c a te d a t a d e p th o f 33 m . M e th a n e c o n c e n tra tio n s , w h ic h w e re d e te rm in e d fo r sa m p le s c o lle cted in Ju n e 2007 i n c rea se d b e lo w 33 m a n d re a c h e d s a tu ra tio n a t 45 m (d a ta n o t sh o w n ), fo rm in g a su lfa te m e th a n e tra n s itio n z o n e (S M T Z ). P eak c o n c e n tra tio n s o f Fe, M n , a n d S 0 42~ w ere o b se rv e d in th e m ic ro m o la r ra n g e (8.1 p,M , 4.2 |xM , a n d 57.5 |xM , resp ectiv ely ) a t a d e p th o f 36 m a n d c o rre s p o n d e d w ith in c re a se d m ic ro b ia l cell a b u n d a n c e ( ~ 2 .8 X 105 cells/m l). Sulfide c o n c e n tra tio n s in th e lo w e r p a rt o f th e re d o x tra n s itio n z o n e a n d th e m o n im o lim n io n (50 m to 120 m ) w e re h ig h (4.5 m M ) a n d re su lte d in th e re m o v a l o f so lu b le Fe fro m th e w a te r c o lu m n b y p re c ip ita tio n in to iro n su lfid e (FeS) a n d p y rite (FeS2) (40). T h e p e a k c o n c e n tra tio n o f As w as in th e n a n o m o la r ra n g e (93.4 n M ) a t a d e p th o f 60 m .
Relationship between community structure and physico chemical characteristics. T o b e tte r u n d e rs ta n d h o w th e p h y sic o c h e m ic a l p ro p e rtie s o f th e S a k in aw L ake w a te r c o lu m n in flu e n c e m ic ro b ia l c o m m u n ity d iv ersity , w e p e rfo rm e d 454 p y ro se q u e n c in g o f th e SSU rR N A gene w ith th re e -d o m a in re so lu tio n . Al-
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200 reads in the time series consistent with the presence of conditionally rare taxa in the Sakinaw Lake water column (see Table S2 in the supplemen tal material) (41). Approximately 75% of the organisms in the May 2011 data set were assigned to the domain Bacteria, and 25% of these were as signed to candidate divisions. Collectively, bacterial OTUs con tained the majority of pyrotag reads in all but the 45-m-deep sam ple where almost 60% of the reads were assigned to the domain Archaea (Fig. 2A). Eukaryotes, although prevalent in oxygenated surface waters, vanished almost completely below the mixolim
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nion. Richness estimates based on count data indicated that the mixolimnion had fewer OTUs than samples from the transition zone and monimolimnion (Fig. 2B). This pattern was consistent for all samples collected between 2007 and 2011 (see Fig. SI in the supplemental material). Hierarchical clustering of the microbial community composi tion profiles and selected environmental parameter data (temper ature, TEAs, and H2S) reflected the stratified nature of the lake ecosystem (Fig. 2C). The resulting dendrograms revealed identical clustering patterns between OTU abundance and environmental parameters for almost all depth intervals. Both dendrograms sug gest that water column partitioning in Sakinaw Lake did not easily conform to a three-compartment meromixis model, i.e., mix olimnion, redox transition zone, and monimolimnion. This was not unexpected, as the division of the water column into three compartments is based solely on hydrodynamic conditions and does not reflect partitioning of the water column into distinct environmental niches. On the basis of these observations, we op-
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FIG 2 Relationship between community structure and physicochemical characteristics of the Sakinaw Lake water column. (A) Bacterial operational taxonomic units (OTUs) contained the majority of pyrotag reads in all samples but the sample taken at a depth of 45 m, where almost 60% of the reads were assigned to Archaea. Eukaryotes vanished almost completely below the mixolimnion. (B) Richness estimates based on count data indicated that the mixolimnion had fewer OTUs than samples from the transition zone and monimolimnion. (C) Hierarchical clustering of the OTU abundance and environmental parameter data (temperature, S„, TEAs, and H2S).
erationally divided the transition zone into an upper and lower part to more accurately reflect potential niche partitioning. To identify how OTUs partitioned between the mixolimnion, upper part of the transition zone, lower part of the transition zone, and monimolimnion, we conducted a four-way set difference analysis of the presence of OTUs (Fig. 3A). Approximately 1% of the OTUs were shared between all four water column compart ments, with the majority of OTUs exhibiting mutually exclusive distribution patterns (13% unique to the mixolimnion, 21% unique to the upper transition zone, 10% unique to the lower transition zone, and 21% unique to the monimolimnion) (Fig. 3A). Operational taxonomic units associated with a given water column compartment that are absent or rare in other compart ments are potential ecological indicators. To identify potential indicator OTUs, we performed a multilevel indicator species anal ysis. With this analysis we were able to determine indicator species for individual water column compartments and compartment combinations. Only 5 to 10% of the compartment-specific OTUs were identified as indicator species (Fig. 3B; see Fig. S2 in the supplemental material). Indeed, the majority of OTUs that were identified as compartment specific in the four-way set difference analysis exhibited fine-scale depth partitioning (Fig. 3C and Table S5). Indicators shared between compartments were also uncom mon with the exception of the lower part of the transition zone and monimolimnion. In these two compartments, —44% of the
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OTUs were identified as indicators. Overall, the majority of OTU indicators were affiliated with unassigned Archaea (25.63%) and candidate phyla (21.3%), followed by Chloroflexi (16.2%). For a detailed taxonomic breakdown of indicator OTUs, see Table S6. Taxonomic composition. To determine how the taxonomic composition of the microbial community was distributed within and between water column compartments, we plotted bacterial and archaeal OTUs based on relative abundance (Fig. 4). The presence of eukaryotes in Sakinaw Lake is described in the supple mental material (see Fig. S3 in the supplemental material). All taxa exhibiting intermediate abundance (>0.1%) or above are repre sented in Fig. 4 and also Fig. S3 in the supplemental material. However, for visualization purposes, only taxa >1% are shown to scale. The most abundant taxa in the mixolimnion were Alphaproteobacteria (11.8%) the majority of which were affiliated with SAR11 (9.7%), Betaproteobacteria (10%) mostly affiliated with Burkholderiales (8.1%), Bacteriodetes (13.2%), Actinobacteria (22.3%), Cyanobacteria (16.9%), Planctomycetes (8.3%), and Verrucomicrobia (1.1%) (Fig. 4A; see Table S2 in the supplemental material). Archaea were almost absent from the mixolimnion, al though Methanomicrobia were identified among members of the rare biosphere (
Illuminating Microbial Dark Matter in Meromictic Sakinaw Lake Esther A. Gies,a Kishori M . K o n w ar ,3 J. T hom as B eatty ,3 S teven J. H a lla m 3'b’c D epartm ent o f M icro biolog y and Im m unology, University o f British Colum bia, Vancouver, British Colum bia, Canada8; Genom e Science and Technology Program, University o f British Colum bia, Vancouver, British Colum bia, Canada15; Graduate Program in Bioinformatics, University o f British Colum bia, Vancouver, British Colum bia, Canada8
Despite recent advances in metagenomic and single-cell genomic sequencing to investigate uncultivated microbial diversity and metabolic potential, fundamental questions related to population structure, interactions, and biogeochemical roles of candidate divisions remain. Numerous molecular surveys suggest that stratified ecosystems manifesting anoxic, sulfidic, and/or methanerich conditions are enriched in these enigmatic microbes. Here we describe diversity, abundance, and cooccurrence patterns of uncultivated microbial communities inhabiting the permanently stratified waters of meromictic Sakinaw Lake, British Colum bia, Canada, using 454 sequencing of the small-subunit rRNA gene with three-domain resolution. Operational taxonomic units (OTUs) were affiliated with 64 phyla, including more than 25 candidate divisions. Pronounced trends in community structure were observed for all three domains with eukaryotic sequences vanishing almost completely below the mixolimnion, followed by a rapid and sustained increase in methanogen-affiliated (—10%) and unassigned (-60% ) archaeal sequences as well as bacterial OTUs affiliated with Chloroflexi (-22% ) and candidate divisions (-28% ). Network analysis revealed highly correlated, depthdependent cooccurrence patterns between Chloroflexi, candidate divisions WWE1, OP9/JS1, OP8, and OD1, methanogens, and unassigned archaeal OTUs indicating niche partitioning and putative syntrophic growth modes. Indeed, pathway reconstruction using recently published Sakinaw Lake single-cell genomes affiliated with OP9/JS1 and OP8 revealed complete coverage of the Wood-Ljungdahl pathway with potential to drive syntrophic acetate oxidation to hydrogen and carbon dioxide under methanogenic conditions. Taken together, these observations point to previously unrecognized syntrophic networks in meromictic lake ecosystems with the potential to inform design and operation of anaerobic methanogenic bioreactors.
ver the past 2 decades, cultivation-independent approaches
O have identified at least 60 major branch points (phyla or di visions) in the bacterial and archaeal domains of life (1). Approx imately half of these branch points represent candidate divisions with no known cultivated representatives, so-called microbial dark matter (MDM) (2). Emerging lines of evidence suggest that bacteria affiliated with BD1-5, OP11, OP9/JS1, OP1, and WWE1 candidate divisions harbor genes encoding components of fer mentation, hydrogen or sulfur metabolic pathways supporting cometabolic or syntrophic growth modes under anaerobic condi tions (3-6). Consistent with this, a recent single-cell genomic study of OP9 bacteria from hot spring sediments suggested a de pendence on exogenous vitamins sourced from surrounding mi crobial community members (3). Such public good dynamics could represent a common organizing principle in structuring microbial community interaction networks (7, 8) and help ex plain the resistance of most environmental microorganisms, in cluding candidate divisions to clonal isolation (7, 9). While advances in metagenomic and single-cell genomic se quencing have begun to open a window on the metabolic potential of many candidate divisions, fundamental questions relating to their population structure, interactions, and biogeochemical roles remain. Recent molecular surveys indicate that natural and engi neered ecosystems with anoxic, sulfidic, and/or methane-rich conditions tend to harbor a diversity of candidate divisions (6, 10-12). Among ecosystems manifesting these biogeochemical conditions, permanently stratified meromictic lakes provide trac table models in which to study MDM population structure, func tion, and dynamics. In these aquatic ecosystems, the water column partitions into oxic fresh surface waters referred to as the mix olimnion, a redox transition zone, and anoxic, often hydrogen
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sulfide (H2S)- and methane (CH4)-rich bottom waters, referred to as the monimolimnion (13, 14). Previous studies have identified MDM in geographically iso lated meromictic lakes with different degrees of spatial and taxo nomic resolution. For example, small-subunit (SSU) rRNA gene clone library sequencing in Lake Pavin in France identified at least five bacterial candidate divisions, including OP1, OP3, OPIO, OP11, and WS5 that partitioned between the redox transition zone and monimolimnion (15). A more recent study using a com bination of SSU rRNA gene clone library sequencing and PhyloChip hybridization in Mahoney Lake in Canada identified at least eight bacterial candidate divisions, including OP1, OP8, OP9/JS1, OP11, TM6, WS1, WS3, and ZB2 (16). A higherthroughput study in Arctic Lake A identified SSU rRNA gene pyrotag sequences affiliated with at least four candidate divisions, including OP3, OP8, OP9, and OP 11 that increased in abundance within the monimolimnion (17). Here we describe the microbial community inhabiting Saki naw Lake, a meromictic lake, on the Sunshine Coast of British
Received 29 M ay 2014 A c c e p te d 22 A u g ust 2014 P ublished ahead o f p rin t 29 A ugust 2014 E ditor: F. E. Loffler Address correspondence to Steven J. Hallam, shallam@ mail.ubc.ca. Supplem ental m aterial fo r this article m ay be fo u n d at http://dx.doi.O rg/10.1128 /AEM.01774-14. C opyright © 2014, Am erican Society fo r M icrobiology. All Rights Reserved. doi: 10.1128/AEM .01774-14 The authors have paid a fee to allo w im m ed iate free access to this article.
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Columbia, Canada, using a pyrosequencing approach targeting the SSU rRNA gene, with specific emphasis on describing the pop ulation structure of MDM across defined redox transition zones. We apply hierarchical clustering and indicator species analyses to identify operational taxonomic units (OTUs) differentially asso ciated with the mixolimnion, upper and lower part of the transi tion zone, and monimolimnion in relation to environmental pa rameter data. Network analysis is then used to chart potential interactions between prevalent OTUs, including many candidate divisions. Metabolic reconstruction based on recently published near-complete Sakinaw Lake single-cell amplified genome se quences reinforces network connectivity patterns, revealing puta tive syntrophic relationships among candidate divisions and between candidate divisions and other microbial community members (2). MATERIALS AND METHODS Sampling. Water samples from Sakinaw Lake, British Columbia, Canada, were taken at deep basin station SI (49°40.968'N 124°00.119'W) using a combination of 12-liter Niskin and 8-liter GO-FLO bottles on 6 June 2007, 23 October 2007, 21 May 2008, 5 August 2009, 5 January 2010, 27 January2011,and 24 May 2011 (seeTableSl in th e supplemental mate rial). A total of 66 samples were collected from different depths covering the mixolimnion (5 m to 30 m), transition zone (33 m to 55 m), and monimolimnion (60 m to 120 m). Conductivity temperature and depth were measured with a Sea-Bird SBE19 CTD device measuring conductiv ity, temperature, and depth (Sea-Bird Electronics Inc., Bellevue, WA, USA). Samples were kept at 4°C in the dark and subsequently processed for environmental DNA extraction. Additionally, transition metal, sulfide (H2S), and sulfate (S042 -) concentrations were determined for samples collected on 24 May 2011. Environmental DNA. Approximately 6 h after sample collection, 2 liters of water was filtered through a 0.22-p.m Sterivex GV filter (Millipore) without a prefilter using a Masterflex L/S 7553-70 peristaltic pump (Cole-Parmer) for DNA extraction. DNA was extracted from Sterivex filters by the method of Zaikova and colleagues (18) and DeLong and colleagues (19). The DNA extraction protocol can be viewed as a visual ized experiment at http://www.jove.com/video/1352/ (20). Enumeration of cells by flow cytometry. Enumeration of total cells was performed by the method of Zaikova and colleagues (18). Briefly, water samples were fixed in 4% (wt/vol) formaldehyde and stored at 4°C in the dark for approximately 18 h prior to processing for flow cytometry. Nucleic acids were stained with SYBR green (Invitrogen), and cells were counted with a fluorescence-activated cell sorting (FACS) LSRII flow cy tometer equipped with an air-cooled argon laser (Becton Dickinson). Cell counts were estimated using a known concentration of 6-p.m fluorescent beads (Invitrogen). Chemical profiling. All salinities are on the TEOS-10 reference com position salinity scale, with the salinity anomaly assumed to be zero (21). Dissolved oxygen concentrations were determined by Winkler titrations (22). Hydrogen sulfide concentration was measured from water samples fixed with 2% final concentration of zinc acetate and analyzed in the lab using the methylene blue method of Cline et al. (23). The concentrations of transition metals, including iron (Fe), manganese (Mn), and arsenic (As), and S042- were determined at Maxxam Analytics (Burnaby, British Columbia, Canada) using Standard Methods for the Examination o f Water and Wastewater published by the American Public Health Association (24). Sulfate was determined with automated colorimetry according to the standard protocol SM 4500 S 0 42- within 24 h after sample collection (24). Samples for dissolved fractions of Fe, Mn, and As were preserved with nitric acid (H N 03) and filtered through a 0.45-p.m membrane filter prior to analysis with inductively coupled plasma mass spectrometry based on standard protocol EPA 200.8 (25) within 14 days of sampling. Dissolved methane (CH4) was measured from 13 samples (water depths
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of 5 m, 10 m, 25 m, 30 m, 33 m, 36 m, 40 m, 45 m, 50 m, 55 m, 60 m, 80 m, and 120 m) collected in June 2007 by gas chromatography coupled to mass spectrometry (GC-MS) using the static headspace equilibrium tech nique of Zaikova et al. (18). As CH4 concentrations in the monimolim nion of Sakinaw Lake exceed detection limits of the applied method, our data are estimates and indicate that CH4 concentrations in the deep Saki naw Lake waters exceed atmospheric saturation values. PCR amplification of SSU rRNA gene for pyrotag sequencing. Envi ronmental DNA extracts described above were amplified using previously published three-domain primers targeting the V6-V8 region of the SSU rRNA gene (26): 926F (5'-cct ate ccc tgt gtg cct tgg cag tet cag AAA CTY AAA KGA ATT GRC GG-3') and 1392R (5'-cca tet cat ccc tgc gtg tet ccg act cag-XXXXX-ACG GGC GGT GTG TRC-3')- Primer sequences were modified by the addition of 454 A or B adapter sequences (shown in lowercase type). In addition, the reverse primer included a 5-bp bar code designated XXXXX for multiplexing of samples during sequencing. Twenty-five microliter PCRs were performed in triplicate and pooled to minimize PCR bias. Each reaction mixture contained between 1 and 10 ng of target DNA, 0.5 p.1 Taq DNA polymerase (Bioshop Inc., Canada), 2.5 p.1 Bioshop 10X buffer provided in the Bioshop Taq-polymerase kit, 1.5 pi of 25 mM Bioshop MgCl2, 2.5 pi of 10 mM deoxynucleoside triphos phates (dNTPs) (Agilent Technologies), and 0.5 pi of 10 mM (each) primer. The thermal cycler protocol started with an initial denaturation at 95°C for 3 min and then 25 cycles, with 1 cycle consisting of 30 s at 95°C, 45 s at 55°C, 90 s at 72°C, and 45 s at 55°C. The final step, an extension step, was 10 min at 72°C. PCR products were purified using the QIAquick PCR purification kit (Qiagen), eluted in 20 mM Tris (pH 8), quantified using the Quant-it Picogreen dsDNA reagent. SSU rRNA amplicons were pooled at 30 ng DNA for each sample. Emulsion PCR and sequencing of the PCR amplicons were performed at the McGill University and G6nome Quebec Innovation Center on a Roche 454 GS FLX titanium platform according to the manufacturer’s instructions. Pyrotag sequence analysis. Pyrotag sequences were analyzed using the Quantitative Insights Into Microbial Ecology (QIIME) software pack age (27). To minimize the removal of false-positive singleton OTUs, 901,664 pyrotag sequences generated from 66 samples collected in Saki naw Lake between 2007 and 2011 were clustered together (see Table SI in the supplemental material). Reads with length shorter than 200 bases, ambiguous bases, and homopolymer sequences were removed prior to chimera detection. Chimeras were detected and removed using the chi mera slayer provided in the QIIME software package. Sequences were then clustered at 97% identity using uclust with furthest linkage algo rithm. Prior to taxonomic assignment, singleton OTUs (OTUs repre sented by one read) were omitted, leaving 23,231 OTUs (Table S2). To generate an OTU table specific for the May 2011 data set, the filter_otus_ by_sample.py script was used, leaving 181,464 sequences and 12,908 OTUs for downstream analysis. The average number of reads per sample in the Sakinaw Lake May 2011 data set was 16,323, with the exception of the 33-m-deep sample, which had ~50% fewer reads. Representative se quences from each nonsingleton OTU were queried against the SILVA database release 111 (28) and the Greengenes database (29) using BLAST (30). Overall, the comparison between SILVA and Greengenes databases revealed similar taxonomic assignments for bacterial OTUs. However, significant differences for archaeal OTU assignments were observed be tween the two databases. Approximately twice as many reads were as signed to Methanomicrobia using the Greengenes database than when the SILVA database was used. This difference could be mapped back to a single abundant OTU, approaching 10% of total sequences at a depth of 45 m that was assigned to Methanomicrobiales using the Greengenes da tabase and to Halobacteriales using the SILVA database. BLAST-based comparisons indicated that sequences comprising this OTU shared ~90% identity with Halobacteriales reference sequences in SILVA and 90% identity with Methanomicrobiales reference sequences in Green genes. Moreover, a number of putative archaeal OTUs associated with the
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redox transition zone and m onim olim nion (depths of 33 m to 120 m) were identified with “no blast hits” using Greengenes b u t assigned to Halobacteriales using SILVA. Because both databases are not well anno tated for Halobacteria (31), the Sakinaw Lake data set was m apped onto the full-length Halobacteria SSU rRNA gene database generated by Youssef and colleagues (31) using the program CD -H IT (cd-hit-est-2d) (32). The data set could not be clustered with the Halobacteria reference sequences at 99%, 97%, or 95% identity. Even for 90% sequence identity, only 981 reads (0.5%) clustered with the Halobacteria reference se quences. Given these uncertainties in putative archaeal OTU affiliation, we designated them “unassigned Archaea” until full-length sequences or reference genomes becom e available to support m ore-in-depth phyloge netic analysis. Statistical analyses. M icrobial com m unity richness was determ ined using scripts im plem ented in the QIIM E package. OTU tables were rar efied starting with 10 sequences to a m axim um o f 6,000 sequences. Ten iterations per sam ple were calculated with 100 sequences between each step. Hierarchical cluster analysis o f m icrobial com m unity compositional profiles and environmental parameters was conducted in the R statistical environm ent (Development Core Team, 2011; http://www.R-project.org/) using M anhattan distance measures for clustering microbial comm unity compositional profiles (33) and Euclidean distance for environmental pa rameters (34). Prior to analysis, pyrotag data sets were normalized to the total num ber of reads per sample, and environmental parameter data were trans formed to the same order o f magnitude so that each variable had equal weight. Multilevel indicator species analysis (ISA) was perform ed to identify OTUs specifically associated with different water colum n com partm ents (m ixolim nion [5 m, 20 m ,an d 30 m ], upper part of the transition zone [33 m, 36 m, 40 m, and 45 m ], lower part o f the transition zone [50 m and 55 m ], and m onim olim nion [60 m, 80 m, and 120 m [). Dufrene and Leg endre established a m ethod to determ ine an indicator species by its relat edness to a user-defined environm ent, e.g., group o f samples, where each species is treated individually, and its indicator value is determ ined ac cording to its abundance value (35). De Caceres and colleagues recently extended the algorithm for ISA to include a multilevel pattern analysis (36). Arguing that species with higher adaptability for different environ m ental factors are indicative for specific environm ent com binations, this analysis additionally determ ines indicator species for com binations of environm ents. The ISA/multilevel pattern analysis calculates P values with M onte Carlo sim ulations and returns indicator values (IVs) and values with a :£ 0.05. The IVs fall between zero and one, where one is considered a true indicator. Cooccurrence network. To generate a robust network emphasizing cooccurrences betw een prevalent O TUs in w ater colum n c o m p a rt m ents ra th e r th an individual dep th intervals, S pearm an’s rank co rre lation was used. S pearm an’s rank correlation coefficients were calculated using a custom perl script, “correlation_netw ork.pl” (https://github.com /hallam lab/utilities/tree/m aster/correlation_netw ork).T he initial data set consisted of 12,900 OTUs. To simplify the network, we retained OTUs with at least 10 reads appearing in at least three samples leaving 1,528 OTUs with Spearm an’s rank correlations equal to or greater than 0.99. The resulting cooccurrence netw ork contained 130,101 edges, each with a positive correlation. The netw ork was visualized with a force-directed layout, using Cytoscape 2.8.3 (37). Network properties were calculated with the “Network Analysis” plug-in. Nodes in the cooccurrence netw ork corresponded to individual OTUs, and edges were defined by com puted correlations between corresponding O TU pairs. The layout revealed four distinct m odules, which persisted after lowering the correlation coeffi cient cutoff for edge creation to 0.90, reinforcing the robustness o f the network. Pathway reconstruction. To evaluate the metabolic potential for syn trophic acetate oxidation (SAO) am ong highly connected candidate divi sions, publicly available single-cell genomes from Sakinaw Lake affiliated with OP9/JS1, OP8, and WWE1 were searched for coverage o f the W ood-
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Ljungdahl pathw ay using the Joint Genom e Institute integrated m icrobial genomes expert review portal (IMG-ER) (http://im g.jgi.doe.gov/). Nucleotide sequence accession number. The sequences reported in this study have been deposited in the NCBI BioProject database (www .ncbi.nlm .nih.gov/bioproject) u n d e r B ioProject accession no. PRJNA 257655 (identification no. [ID] 257655). RESULTS
Site location and physicochemical properties. S ak in aw Lake is a p e rm a n e n tly stra tifie d lake lo c a te d o n th e S u n s h in e C o a st o f B rit ish C o lu m b ia , C a n a d a (4 9 °4 0 .8 'N , 1 2 4 °0 0 .3 9 'W ) a t a n e le v atio n o f ~ 5 m a b o v e sea level. O rig in a lly a fjo rd o p e n in g to th e S tra it o f G e o rg ia, S a k in aw L ake w as a lm o st c o m p le te ly iso late d fro m th e o c ea n d u e to c o asta l u p lift ~ 11,000 y ears ago fo llo w in g th e last ice age (38). F o r th o u s a n d s o f years, a sm all s tre a m n a m e d S ak in aw C re ek w as th e o n ly c o n d u it b e tw e e n S a k in aw Lake a n d th e S tra it o f G eo rg ia. In 1952, S a k in aw C re e k w as d a m m e d to b e tte r m an a g e w a te r levels fo r d e v e lo p m e n t p ro je c ts in th e s u r r o u n d in g w a te r sh e d p re v e n tin g se a w ate r ingress. T h e lake c o n sists o f tw o basins: a relatively sh a llo w fre sh w a te r b a sin 49 m d e e p a n d a m o re e x te n sive sa lt stra tifie d b a sin 140 m d e e p (39). A g ra d u a l sa lin ity i n crease in th e tr a n s itio n z o n e is fo llow ed b y a series o f th re e < 0 .5 g /kg sa lin ity ste p s p u n c tu a tin g th e m o n im o lim n io n . M e a s u re m e n ts o f a b so lu te sa lin ity (SA), te m p e ra tu re , a n d o x ygen b e tw e e n 2007 a n d 2011 re v ea le d a re m a rk a b le s ta b ility in th e sa lin ity g ra d ie n t t h r o u g h o u t th e w a te r c o lu m n as w ell as c o n sis te n t te m p e ra tu re s b e lo w a d e p th o f 36 m (see T ab le S3 in th e s u p p le m e n ta l m a te ria l). T e m p e ra tu re a n d o x y g e n ( 0 2) p ro files in th e m ix o lim n io n c h a n g e d b e tw e e n d iffe re n t sa m p lin g tim e p o in ts , w h ic h w as e x p ec te d d u e to sea so n al in flu en ces. T o d e te r m in e th e a v ailab ility o f te rm in a l e le c tro n a c c e p to rs (T E A s) fo r m ic ro b ia l e n e rg y m e ta b o lism in d iffe re n t w a te r c o lu m n c o m p a r t m e n ts , 0 2, iro n (F e), m a n g a n e s e (M n ), a rse n ic (A s), a n d S 0 42 m e a s u re m e n ts w ere p lo tte d as a fu n c tio n o f d e p th a n d c o m p a re d to m e a s u re m e n ts o f SA, su lfid e ( H 2S), a n d to ta l b a c te ria l cell c o u n ts (Fig. 1). T h e re su ltin g p rofiles w ere th e n c o m p a re d to g e o g ra p h ic a lly d is tin c t m e ro m ic tic lakes to d e te rm in e c o m m o n b a se lin e c o n d itio n s (T ab le S4). T h e m ix o lim n io n in S a k in aw Lake (5 m to 30 m ) w as c o n s titu te d o f e n tire ly fresh , o x y g e n -ric h w a ter. In th e tr a n s itio n z o n e (30 m to 55 m ), sa lin ity a n d th e c o n c e n tra tio n o f a lte rn a tiv e TEA s, in c lu d in g Fe, M n , a n d S 0 42 - g ra d u a lly i n crea se d , w h e rea s 0 2 c o n c e n tra tio n s d e cre ased . T h e o x ic -a n o x ic in te rfa c e w as lo c a te d a t a d e p th o f 33 m . M e th a n e c o n c e n tra tio n s , w h ic h w e re d e te rm in e d fo r sa m p le s c o lle cted in Ju n e 2007 i n c rea se d b e lo w 33 m a n d re a c h e d s a tu ra tio n a t 45 m (d a ta n o t sh o w n ), fo rm in g a su lfa te m e th a n e tra n s itio n z o n e (S M T Z ). P eak c o n c e n tra tio n s o f Fe, M n , a n d S 0 42~ w ere o b se rv e d in th e m ic ro m o la r ra n g e (8.1 p,M , 4.2 |xM , a n d 57.5 |xM , resp ectiv ely ) a t a d e p th o f 36 m a n d c o rre s p o n d e d w ith in c re a se d m ic ro b ia l cell a b u n d a n c e ( ~ 2 .8 X 105 cells/m l). Sulfide c o n c e n tra tio n s in th e lo w e r p a rt o f th e re d o x tra n s itio n z o n e a n d th e m o n im o lim n io n (50 m to 120 m ) w e re h ig h (4.5 m M ) a n d re su lte d in th e re m o v a l o f so lu b le Fe fro m th e w a te r c o lu m n b y p re c ip ita tio n in to iro n su lfid e (FeS) a n d p y rite (FeS2) (40). T h e p e a k c o n c e n tra tio n o f As w as in th e n a n o m o la r ra n g e (93.4 n M ) a t a d e p th o f 60 m .
Relationship between community structure and physico chemical characteristics. T o b e tte r u n d e rs ta n d h o w th e p h y sic o c h e m ic a l p ro p e rtie s o f th e S a k in aw L ake w a te r c o lu m n in flu e n c e m ic ro b ia l c o m m u n ity d iv ersity , w e p e rfo rm e d 454 p y ro se q u e n c in g o f th e SSU rR N A gene w ith th re e -d o m a in re so lu tio n . Al-
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200 reads in the time series consistent with the presence of conditionally rare taxa in the Sakinaw Lake water column (see Table S2 in the supplemen tal material) (41). Approximately 75% of the organisms in the May 2011 data set were assigned to the domain Bacteria, and 25% of these were as signed to candidate divisions. Collectively, bacterial OTUs con tained the majority of pyrotag reads in all but the 45-m-deep sam ple where almost 60% of the reads were assigned to the domain Archaea (Fig. 2A). Eukaryotes, although prevalent in oxygenated surface waters, vanished almost completely below the mixolim
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nion. Richness estimates based on count data indicated that the mixolimnion had fewer OTUs than samples from the transition zone and monimolimnion (Fig. 2B). This pattern was consistent for all samples collected between 2007 and 2011 (see Fig. SI in the supplemental material). Hierarchical clustering of the microbial community composi tion profiles and selected environmental parameter data (temper ature, TEAs, and H2S) reflected the stratified nature of the lake ecosystem (Fig. 2C). The resulting dendrograms revealed identical clustering patterns between OTU abundance and environmental parameters for almost all depth intervals. Both dendrograms sug gest that water column partitioning in Sakinaw Lake did not easily conform to a three-compartment meromixis model, i.e., mix olimnion, redox transition zone, and monimolimnion. This was not unexpected, as the division of the water column into three compartments is based solely on hydrodynamic conditions and does not reflect partitioning of the water column into distinct environmental niches. On the basis of these observations, we op-
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FIG 2 Relationship between community structure and physicochemical characteristics of the Sakinaw Lake water column. (A) Bacterial operational taxonomic units (OTUs) contained the majority of pyrotag reads in all samples but the sample taken at a depth of 45 m, where almost 60% of the reads were assigned to Archaea. Eukaryotes vanished almost completely below the mixolimnion. (B) Richness estimates based on count data indicated that the mixolimnion had fewer OTUs than samples from the transition zone and monimolimnion. (C) Hierarchical clustering of the OTU abundance and environmental parameter data (temperature, S„, TEAs, and H2S).
erationally divided the transition zone into an upper and lower part to more accurately reflect potential niche partitioning. To identify how OTUs partitioned between the mixolimnion, upper part of the transition zone, lower part of the transition zone, and monimolimnion, we conducted a four-way set difference analysis of the presence of OTUs (Fig. 3A). Approximately 1% of the OTUs were shared between all four water column compart ments, with the majority of OTUs exhibiting mutually exclusive distribution patterns (13% unique to the mixolimnion, 21% unique to the upper transition zone, 10% unique to the lower transition zone, and 21% unique to the monimolimnion) (Fig. 3A). Operational taxonomic units associated with a given water column compartment that are absent or rare in other compart ments are potential ecological indicators. To identify potential indicator OTUs, we performed a multilevel indicator species anal ysis. With this analysis we were able to determine indicator species for individual water column compartments and compartment combinations. Only 5 to 10% of the compartment-specific OTUs were identified as indicator species (Fig. 3B; see Fig. S2 in the supplemental material). Indeed, the majority of OTUs that were identified as compartment specific in the four-way set difference analysis exhibited fine-scale depth partitioning (Fig. 3C and Table S5). Indicators shared between compartments were also uncom mon with the exception of the lower part of the transition zone and monimolimnion. In these two compartments, —44% of the
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OTUs were identified as indicators. Overall, the majority of OTU indicators were affiliated with unassigned Archaea (25.63%) and candidate phyla (21.3%), followed by Chloroflexi (16.2%). For a detailed taxonomic breakdown of indicator OTUs, see Table S6. Taxonomic composition. To determine how the taxonomic composition of the microbial community was distributed within and between water column compartments, we plotted bacterial and archaeal OTUs based on relative abundance (Fig. 4). The presence of eukaryotes in Sakinaw Lake is described in the supple mental material (see Fig. S3 in the supplemental material). All taxa exhibiting intermediate abundance (>0.1%) or above are repre sented in Fig. 4 and also Fig. S3 in the supplemental material. However, for visualization purposes, only taxa >1% are shown to scale. The most abundant taxa in the mixolimnion were Alphaproteobacteria (11.8%) the majority of which were affiliated with SAR11 (9.7%), Betaproteobacteria (10%) mostly affiliated with Burkholderiales (8.1%), Bacteriodetes (13.2%), Actinobacteria (22.3%), Cyanobacteria (16.9%), Planctomycetes (8.3%), and Verrucomicrobia (1.1%) (Fig. 4A; see Table S2 in the supplemental material). Archaea were almost absent from the mixolimnion, al though Methanomicrobia were identified among members of the rare biosphere (
