Spatial patterns of bacterial community composition ... - PubAg - USDA

Journal of Great Lakes Research 39 (2013) 344–351

Contents lists available at SciVerse ScienceDirect

Journal of Great Lakes Research journal homepage: www.elsevier.com/locate/jglr

Notes

Spatial patterns of bacterial community composition within Lake Erie sediments Juan L. Bouzat ⁎, Matthew J. Hoostal, Torey Looft 1 Department of Biological Sciences, Bowling Green State University, Bowling Green, OH 43403, USA

a r t i c l e

i n f o

Article history: Received 18 July 2012 Accepted 15 January 2013 Available online 11 April 2013 Communicated by Hunter Carrick Keywords: Bacterial communities Lake Erie Local adaptation Microbial diversity Sediment 16S rDNA

a b s t r a c t Lake Erie is a large freshwater ecosystem with three distinct basins that exhibit an east-to-west gradient of increasing productivity, as well as allochthonous inputs of nutrients and xenobiotics. To evaluate microbial community composition throughout this ecosystem, 435 16S rDNA environmental clones were sequenced from 11 sediment samples throughout the Western, Central, and Eastern basins, as well as the hypoxic “dead zone” of Lake Erie in the hypolimnetic region of the Central basin. Rank abundance distributions of bacterial taxa within each location revealed that Gamma- and Betaproteobacteria, microbes capable of metabolizing a wide range of organic matter pools, comprised a greater fraction of the microbial community within inshore sites of the Central and Western basins compared to the Eastern basin. While geophysical characteristics of the three major basins and the dead zone did not drive significant differences in species diversity, Fast UniFrac analyses revealed microbial community spatial structuring, with the Central basin showing higher phylogenetic uniqueness of bacterial lineages. Principal component analyses based on phylogenetic distances consistently grouped the dead zone with the Central basin and highlighted the distinctiveness of microbial communities from the Eastern basin. Results from this study provide evidence for the local adaptation of microbial communities and the potential role of riverine inputs in modulating taxonomic composition of lacustrine bacterial communities. These results are consistent with previous functional studies on microbial metabolism, which showed that differences in geochemistry across the three basins of Lake Erie play an important role in the local adaptation of microbial communities. © 2013 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

Introduction Lake Erie has experienced immense anthropogenic influences within its drainage basin due to extensive phosphorus loadings during the 1960s, as well as considerable inputs of xenobiotics, resulting in the biomagnification of contaminants throughout the food web, eutrophication, algal blooms, and hypoxia (Burns, 1985). The microbial community composition of freshwater systems is critical in nutrient cycling, contaminant transformation, and energy transfer through aquatic food webs (Paerl and Pinckney, 1996). Due to the physical and chemical gradients found across large ecosystems, a variety of habitats and microniches can generate spatial patterns of microbial diversity associated with local adaptations of metabolically diverse microorganisms (Fierer and Lennon, 2011; Nold et al., 2010). Previously, spatial patterns of microbial metabolism were observed in surface sediments throughout the three basins of Lake Erie (Hoostal and Bouzat, 2008). In addition, microbial communities indigenous to polluted sediments were found to be metabolically more resilient to treatments with heavy metals than microbial communities from relatively pristine sediments within the Central basin ⁎ Corresponding author. Tel.: +1 419 372 9240. E-mail address: [email protected] (J.L. Bouzat). 1 Current address: United States Department of Agriculture, Agricultural Research Service, Ames, IA 50010, USA.

of Lake Erie (Hoostal et al., 2008). Together, these results suggest that natural and anthropogenic inputs into Lake Erie modulate the adaptation of microbial communities to local environmental conditions, determining large-scale spatial patterns of microbial functional diversity. In the present study, 16S rDNA libraries from environmental samples were developed to characterize patterns of microbial diversity in sediments across Lake Erie and provide insights into the potential factors leading to differential functional diversity. Fluxes in nutrient composition may result in shifts in microbial metabolism linked to adaptive changes in bacterial community composition (Freeman et al., 1990). In contrast, functional responses of microbial communities may result from physiological responses of individual taxa, whereas moderate changes in bacterial composition may not be consequential to microbial community metabolism (Langenheder et al., 2006). The role of environmental gradients in modulating bacterial community composition has been well-studied in artificial systems (e.g., Chróst and Rai, 1993), or communities associated with extreme environments, such as hot springs (e.g., Achour et al., 2007). Microflora composition has also been examined in specialized niches throughout the Great Lakes system, including sinkholes within Lake Huron (Nold et al., 2010) and the water column of Lake Erie's hypoxic region (Wilhelm et al., 2006). However, the potential selective role that spatial gradients exert on bacterial community composition in large, complex aquatic ecosystems is relatively less documented (Judd et al., 2006). Lake Erie, with an

0380-1330/$ – see front matter © 2013 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jglr.2013.03.003

J.L. Bouzat et al. / Journal of Great Lakes Research 39 (2013) 344–351

replicate sediment samples were collected from the top 3 cm of the sediment surface in sterile 50 mL polystyrene tubes and kept on ice until returned to the laboratory, where they were stored at −20 °C until further analysis.

east-to-west gradient of increasing nutrient and xenobiotic loadings across its three basins provides an excellent model to assess how bacterial community composition is modulated across environmental gradients within a large ecosystem. The overall goal of this study was to provide a molecular characterization of microbial communities based on 16S rDNA libraries from sediment samples collected throughout Lake Erie. In addition, spatial patterns of microbial diversity based on phylogenetic inferences of taxonomic groups detected within and across sample sites were assessed in relation to east-to-west gradients of nutrient inputs and environmental contaminants, as well as the unique ecological conditions of Lake Erie's hypoxic region. Results from this study provide an initial basis to link genetic and functional diversity in microbial communities from Lake Erie sediments.

Environmental characterization of 16S rDNA sequences To characterize bacterial community composition within sediment samples, microbial genomic DNA was extracted from sediment samples using Fast DNA Spin kits (Biol 101, La Jolla, CA). The DNA concentration and purity were determined by gel electrophoresis, as well as spectrophotometry at 260 and 280 nm of light. A total of 80 ng was used as template DNA for subsequent polymerase chain reactions (PCR). PCR amplification of bacterial community 16S rDNA was performed using PCR primers designed to correspond with conserved regions of the 16S rDNA gene (Lane et al., 1985). These primers have previously been shown to amplify an approximately 325 bp section of the 16S rDNA gene, which is highly variable across bacterial taxa (Lane et al., 1985; Schwieger and Tebbe, 2000). Both forward (Com1-F: 5′-CAG CAGCCGCGGTAATAC-3′) and reverse (Com1-R: 5′-CCGTCAATTCCTTTG AGTTT-3′) primers were selected to amplify a segment of bacterial 16S rDNA flanked at positions 519–536 and 907–926 of the Escherichia coli genome, respectively. PCR amplifications were carried out in an MJ PTC-100 (MJ Research, Waltham, MA) thermal cycler. Reaction mixtures contained 1× PCR buffer, 2.0 mM MgCl2, 2.0 units of Taq polymerase (Promega), and 0.5 mM of each primer. The reaction profile was comprised of an initial denaturing step at 94 °C for 3 min, followed by 35 cycles of 60 s at 94 °C, 60 s at 50 °C, and 90 s at 74 °C, with a final DNA extension step at 72 °C for 4 min, followed by 4 min at 4 °C (Schwieger and Tebbe, 2000). To control for potential bias during PCR amplifications, two independent sets of genomic extractions from independent replicates were separately used for PCR and subsequent cloning of 16S rDNA libraries. PCR amplicons were cloned using standard Topo TA cloning kits with a pCR 2.1 vector, according to the manufacturer's directions (Invitrogen, Grand Island, NY). Individual clones were then extracted using Qiaspin kits (Qiagen, Valencia, CA) and purified plasmids were

Methods Sample collection To examine bacterial community composition, surface sediments were collected with a Ponar grab from 11 stations distributed throughout the three basins of Lake Erie in mid-September of 2002, during a cruise of the RV Lake Guardian (Fig. 1). Lake sediments generally act as both nutrient and contaminant sinks (Allan and Castillo, 2007), which provide excellent systems for investigating potential links between microbial metabolism and community composition (Crump et al., 2004; Findlay et al., 2003). For this study, sample sites corresponded to sediments analyzed in a previous report on spatial patterns of microbial community metabolism (Hoostal and Bouzat, 2008). Sample locations were divided into four groups, which included three sample sites from inshore regions of the eastern basin (Erie, Barcelona, and Port Dover), three sample sites from inshore regions of the Central basin (Cleveland, Ashtabula, and Port Alma), and two sites associated with inshore regions of the western basin (Sandusky and S-61). The fourth group consisted of three sample sites from offshore regions of the Central basin (dead zone) associated with seasonal hypoxia (sites S-42, S-43 and S-78; see Fig. 1 for distribution of sample locations). At each station,

83

82

345

80

81

79

DOV

Eastern Basin BAR

PAL

Dead Zone

S-42 S-61

ERIE

S-78 ASH

42

S-43 CLE SAN

Western Basin 0

miles

Central Basin 40

Lake Erie

Fig. 1. Map of Lake Erie showing the location of eleven sample sites (black circles) throughout the three major basins of Lake Erie. Pie charts show relative abundance of major bacterial groups as determined by the characterization of 435 16S rDNA sequences from the Western, Central, and Eastern basins, as well as the dead zone of Lake Erie. The pie chart for Lake Erie shows the relative abundance of bacterial groups across the entire lake. Station names are abbreviated as CLE (Cleveland), ASH (Ashtabula), ERIE (Erie), BAR (Barcelona), PAL (Port Alma), and DOV (Dover).

346


sequenced in our lab or at the sequencing facility of the Southern Insect Management Unit of the USDA-ARS in Stoneville (Mississippi). A total of 435 clones from 11 sample sites were sequenced, with an average of 39 clones per site (range 38–41). From each sample site, clones were obtained in approximately equal numbers from each independent PCR reaction to avoid potential effects of sample size on estimates of diversity. Sequencing reactions were performed using capillary DNA sequencing technology and fluorescent chemistry (Applied Biosystems, Foster City, CA). Electrophoretograms from DNA sequences were checked for base miscalling by direct visualization of focal sequences in Bioedit (Hall, 1999). Taxonomic characterization of 16S rDNA sequences To generate rank abundances of bacterial community composition, 16S rDNA sequences were characterized using the open-source Ribosomal Database Project (RDP) Sequence Classifier (Cole et al., 2005) and the Basic Local Alignment Search Tool (BLAST) (Altschul et al., 1990) implemented in GenBank. RDP characterizations place DNA sequences in the major formal taxonomic ranks of domain, phylum,

class, order, family, genus, and species, with estimates of confidence given for each assignment (Nold et al., 2010; Wang et al., 2007). For rank abundance analyses, the characterization of DNA sequences was resolved to the level of phylum. However, in the case of phylum Proteobacteria, classifications were specified to the class level. In addition, sequences classified as Archaea were not specified further. Only microorganisms classified with at least an 80% confidence level were assigned to taxonomic groups with the RDP classifier tool. In addition to the RDP classifier, BLASTn searches were performed using each 16S rDNA sequence as a query sequence to determine the most similar characterized bacteria within the GenBank database. Subsequent to bacterial categorization by the RDP classifier and BLAST queries, phylogenetic analyses were also performed to confirm bacterial classifications and potentially identify microorganisms not classified by the RDP classifier and BLAST queries. Maximum likelihood phylogenies were reconstructed using the program MEGA v. 3 (Kumar et al., 2004) with the Tamura–Nei model of sequence evolution (Tamura and Nei, 1993; Fig. 2). DNA sequences identified by the RDP classifier as belonging to domain Archaea were incorporated as outgroups for phylogenetic reconstructions. Multiple 16S rDNA

Fig. 2. Maximum likelihood phylogenetic tree of 435 16S rDNA environmental sequences reported in this study (unlabeled branches) and reference sequences of major bacterial groups. Bacterial group names appear in bold and reference sequences of major bacterial groups appeared in smaller font labeled with the first letter of the genus followed by the first three letters of the species (see Supplementary Information 1 for reference sequences). Black circles on branches represent bootstraps of major clades with values greater than 50%.


sequences from previously characterized taxa were collected from the NCBI GenBank and included in phylogenetic analyses to aid in the determination of bacterial taxa (GenBank accession numbers of reference sequences and sequences reported in this study are included in Supplementary Information 1 and 2). Additionally, a Bayesian phylogenetic analysis was performed using the program Mr. Bayes (Ronquist and Huelsenbeck, 2003) to provide input for subsequent evaluation of spatial structuring of microbial communities (see UniFrac methods below). Posterior probabilities were generated with a Bayesian Metropolis-coupled Markov Chain Monte Carlo Method using 150,000 generations with eight runs and six chains to reach convergence. Phylogenetic trees were visualized using the program PAUP*, v. 4 (Swofford, 2003). Measures of taxonomic richness and diversity The online software Species Prediction and Diversity Estimation (SPADE; Hong et al., 2006) was used to measure taxonomic richness (i.e., the number of taxa present in a sample) and taxonomic diversity (an index of both the number and abundance of taxa) within each study location. SPADE was utilized to directly test the null hypotheses that the richness and diversity of bacterial taxa among sample locations within Lake Erie are equal. SPADE calculates diversity using multiple methods, so that estimates of diversity are measured with varying models and underlying assumptions. Rank abundances of phyla and class were used as input into SPADE because multiple reports indicate the relative importance of these taxonomic levels in differentiating bacteria based upon nutrient assimilation (Cottrell and Kirchman, 2000; Nold et al., 2010). Measures of richness were obtained with the parametric maximum likelihood estimate method (Bunge and Barger, 2008), as well as the non-parametric bias-corrected Chao1 method (Chao and Shen, 2003) and first-order jackknife method (Burnham and Overton, 1978). A Shannon index of diversity was also obtained using three different estimators: the maximum likelihood estimate method (Bunge and Barger, 2008), the Chao and Shen method (Chao and Shen, 2003), and the jackknife method (Zahl, 1977). Standard errors and 95% confidence intervals for richness and diversity estimates were calculated following 200 bootstrap resamplings. Rarefaction curves indicated that the number of clones collected from each of the four regions was sufficient to identify a large proportion of major taxonomic groups within each location (see Supplementary Information 3). Fast UniFrac analyses of microbial community composition Bayesian phylogenetic inferences were used as input for Fast UniFrac (Hamady et al., 2010), which uses the UniFrac Significance Test (Lozupone and Knight, 2005) and the P-test (Martin, 2002) to compare the similarity of communities based upon phylogenetic inferences. If two environments have similar microbial community composition, most nodes in a phylogenetic tree may be expected to have descendants from both communities. In contrast, locally adapted microbial communities may result in phylogenetic structures with relatively independent lineages from different locations (Lozupone and Knight, 2005). UniFrac allows comparisons of all samples across environments (“all environments” option), as well as estimates of differentiation of individual samples (“each sample individually” option). Within this study, 1000 permutations were performed to calculate P-values for the UniFrac significance test with each option. Fast UniFrac was also utilized to perform principal coordinate analysis (PCA), which transforms an input matrix of UniFrac distance pairwise comparisons between locations into points within a coordinate system (Lozupone and Knight, 2005). The first three principal components collectively explained > 99% of the total variance, with 33% of the total variance explained by each principal component (see Fig. 3A). In assessing the ecological significance of principal

347

component loadings when sample sizes are greater than 50, Hair et al. (1987) suggest that principal component loadings with an absolute value greater than 0.30 are significant. Results Taxonomic characterization of 16S rDNA sequences Using a combination of the RDP Sequence Classifier, BLASTn queries and phylogenetic reconstructions, a large majority (79%) of the 435 DNA sequences collected from 11 sample stations throughout Lake Erie were assigned to major taxonomic groups (Figs. 1 and 2, Table 1). Throughout the three basins, a wide variety of microbial taxa were identified to the level of genus with RDP classifier confidences greater than 90%. These genera included groups with a broad array of metabolic pathways and geochemical transformations, including nitrogen fixation, e.g. Azospirillum; nitrification, e.g. Nitrospira; denitrification, e.g. Steroidobacter and Acidovorax; sulfate reduction, e.g. Geobacter, Desulfuromonas, and Desulfobulbus; nitrate reduction, e.g. Caldithrix; methanogenesis, e.g. Smithella; methylotrophy, e.g. Methylophilus; iron reduction, e.g. Anaeromyxobacter and Geobacter; as well as acidophilic iron and sulfur oxidation, e.g. Thiobacillus (Haaijer et al., 2008; Hunger et al., 2011; Revsbech et al., 2006; Scheid et al., 2004). A predominant fraction of the total bacterial sequences collected from Lake Erie sediments (53%) were identified to be from the phylum Proteobacteria (Figs. 1 and 2), specifically from the classes Deltaproteobacteria (23%), Betaproteobacteria (15%), Gammaproteobacteria (12%), and Alphaproteobacteria (2%). Bacterial phyla that were also notably present included Acidobacteria (6%), Bacteroidetes (5%), Nitrospira (5%), and Firmicutes (4%). DNA sequences from the phyla Chloroflexi, Verrucomicrobia, Gemmatinomanadales, Deferribacteres, Cyanobacteria, and Spirochaete, as well as potential representatives from a number of bacterial candidate divisions each comprised 1% or less of the total number of sequences collected (Fig. 1 and Table 1). DNA sequences identified as Archaea comprised 3% of all sequences. Interestingly, the RDP classifier, as well as BLAST searches of GenBank and phylogenetic analyses, characterized six sequences from three sample sites as belonging to the thermophilic class Thermoprotei of domain Archaea (Anderson et al., 2011; Sokolova et al., 2009). Moreover, two sequences from the hypoxic area of the Central basin were identified as psychrophilic Bacteroidetes. While great bacterial diversity was observed within each sample site, an examination of the distribution of major microbial taxa across the four sample locations investigated suggested some ecosystem-level patterns in bacterial community composition across Lake Erie (Fig. 1 and Table 1). While the class Deltaproteobacteria was a prominent phylogenetic group observed in all four locations, especially the hypoxic region of the Central basin, Gammaproteobacteria and, particularly, Betaproteobacteria showed an east-to-west gradient in their relative abundances. Representatives from the class Betaproteobacteria represented approximately 19% of all sequences from the inshore sample stations of the Central and Western basins. However, within the eastern basin and dead zone locations, representatives from this class only accounted for 10% and 15% of total bacteria sequences from these locations, respectively. Similarly, Gammaproteobacteria composed 18% of the total bacterial community in the western basin, 18% in the dead zone, 15% in the near-shore sites of the Central basin, and 8% in the Eastern basin. SPADE measures of taxonomic richness and diversity Each of the three methods used for measuring richness and diversity provided consistent estimates among sample locations (Table 2). Unlike rank abundance data of microbial community composition, no

348


Fig. 3. Principal component analysis to determine microbial community associations among sample locations of Lake Erie (W, Western basin; C, Central basin; E, Eastern basin; and D, Dead zone). (A) Scree plot: the top line demonstrates the cumulative importance of PC1, PC2, and PC3 in explaining variance within the distance matrix; the bottom line demonstrates the nearly equal importance of each principal component. (B) Sample location ordinations generated along PC1 and PC2. (C) Sample location ordinations generated along PC1 and PC3. (D) Sample location ordinations generated along PC2 and PC3.

distinct spatial patterns in taxonomic diversity or richness could be ascertained among Lake Erie locations. In general, estimates showed no significant differences in species richness and species diversity among the four regions with overlapping 95% confidence intervals (see Table 2). The estimated sample coverages of major taxonomic

groups in the Western, Central, and Eastern basins, as well as the dead zone, were 0.922, 0.939, 0.964, and 0.968, respectively, suggesting that representatives from a large majority of major taxonomic groups recovered had been sequenced. This is consistent with rarefaction curves showing that most major taxonomic groups

Table 1 Rank abundances among the four locations based upon characterizations of 16S rDNA sequences collected from 11 sediment sample sites throughout the three basins of Lake Erie and the dead zone. Data indicates the number of sequences identified as a particular bacteria taxonomic group based on a combination of phylogenetic analyses, as well as RDP and BLAST queries. Western basin Deltaproteobacteria Betaproteobacteria Gammaproteobacteria Bacteroidetes Firmicutes Acidobacteria Nitrospira Alphaproteobacteria Archaea Chloroflexi Verrumicrobiota Candidate division OP-11 Candidate division OP-8 Cyanobacteria Deferribacter Gemmatimonadetes Spirochaete Candidate division BRCI Candidate division W3 Candidate division TG3 Candidate division OD1 Total

Dead zone 13 14 14 2 0 8 4 1 1 1 1 1 0 0 0 0 0 0 0 0 0 60

Deltaproteobacteria Betaproteobacteria Gammaproteobacteria Bacteroidetes Firmicutes Acidobacteria Nitrospira Alphaproteobacteria Archaea Chloroflexi Cyanobacteria Gemmatimonadetes Deferribacter Spirochaete Verrumicrobiota Candidate division BRCI Candidate division OP-11 Candidate division W3 Candidate division TG3 Candidate division OP-8 Candidate division OD1

Central basin 32 16 16 8 2 3 9 1 3 1 1 1 0 0 0 0 0 0 0 0 0 93

Deltaproteobacteria Betaproteobacteria Gammaproteobacteria Bacteroidetes Firmicutes Acidobacteria Nitrospira Alphaproteobacteria Archaea Chloroflexi Deferribacter Spirochaete Candidate division TG3 Candidate division OP-8 Candidate division OD1 Cyanobacteria Gemmatimonadetes Verrumicrobiota Candidate division BRCI Candidate division OP-11 Candidate division W3

Eastern basin 28 25 13 7 7 4 4 3 2 1 1 1 1 1 1 0 0 0 0 0 0 99

Deltaproteobacteria Betaproteobacteria Gammaproteobacteria Bacteroidetes Firmicutes Acidobacteria Nitrospira Alphaproteobacteria Archaea Chloroflexi Verrumicrobiota Gemmatimonadetes Candidate division BRCI Candidate division W3 Cyanobacteria Deferribacter Spirochaete Candidate division OP-11 Candidate division TG3 Candidate division OP-8 Candidate division OD1

28 12 10 3 7 10 4 2 5 2 2 1 1 0 0 0 0 0 0 0 0 87


349

Table 2 Species richness and Shannon diversity index each estimated using the program SPADE and three independent models (see Methods). The table shows the estimated richness and diversity of bacterial phyla, standard errors of the mean (S.E.), and 95% confidence intervals (CI). Richness measures Location

Western basin Central basin Dead zone Eastern basin

Homogenous model (MLE)

Bias-corrected Chao1

First-order jackknife

Estimate

S.E.

95% CI

Estimate

S.E.

95% CI

Estimate

S.E.

95% CI

11.0 15.0 12.0 14.0

0.1 0.2 0.1 0.2

11.2–12.4 15.0–16.2 12.0–12.5 14.0–15.4

16.0 22.5 18.0 14.8

6.0 8.2 7.2 1.4

11.8–43.1 16.3–57.5 12.9–50.0 14.1–22.5

15.9 20.9 16.0 17.0

3.1 3.4 2.8 2.4

12.6–26.4 17.1–32.0 13.1–25.8 14.7–26.1

S.E.

95% CI

Estimate

S.E.

95% CI

Estimate

0.1 0.1 0.1 0.1

1.65–2.07 1.90–2.29 1.73–2.10 1.92–2.05

0.19 0.16 0.19 0.16

1.65–2.37 1.91–2.56 1.65–2.23 1.97–2.58

Shannon index of diversity Location

Homogenous model (MLE) Estimate

Western basin Central basin Dead zone Eastern basin

1.91 2.10 1.92 2.08

Chao and Shen

2.01 2.23 2.01 2.28

were identified within each of the four study regions (see Supplementary Information 3). Fast UniFrac analyses of microbial community composition To quantitatively assess spatial patterns of microbial community composition among sample locations, Fast UniFrac provides various parameters to compare the phylogenetic distances of DNA sequences collected from multiple locations using the UniFrac metric (Hamady et al., 2010). The significance of the corrected UniFrac P-value and the P test for the “all environments” option were 0.002 and 0.001, respectively, indicating significant spatial structuring of microbial communities. When each location was examined individually using the UniFrac metric, only the inshore sites of the Central basin demonstrated a significant P-value (0.04), suggesting that sequences from this location have more unique branch lengths than would be expected if sequences were randomly distributed throughout the tree. Principal component analysis (PCA) was used to demonstrate potential genetic similarities among microbial communities from various locations. Microbial communities from the three major basins (East, Central and Western) were clearly separated across principal components 1 and 2 (see Fig. 3B). Furthermore, these principal components consistently grouped the dead zone with the Central basin. Along PC1, the Eastern basin was most distinctly separate from the other locations, with a significant loading of 0.589 (Figs. 3B and C). Samples from the Western basin also showed a negative loading (−0.358) along PC1, indicating a more close association to the Central basin and the dead zone than to the Eastern basin. The association of bacterial communities from the dead zone and Central basin near-shore sites was also revealed along principal component 2, which showed significant component loadings of −0.354 and −0.355, respectively (Figs. 3B and D). Discussion This study provided an initial characterization of the sediment microbial communities across the three major basins of Lake Erie and the hypoxic region known as the dead zone. Rank abundance data revealed that sediment microbial communities in Lake Erie are dominated by three major classes of Proteobacteria (Deltaproteobacteria, Betaproteobacteria and Gammaproteobacteria). The greater fraction of Gamma- and Betaproteobacteria within inshore sites of the Central and Western basins, with relatively greater riverine loadings (Nicholls and Hopkins, 1993) compared to the Eastern basin, was consistent with the association of these bacterial classes with fluctuating nutrient pools in freshwater systems dominated by terrestrial inputs (Bramucci et al., 2003; Burkert et al., 2003). These results are also consistent with the

First-order jackknife

2.03 2.21 2.00 2.27

S.E.

95% CI

0.22 0.12 0.11 0.12

1.64–2.48 1.97–2.45 1.78–2.23 2.04–2.50

observation of Betaproteobacteria comprising the majority of 16S rDNA libraries from bacterial communities from inshore areas of Lake Michigan (Mueller-Spitz et al., 2009). Deltaproteobacteria, especially anaerobes such as Anaeromyxobacter or Desulfuromonas, were the most dominant taxa identified in the dead zone, making up 34% of characterized sequences. The dead zone experiences the most hypoxic conditions in the late summer and early fall, the time at which our samples were collected, but is reduced or nonexistent for the remainder of the year. This seasonal change in dissolved oxygen is likely to result in significant changes in the composition of the microbial community. The observation of Acidobacteria, Bacteroidetes, and Nitrospira in all four Lake Erie regions may reflect the metabolic versatility of these groups. Acidobacteria are often prevalent constituents in freshwater sediments (Kouridaki et al., 2010; Zeglin et al., 2011), while also being observed in diverse environments such as hot springs (Barns et al., 1996), as well as sewage sludge (Layton et al., 2000) and a wastewater treatment plant (LaPara et al., 2000). Bacteroidetes metabolize a wide range of high-weight carbohydrates and proteins in aquatic sediments (Teske et al., 2011), while Nitrospira is one of the most highly diverse groups of nitrite-oxidizing bacteria that contributes substantially to nitrification in freshwater systems (Lücker et al., 2010). The finding of eleven archaeal sequences was not surprising because the primers used to obtain our environmental DNA libraries showed complete sequence matches with archaeal 16S rDNA sequences. The identification of six sequences as belonging to the Thermoprotei was notable because Archaea within this class are thermophiles and hyperthermophiles typically discovered in hydrothermal vents (Lanzén et al., 2011), hot springs (Song et al., 2010), as well as temperate freshwater lake sediments (Pazinato et al., 2010). While a great variety of bacterial groups was detected in the sediment samples, most estimates of species richness and species diversity based on SPADE showed no significant differences in the number of taxa and the abundances of the most common taxonomic groups among Lake Erie regions (Table 2). However, the spatial analysis of microbial diversity based on Fast UniFrac revealed significant spatial structure (UniFrac P-value = 0.002; P-test = 0.001) of microbial lineages associated with particular regions of Lake Erie, consistent with the idea that local adaptation may be favoring particular lineages in specific regions. The analysis of each individual region revealed that the inshore sites of the Central basin were the main contributors to the spatial structuring of microbial lineages, since this was the only region that showed a significant P-test (0.04) when regions were tested individually. This finding was also consistent with the SPADE analysis, which showed that this region had the highest species richness under the maximum likelihood estimate (Table 2). However, some caution should be taken in interpreting the observed patterns,

350


given that limited replication in the number of sites and environmental clones analyzed per region may prevent a thorough characterization of the sediment microbial communities across Lake Erie. Furthermore, periodic sampling (e.g., sampling during different seasons) may provide insights into the temporal dynamics of bacterial communities. The principal component analysis revealed similarities between the microbial communities of the near-shore sites of the Central basin and the dead zone. These regions were consistently grouped under PC1 and PC2, both of which explained over 65% of the observed variance (Fig. 3A). The association of the inshore sites from the Central basin and the dead zone may be expected given the close proximity of the areas (both major regions of the Central basin) and the significant mixing of water throughout the Central basin (Edwards et al., 2005). The principal component analysis also revealed the Eastern basin as the most distinctive region. The Eastern basin has a relatively smaller drainage basin and fewer riparian inputs compared to the Central and Western basins (Martin, 2002; Painter et al., 2001), which may explain the distinctiveness of microbial communities from this basin. Results from this study provide preliminary insights into the spatial variability of bacterial communities in surface sediments throughout Lake Erie. Environmental DNA sampling revealed bacterial groups with diverse metabolic capabilities. Overall, environmental gradients and geophysical characteristics of the three major basins and the dead zone do not seem to drive significant differences in taxa diversity among Lake Erie regions. Studies on Lake Michigan (Mueller-Spitz et al., 2009) and a group of thermally stratified lakes within the Great Lakes basin (Konopka et al., 1999) showed similar levels of diversity in the dominant bacterial taxa across sample locations, regardless of differences in geophysical characteristics. However, in this study, the Fast UniFrac analysis revealed significant spatial structuring of microbial community composition throughout Lake Erie. In particular, microbial lineages from the inshore sites of the Central basin showed higher phylogenetic uniqueness than would be expected if they were randomly distributed with taxonomic groups from other regions throughout the tree. This result, along with the relatively higher proportions of Gamma- and Betaproteobacteria in the Central Basin, is consistent with the hypothesis that local adaptation of microbial communities, potentially from the relatively high number of riverine inputs into the Central basin, may modulate community metabolism by shifts in community composition (Findlay et al., 2003). These results were also consistent with those from functional studies on microbial community metabolism, which showed that differences in dissolved organic matter and heavy metal concentrations play a significant role in the local adaptation of microbial communities, determining spatial patterns of microbial functional diversity in Lake Erie sediments (Hoostal and Bouzat, 2008; Hoostal et al., 2008). This relationship between the genetic and functional diversity of microbial communities underscores the importance of both molecular (taxonomic characterization) and functional (microbial community metabolism) studies for understanding ecological and evolutionary processes associated with microbial communities.

Acknowledgments We would like to thank Drs. George Bullerjahn, Michael McKay, Hunter J. Carrick, and three anonymous reviewers for their constructive comments on earlier versions of the manuscript. We also thank Dr. McKay and David Porta for the collection of sediment samples and Dr. Omaththage P. Perera for technical support. Financial support for this research was provided by a National Science Foundation DDIG grant to MH and JLB, and the Department of Biological Sciences at BGSU. Appendix A. Supplementary Information Supplementary data to this article can be found online at http:// dx.doi.org/10.1016/j.jglr.2013.03.003.

References Achour, A.R., Bauda, P., Billard, P., 2007. Diversity of arsenite transporter genes from arsenic-resistant soil bacteria. Res. Microbiol. 158 (2), 128–137. Allan, J., Castillo, M.M., 2007. Stream Ecology: Structure and Function of Running Waters. Springer, Dordrecht, The Netherlands. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J., 1990. Basic local alignment search tool. J. Mol. Biol. 215, 403–410. Anderson, I., Göker, M., Nolan, M., Lucas, S., Hammon, N., Deshpande, S., Cheng, J., Tapia, R., Han, C., Goodwin, L., Pitluck, S., Huntemann, M., Liolios, K., Ivanova, N., Pagani, I., et al., 2011. Complete genome sequence of the hyperthermophilic chemolithoautotrophic Pyrolobus fumarii type strain (1A). Stand. Genomic. Sci. 4 (3), 381–392. Barns, S.M., Delwiche, C.F., Palmer, J.D., Pace, N.R., 1996. Perspectives on archaeal diversity, thermophily and monophyly from environmental rRNA sequences. PNAS 93 (17), 9188–9193. Bramucci, M., Kane, H., Chen, M., Nagarajan, V., 2003. Bacterial diversity in an industrial waste reactor. Appl. Microbiol. Biotechnol. 62 (5–6), 594–600. Bunge, J., Barger, K., 2008. Parametric models for estimating the number of classes. Biom. J. 50 (6), 971–982. Burkert, U., Warnecke, F., Babenzien, D., Zwirnmann, E., Pernthaler, J., 2003. Members of a readily enriched β-proteobacterial clade are common in the surface waters of a humic lake. Appl. Environ. Microbiol. 69, 6550–6559. Burnham, K.P., Overton, W.S., 1978. Estimation of the size of a closed population when capture probabilities vary among animals. Biometrika 65 (3), 625–633. Burns, N.M., 1985. Erie: The Lake that Survived. Rowman and Allanheld, Totowa, NJ. Chao, A., Shen, T.J., 2003. Nonparametric estimation of Shannon's index of diversity when there are unseen species in sample. Environ. Ecol. Stat. 10 (4), 429–443. Chróst, R.J., Rai, H., 1993. Ectoenzyme activity and bacterial secondary production in nutrient-impoverished and nutrient-enriched freshwater mesocosms. Microb. Ecol. 25, 131–150. Cole, J.R., Chai, B., Farris, R.J., Wang, Q., Kulam, S.A., McGarrell, D.M., Garrity, G.M., Tiedje, J.M., 2005. The Ribosomal Database Project (RDP-II): sequences and tools for high-throughput rRNA analysis. Nucleic Acids Res. 33, D294–D296. Cottrell, M.T., Kirchman, D.L., 2000. Natural assemblages of marine proteobacteria and members of the Cytophaga-Flavobacter cluster consuming low- and high-molecular-weight dissolved organic matter. Appl. Environ. Microbiol. 66, 1692–1697. Crump, B.C., Hopkinson, C.S., Sogin, M.L., Hobbie, J.E., 2004. Microbial biogeography along an estuarine salinity gradient: combined influences of bacterial growth and residence time. Appl. Environ. Microbiol. 70 (3), 1494–1505. Edwards, W.J., Conroy, J.D., Culver, D.A., 2005. Hypolimnetic oxygen depletion dynamics in the Central Basin of Lake Erie. J. Great Lakes Res. 31 (Suppl. 2), 262–271. Fierer, N., Lennon, J.T., 2011. The generation and maintenance of diversity in microbial communities. Am. J. Bot. 98, 1–10. Findlay, S.E.G., Sinsabaugh, R.L., Sobczak, W.V., Hoostal, M.J., 2003. Metabolic and structural response of hyporheic microbial communities to variations in supply of dissolved organic matter. Limnol. Oceanogr. 48 (4), 1608–1617. Freeman, C., Lock, M.A., Marxsen, J., Jones, S.E., 1990. Inhibitory effects of high molecular weight dissolved organic matter upon metabolic processes in biofilms from contrasting rivers and streams. Freshw. Biol. 24, 159–166. Haaijer, S.C.M., Harhangi, H.R., Meijerink, B.B., Strous, M., Pol, A., Smolders, A.J.P., Verwegen, K., Jetten, M.S.M., den Camp, H.O., 2008. Bacteria associated with iron seeps in a sulfur-rich, neutral pH, freshwater ecosystem. ISME J. 2, 1231–1242. Hair, J.F., Anderson, R.E., Tatham, R.L., 1987. Multivariate Data Analysis, 2nd ed. Macmillan, New York. Hall, T.A., 1999. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp. Ser. 41, 95–98. Hamady, M., Lozupone, C., Knight, R., 2010. Fast UniFrac: facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data. ISME J. 4, 17–27. Hong, S., Bunge, J., Jeon, S., Epstein, S.S., 2006. Predicting microbial species richness. PNAS 103 (1), 117–122. Hoostal, M.J., Bouzat, J.L., 2008. The modulating role of dissolved organic matter on spatial patterns of microbial metabolism in Lake Erie sediments. Microb. Ecol. 55, 358–368. Hoostal, M.J., Bidart-Bouzat, M.G., Bouzat, J.L., 2008. Local adaptation of microbial communities to heavy metal stress in polluted sediments of Lake Erie. FEMS Microbiol. Ecol. 65, 156–168. Hunger, S., Schmidt, O., Hilgarth, M., Horn, M.A., Kolb, S., Conrad, R., Drake, H.L., 2011. Competing formate- and carbon dioxide-utilizing prokaryotes in an anoxic methane-emitting fen soil. Appl. Environ. Microbiol. 77 (11), 3773–3785. Judd, K.E., Crump, B.C., Kling, G.W., 2006. Variation in dissolved organic matter controls bacterial production and community composition. Ecology 87 (8), 2068–2079. Konopka, A., Bercot, T., Nakatsu, C., 1999. Bacterioplankton community diversity in a series of thermally stratified lakes. Microb. Ecol. 38, 126–135. Kouridaki, I., Paraskevi, P.N., Tselepides, A., Mandalakis, M., Smith, K.L., 2010. Phylogenetic diversity of sediment bacteria from the deep Northeastern Pacific Ocean: a comparison with the deep Eastern Mediterranean Sea. Int. Microbiol. 13 (3), 143–150. Kumar, S., Tamura, K., Nei, M., 2004. MEGA 3: Integrated software for Molecular Evolutionary Genetics Analysis and sequence alignment. Brief. Bioinform. 5, 150–163. Lane, D.J., Pace, B., Olsen, G.J., Stahl, D.A., Sogin, M.L., Pace, N.R., 1985. Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. PNAS 82, 6955–6959. Langenheder, S., Lindstrom, E.S., Tranvik, L.J., 2006. Structure and function of bacterial communities emerging from different sources under identical conditions. Appl. Environ. Microbiol. 72 (1), 212–220. Lanzén, A., Jørgensen, S.L., Bengtssson, M.M., Jonassen, I., Øvreås, U.T., 2011. Exploring the composition and diversity of microbial communities at the Jan Mayen hydrothermal vent field using RNA and DNA. FEMS Microbiol. Ecol. 77 (3), 577–589.

J.L. Bouzat et al. / Journal of Great Lakes Research 39 (2013) 344–351 LaPara, T.M., Nakatsu, C.H., Pantea, L., Alleman, J.E., 2000. Phylogenetic analysis of bacterial communities in mesophilic and thermophilic bioreactors treating pharmaceutical wastewater. Appl. Environ. Microbiol. 66, 3951–3959. Layton, A.C., Karanth, P.N., Lajoie, C.A., Meyers, A.J., Gregory, I.R., Stapleton, R.D., Taylor, D.E., Sayler, G.S., 2000. Quantification of Hyphomicrobium populations in activated sludge from an industrial wastewater treatment system as determined by 16S rRNA analysis. Appl. Environ. Microbiol. 66 (3), 1167–1174. Lozupone, C., Knight, R., 2005. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71 (12), 8228–8235. Lücker, S., Wagner, M., Maixner, F., Pelletier, E., Koch, H., Vacherie, B., Rattei, T., Sinninghe Damsté, J.F., Spieck, E., Le Paslier, D., Daims, H., 2010. A Nitrospira metagenome illuminates the physiology and evolution of globally important nitrite-oxidizing bacteria. PNAS 107 (30), 13479–13484. Martin, A.P., 2002. Phylogenetic approaches for describing and comparing the diversity of microbial communities. Appl. Environ. Microbiol. 68 (8), 3673–3682. Mueller-Spitz, S.R., Goetz, G.W., McLellan, S.L., 2009. Temporal and spatial variability in nearshore bacterioplankton communities of Lake Michigan. FEMS Microbiol. Ecol. 67 (3), 511–512. Nicholls, K.H., Hopkins, G.J., 1993. Recent changes in Lake Erie phytoplankton: cumulative impacts of phosphorus loading reductions and Zebra mussel introduction. J. Great Lakes Res. 19, 637–647. Nold, S.C., Pangborn, J.B., Zajack, H.A., Kendall, S.T., Rediske, R.R., Biddanda, B.A., 2010. Benthic bacterial diversity in submerged sinkhole ecosystems. Appl. Environ. Microbiol. 76, 347–351. Paerl, H.W., Pinckney, J.L., 1996. A mini-review of microbial consortia: their roles in aquatic production and biogeochemical cycling. Microbiol. Ecol. 31, 225–247. Painter, S., Marvin, C., Fernando, R., Reynoldson, T.B., Charlton, M.N., Fox, M., Thiessen, P.A., Estenik, J.F., 2001. Sediment contamination in Lake Erie: a 25-year retrospective analysis. J. Great Lakes Res. 27 (4), 434–448. Pazinato, J.M., Paulo, E.N., Mendes, L.W., Vazoller, R.F., Tsai, S.M., 2010. Molecular characterization of the archaeal community in an Amazonian wetland soil and culturedependent isolation of methanogenic Archaea. Diversity 2, 1026–1047. Revsbech, N.P., Risgaard-Petersen, N., Schramm, A., Nielsen, L.P., 2006. Nitrogen transformation in stratified aquatic microbial ecosystems. Antonie Van Leeuwenhoek 90 (4), 361–375.

351

Ronquist, F., Huelsenbeck, J.P., 2003. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19 (12), 1572–1574. Scheid, D., Stubner, S., Conrad, R., 2004. Identification of rice root associated nitrate, sulfate, and ferric iron reducing bacteria during root decomposition. FEMS Microbiol. Ecol. 50 (2), 101–110. Schwieger, F., Tebbe, C.C., 2000. A new approach to utilize PCR-single-strand conformation polymorphism for 16S rRNA gene-based microbial community analysis. Appl. Environ. Microbiol. 64, 4870–4876. Sokolova, T.G., Henstra, A., Sipma, J., Parshina, S.N., Stams, A.J.M., Lebedinksy, A.V., 2009. Diversity and ecophysiological features of thermophilic carboxydotrophic anaerobes. FEMS Microbiol. Ecol. 68 (2), 131–141. Song, Z., Chen, J., Jiang, H., Zhou, E., Tang, S., Zhi, X., Zhang, L., Zhang, L., Li, W., 2010. Diversity of Crenarchaeota in terrestrial hot springs in Tengchong, China. Extremophiles 14, 287–296. Swofford, D.L., 2003. PAUP*. Phylogenetic Analysis Using Parsimony (*and Other Methods). v. 4. Sinauer Associates, Sunderland, Massachusetts. Tamura, K., Nei, M., 1993. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol. Biol. Evol. 10, 512–526. Teske, A., Durbin, A., Ziervogel, K., Cox, C., Arnosti, C., 2011. Microbial community composition and function in permanently cold seawater and sediments from an arctic fjord of Svalbard. Appl. Environ. Microbiol. 77 (6), 2008–2018. Wang, Q., Garrity, G.M., Tiedje, J.M., Cole, J.R., 2007. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73 (16), 5261–5267. Wilhelm, S.W., Bullerjahn, G.S., Eldridge, M.L., Rinta-Kanto, J.M.R., Poorvin, L., Bourbonniere, R.A., 2006. Seasonal hypoxia and the genetic diversity of prokaryote populations in the Central Basin hypolimnion of Lake Erie: evidence for abundant cyanobacteria and photosynthesis. J. Great Lakes Res. 32, 657–671. Zahl, S., 1977. Jackknifing an index of diversity. Ecology 58, 907–913. Zeglin, L.H., Dahm, C.N., Sinsabaugh, R.L., Barrett, J.E., Gooseff, M.N., Fitzpatrick, S.K., Takacs-Vesbach, C.D., 2011. Bacterial community structure along moisture gradients in the parafluvial sediments of two ephemeral desert streams. Microb. Ecol. 61, 543–556.