Genetic diversity patterns of microbial communities in a ... - Springer Link

Hydrobiologia (2011) 678:113–125 DOI 10.1007/s10750-011-0834-x

PRIMARY RESEARCH PAPER

Genetic diversity patterns of microbial communities in a subtropical riverine ecosystem (Jiulong River, southeast China) Lemian Liu • Jun Yang • Yongyu Zhang

Received: 27 January 2011 / Revised: 7 July 2011 / Accepted: 23 July 2011 / Published online: 5 August 2011 Ó Springer Science+Business Media B.V. 2011

Abstract Prokaryotic and eukaryotic microbes are key organisms in aquatic ecosystems and play pivotal roles in the biogeochemical cycles, but little is known about genetic diversity of these communities in subtropical rivers. In this study, microbial planktonic communities were determined by using denaturing gradient gel electrophoresis (DGGE) analysis from the Jiulong River, southeast China, and their relationships with local environmental factors were studied. The Betaproteobacteria (26%) and Dinophyceae (26%) were the most dominant taxa in prokaryotic and eukaryotic clones derived from DGGE bands, respectively. Further, both cluster and ordination analyses of prokaryotic and eukaryotic DGGE fingerprinting resulted in three identical groups from

Electronic supplementary material The online version of this article (doi:10.1007/s10750-011-0834-x) contains supplementary material, which is available to authorized users. Handling editor: Stefano Amalfitano L. Liu J. Yang (&) Y. Zhang Aquatic Ecohealth Group, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen 361021, People’s Republic of China e-mail: [email protected] L. Liu Graduate School of Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100039, People’s Republic of China

the 15 sites, which were closely related with the environmental factors. Partial redundancy analysis (partial RDA) revealed that agricultural pollution (phosphorus and nitrogen) and saltwater intrusion (conductivity and salinity) were the main factors impacting microbial community composition, by explaining more than two-thirds of the total variation in both prokaryotic (67.0%) and eukaryotic (70.5%) communities. Moreover, the robust and quantifiable relationship between DGGE results and environmental variables indicated that the community-level molecular fingerprinting techniques could support the physicochemical assessment of riverine water quality and ecosystem health. Keywords Plankton Microbial community composition DGGE Redundancy analysis Biomonitoring Subtropical river

Introduction Microorganisms, including bacteria, algae, protozoa, and small metazoa, play essential roles in the biogeochemical processes in aquatic ecosystems because they constitute the base of food webs and their changes in composition and structure can lead to profound changes at high trophic levels (Pomeroy et al., 2007). Thus, it is important to investigate changes in microbial community for assessing aquatic ecosystem health. However, traditional morphological and

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culture-dependent approaches, especially for prokaryotic microbes, can detect only a very small proportion of the total microbial assemblage due to the difficulties of discriminating morphologically similar organisms, and the limitations of a majority of microbes being unculturable in field samples (Amann et al., 1995; Vartoukian et al., 2010). To overcome these limitations, molecular fingerprinting techniques have been showed to be powerful tools for assessing the composition and structure of a microbial community (Diez et al., 2001; Danovaro et al., 2006). Among all of the available fingerprinting approaches, polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) is a powerful one, and can rapidly provide a tangible characterization of community diversity and composition, and shifts in population can be readily demonstrated. Recently, this method has become routinely used to study microbial ecology and diversity in a number of different aquatic ecosystems (Diez et al., 2001; Sakami, 2008; Wu et al., 2009). However, previous studies of microbial communities have largely focused on the analysis of prokaryotic 16S rRNA genes; in comparison studies using eukaryotic 18S rRNA genes are strikingly scarce (Santos et al., 2010). Further, DGGE has rarely been applied in lotic (running water) systems, and we have limited knowledge about the spatial patterns of microbial communities and the factors that influence them in lotic systems (Cody et al., 2000; Leff, 2002; Passy, 2007). Lotic systems are characterized by greater environment heterogeneity compared to lentic systems, and therefore should greatly influence the microbial community diversity (Caron et al., 1999). To improve our knowledge of microbial ecology in lotic environment, it is necessary to understand the genetic diversity of these assemblages and the factors controlling their distribution and community structure. The Jiulong River, the second largest river in Fujian province, southeast China, is an important water source for drinking, agricultural and industrial use. The Jiulong River Watershed is home to about nine millions people and characterized by intense agricultural activities (Wang et al., 2005). Previous studies were carried out in this river to describe the bacterial community structure in estuarine region (Tian et al., 2008). Unfortunately, the composition and structure of microbial planktonic communities (including both prokaryotic and eukaryotic organisms) from the whole-river are still largely unknown,

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Hydrobiologia (2011) 678:113–125

although they are potentially an effective indicator of water quality and environmental contamination. The aims of the study were (1) to investigate the genetic diversity pattern of microbial communities in a subtropical river (i.e., Jiulong River); (2) to determine the impact of environmental factors, particularly agricultural pollution and saltwater intrusion, on microbial communities; and (3) to supplement existing information for biomonitoring activities based on community-level molecular fingerprinting techniques.

Methods Study area and sampling The Jiulong River (116°4600 550 –118°0200 170 E, 24°230 5300 –25°530 3800 N) has three tributaries with a total length of 285 km and a drainage area of 14,745 km2. It is characterized by subtropical monsoon climate with an annual mean temperature of 19.9–21.1°C and an annual mean precipitation of 1,400–1,800 mm. The rainfall is concentrated in spring and summer (April–September), while winter rainfall is much lower (Wang et al., 2005). During the winter (dry season), the discharge of freshwater and sediments into estuary is great reduced, thus the saltwater intrusion often occurs in previous freshwater areas. The Jiulong River Watershed is one of the most important agricultural regions. Unfortunately, the water quality problems, caused by nitrogen and phosphorus pollution from intensive agricultural activities (cultivation and animal feeding), have dramatically and rapidly increased in upper Jiulong River over the past decades. Especially, in dry season (e.g., winter), most nutrients were concentrated due to the negative balance between precipitation and evaporation, which had a detrimental impact on the water quality (Guan et al., 2005). Fifteen sites were selected and sampled along the Jiulong River in January 2010 (Fig. 1). Sites 1–3 from the upper Jiulong River were located in a typical agricultural region of large-scale cultivation and intensive livestock/poultry farming. Sites 4–12 and sites 13–15 were at the middle-lower Jiulong River and estuary, respectively. Water samples for planktonic microbial and physicochemical analyses were taken from the surface


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Fig. 1 Map of the Jiulong River showing location of sampling sites

(upper 50 cm) of the middle of the river. Microbial community samples (500 ml water) for DGGE analysis were collected on 0.2-lm pore size polycarbonate filters (47 mm diameter, Millipore, USA) after pre-filtration through a 200-lm mesh to remove the larger debris and metazoa. The filters were stored at -80°C until DNA extraction. Environmental data collection Water temperature, pH, dissolved oxygen (DO), conductivity, and salinity were measured in situ with a Horiba W-23XD Multi-Parameter Water Quality Meter (Horiba, Japan). Water transparency was assessed with a Secchi disc (30 cm diameter). Suspended solids (SSs) were determined gravimetrically by filtering 350 ml water sample through a preweighed filter (pore size of 0.45 lm), then weighing the filter again after drying at 105°C. Total organic carbon (TOC) and total nitrogen (TN) were determined using a Shimadzu TOC-VCPH analyzer (Shimadzu, Japan). Total phosphorus (TP) was analyzed by spectrophotometry after digestion. Ammonium nitrogen (NH4-N), nitrite and nitrate nitrogen (NOxN) and phosphate phosphorus (PO4-P) were measured

with a Lachat QC8500 Flow Injection Analyzer (Lachat Instruments, USA). DNA extraction and PCR amplification Total DNA was extracted using an E.Z.N.A. Soil DNA Kit (Omega Bio-Tek, USA) according to the manufacturer’s instructions. The extracted DNA was dissolved in 50 ll TE buffer, quantified by spectrophotometer and stored at -20°C until further use. The 16S rRNA and 18S rRNA gene fragments were amplified with the prokaryotic primers 341F-GC (50 CGC CCG CCG CGC CCC GCG CCC GTC CCG CCG CCC CCG CCC GCC TAC GGG AGG CAG CAG-30 ) and 907R (50 -CCG TCA ATT CMT TTG AGT TT-30 ) (Scha¨fer & Muyzer, 2001) and the eukaryotic primers Euk1A (50 -CTG GTT GAT CCT GCC AG-30 ) and Euk516r-GC (50 -ACC AGA CTT GCC CTC CCG CCC GGG GCG CGC CCC GGG CGG GGC GGG GGC ACG GGG GG-30 ) (Diez et al., 2001), respectively. PCR mixtures (50 ll) contained 19 PCR buffer, 1.5 mM MgCl2, 200 lM each deoxynucleoside triphosphate, 0.3 lM of each primer, 2.5 U of Taq DNA polymerase (TaKaRa, Japan), and approximately 40 ng of template DNA. The PCR

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program for prokaryotic primers included an initial denaturation at 94°C for 5 min and ten touchdown cycles of denaturation at 94°C for 0.5 min, annealing at 67°C (with the temperature decreasing 1°C each cycle) for 0.5 min, and extension at 72°C for 1 min, followed by 20 cycles of 94°C for 0.5 min, 57°C for 0.5 min, and 72°C for 1 min. The PCR program for eukaryotic primers included an initial denaturation at 94°C for 5 min and ten touchdown cycles of denaturation at 94°C for 0.5 min, annealing at 68°C (with the temperature decreasing 1°C each cycle) for 0.5 min, and extension at 72°C for 1 min, followed by 20 cycles of 94°C for 0.5 min, 58°C for 0.5 min, and 72°C for 1 min. During the last cycle of both programs, the length of the extension step was increased to 10 min.

DNA Purification Kit (Takara, Japan), then cloned into a pMD18-vector (Takara, Japan) and transformed into Escherichia coli DH5a competent cells (Takara, Japan). Finally, successfully inserted plasmids were sequenced unidirectionally using an automated sequencer (ABI3730, USA). All 16S rRNA and 18S rRNA sequences were aligned in Bioedit, version 7.0.4.1, using Clustal W. To determine their phylogenetic affiliation, each sequence was compared with sequences available in GenBank databases using BLAST, and the closest relatives were identified for phylogenetic analysis.

DGGE

In order to display the main gradients in environmental variables, principal component analysis (PCA) was performed on the physicochemical parameters. Prior to analysis, all data were first log(x ? 1) transformed with the exception of pH to improve normality and homoscedasticity. Analysis of variance (ANOVA) was used in combination with Scheffe’s F multiple-comparison test to examine differences among the parameters of the sampling sites. DGGE profiles were transformed into binary code, scoring each position as 1 (present) or 0 (absent). Bray–Curtis similarity (presence/absence-based) matrices were constructed with the DGGE profiles generated from each site. Then, the cluster analysis and non-metric multidimensional scaling (MDS) ordination were used to investigate differences in microbial communities between sites (Clarke & Gorley, 2001). A group-average linked method was used in cluster analysis. A measure of goodness-of-fit of the ordination is given by a stress value (Kruskal’s stress formula) that should be \0.20 to minimize misinterpretation (Kruskal, 1964). The significant differences (P \ 0.01) between groups were evaluated using the randomization/permutation procedure ANOSIM (analysis of similarities). The global R statistic ranges from 0 to 1 and indicates the overall degree of separation between groups of sites, and no separation is indicated by R = 0, whereas R = 1 suggests complete separation. To explore the relationships between microbial communities and environmental variables both redundancy analysis (RDA) and RELATE analysis were

DGGE was performed with a DCode mutation detection system (Bio-Rad, USA). Samples containing approximately equal amounts of PCR amplicons were loaded onto 6% (w/v) polyacrylamide gels (37.5:1 acrylamide:bisacrylamide) in 19 Tris–acetate–EDTA (TAE) buffer. The denaturing gradient of 30–60% was applied for separation of the 16S rRNA genes, and 25–55% for the 18S rRNA genes (100% denaturant is defined as 7 M urea and 40% (v/v) deionized formamide), respectively. Electrophoresis was performed at 60°C with a constant voltage of 100 V for 16 h. The DGGE gels were stained with SYBR Green I nucleic acid stain for 30 min in 19 TAE buffer, rinsed in distilled water, and then visualized with UV radiation by using Gel Doc EQ imager (Bio-Rad, USA). DGGE patterns were analyzed using the Quantity One software (Bio-Rad, USA) (Schauer et al., 2000), and were carefully checked and corrected manually. The bands occupying the same position in the different lanes of the gel were identified. Binary matrices were constructed for all lanes, taking into account the presence (1) or absence (0) of individual bands. Sequencing and phylogenetic analysis Dominant DGGE bands were excised from the gels and eluted overnight in sterilized distilled water at 4°C. The eluted DNA was used for re-amplification with the original primer set (without GC clamp). PCR products were purified with the TaKaRa Agarose Gel

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Data analysis


performed. Preliminary detrended correspondence analysis (DCA) on the microbial data revealed that the longest gradient lengths were shorter than 3.0, indicating that the majority of species exhibited linear responses to the environmental variation. RDA analysis examines variations in the community structure by constraining ordination axes to linear combinations of environmental variables. The environmental variables are represented by arrows pointing in the direction of maximum change, and the arrow length is indicative of the importance of each environmental variable. A forward selection procedure was first applied to all environmental variables, and the significance of the conditional effects was detected each time by 999 Monte Carlo permutations of the residuals of the full regression models (ter Braak & Sˇmilauer, 2002). RELATE works by relating community similarity matrix to environmental distance matrix by calculating rank correlation between the two matrices, thus provides a significance test with the matching coefficient qm, which is equivalent to the Mantel’s test (Clarke & Gorley, 2001). To perform the RELATE analysis, the variables of agricultural pollution variables and saltwater intrusion were used to calculate a similarity matrix using the normalized Euclidean distance. We referred to positive and negative correlation levels between 0.5 and 1 as strong relations at P B 0.01. Variation partitioning was conducted to distinguish the relative importance of different sets of environmental variables in determining community structure of prokaryotes or eukaryotes. Two sets of potential explanatory variables were used: agricultural pollution (TN, NH4-N, NOx-N, TP, and PO4-P) and saltwater intrusion (conductivity and salinity). This analysis segregates total variation in the community matrix into those explained independently by agricultural pollution and saltwater intrusion, respectively, and their shared portions with corresponding P values by using the partial RDA. All data analyses were performed with the PRIMER 5.0 and the CANOCO 4.5 software packages. Nucleotide sequence accession numbers The 16S and 18S rRNA gene sequences from this study were obtained and deposited in GenBank under the accession numbers JF260873 to JF260910.

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Results Environmental characteristics Thirteen environmental variables from 15 sampling sites are summarized in Supplementary Table A (Electronic supplementary material). The concentrations of all nitrogen and phosphorus in the upper stream sites (site 1–3) were significant higher than those of middle-lower stream sites (site 4–12) and estuarine sites (site 13–15) (ANOVA, P \ 0.05) due to agricultural pollution. Notably, the concentrations of TN at all sites and TP at six sites exceeded 1.0 and 0.2 mg l-1, respectively, which are the standard limit for safe drinking water (Environmental Quality Standards for Surface Water, China GB3838-2002). PCA of 13 environmental parameters provides a clear distinction between three groups of sites (Fig. 2): Group I was composed of 3 sites in the upper Jiulong River, Group II included nine sites in the middlelower Jiulong River, and Group III consisted of three estuarine sites. The first two axes explained 91.6% of the total variability and effectively captured the main

Fig. 2 PCA plots showing the resemblance of environmental characteristics of sampling sites in Jiulong River. Three main groups are identified. Numbers are sampling sites (see Fig. 1 for location). (Temp water temperature, DO dissolved oxygen, Cond conductivity, Sal salinity, SD Secchi depth, SS suspended solid, TP total phosphorus, TN total nitrogen, TOC total organic carbon)

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patterns of variation in the original variables. Along the first axis, variability was mainly explained by an increase in the conductivity and salinity. Variability along the second axis mainly corresponded to an increase in concentrations of SS, TN, NH4-N, NOx-N, TP, PO4-P and a decrease in Secchi depth (SD). Microbial community The DGGE profiles showed high variation in band numbers, position and intensity among different sites for the microbial planktonic communities (Supplementary Fig. A, Electronic supplementary material). There were marked differences in the DGGE fingerprinting between prokaryotic and eukaryotic microbial communities. More distinct DGGE bands were found for prokaryotes (75) than for eukaryotes (52), suggesting that prokaryotic diversity was higher than that of eukaryotes. Only 8 and 4% of the total DGGE bands were common to all sites for prokaryotes and eukaryotes, respectively. In total, 38 prominent DGGE bands were successfully sequenced to obtain further information of the dominant microbial populations (Supplementary Fig. A, Electronic supplementary material). Six prokaryotic and eight eukaryotic groups were identified; the Betaproteobacteria (26%) and Dinophyceae (26%) were the most dominant taxa in prokaryotic and eukaryotic communities, respectively (Tables 1, 2). Eleven (58%) prokaryotic sequences and 14 (74%) eukaryotic sequences were similar to the uncultured organisms obtained from different environmental samples such as freshwater, marine, and soil. Interestingly, three identical groups were clearly distinguished by clustering and ordination for both prokaryotic and eukaryotic microbial communities (Fig. 3). The analysis of similarity (ANOSIM) showed a global R value of 0.872 for prokaryotes and 0.937 for eukaryotes at P \ 0.001, indicating a good separation between groups. Group I was characterized by both communities from highly agricultural pollution sites 1–3. Group II was characterized by communities from less polluted sites 4–12. Group III consisted of three estuarine sites with high levels of conductivity and salinity. It appears that agricultural pollution and saltwater intrusion maybe account for this pattern in genetic structure of both prokaryotic and eukaryotic communities.

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Relationships between microbial communities and environmental factors The RDA for prokaryotic and eukaryotic communities yielded similar patterns as both communities resulted in the same three clusters of sites separated by conductivity and NH4-N (Fig. 4). Forward selection in RDA identified NH4-N and conductivity were significant in explaining a portion of the variation in both communities composition in the Jiulong River (P \ 0.01). Both prokaryotic and eukaryotic communities from the upper Jiulong River were placed on the upper right region of the ordination (Fig. 4), whereas estuarine communities were situated at the lower right corner. The cumulative variance of the species–environment relationship explained by the first two RDAs were 32.3% in prokaryotic communities and 42.1% in eukaryotic communities, respectively (Fig. 4). The qualitative observation that NH4N and conductivity were closely related to the structure of both communities was confirmed by the RELATE analysis (Supplementary Table B, Electronic supplementary material). Variance partitioning using partial RDA showed that agricultural pollution and saltwater intrusion combined explained more than 67.0% of the total variation in prokaryotic and eukaryotic communities (Table 3). The amount of variation explained was slightly higher for eukaryotes (70.5%) compared to prokaryotes (67.0%). The relative contribution of agricultural pollution factors (48.0–55.9%) was considerably larger than that of saltwater intrusion factors (26.2–28.0%) in both communities. For prokaryotic communities, the pure variance explained by agricultural pollution (39.0%) was higher than that explained by pure saltwater intrusion (19.0%). Similarly, for eukaryotic communities the pure variance explained by agricultural pollution (44.3%) was substantially higher than that explained by pure saltwater intrusion (14.6%).

Discussion Diversity of microbial communities A total of 127 bands (75 of the 16S rDNA fragments and 52 of the 18S rDNA fragments) were detected on


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Table 1 Phylogenetic affiliations of 16S rRNA gene sequences from DGGE bands Taxon

DGGE band

Accession number

Closest relative (accession number)

Similarity

Betaproteobacteria

B-7-1

JF260875

Beta proteobacterium strain HIBAF013 (AB452987)

Betaproteobacteria

B-8-2

JF260877

Beta proteobacterium strain USHIF005 (AB470459)

99

Freshwater

Betaproteobacteria

B-8-4

JF260879

Uncultured bacterium clone DP10.1.20 (FJ612322)

99

Freshwater

Betaproteobacteria

B-10-1

JF260884

Uncultured bacterium clone zxh-8-19 (GU323668)

99

Freshwater

Betaproteobacteria

B-10-2

JF260885

Sphaerotilus natans strain 380 (GU591793)

99

Freshwater

CFB group

B-9-1

JF260881

Bacterium clone LW9m-1-87 (EU641393)

99

Freshwater

CFB group

B-9-2

JF260882

Uncultured bacterium clone SING677 (HM129286)

99

Freshwater

CFB group

B-13-2

JF260887

Polaribacter sp. strain a500 (FN433005)

99

Marine

CFB group

B-14-2

JF260889

Polaribacter sp. strain J10-4 (FJ425216)

99

Marine

Firmicutes

B-8-5

JF260880

Uncultured bacterium clone b32(GQ472804)

99

Marine

Firmicutes

B-9-3

JF260883

Uncultured bacterium clone b32 (GQ472804)

99

Marine

Firmicutes

B-13-1

JF260886

Bacillus sp. strain PCWCS15 (GQ284380)

93

Sediment of natural spring

Firmicutes

B-15-1

JF260890

Bacterium isolate 8-gw2-7 (DQ990044)

100

Freshwater

Cyanobacteria

B-8-1

JF260876

Uncultured phototrophic eukaryote clone PRD18H03 (AY948067)

99

Freshwater

Cyanobacteria

B-8-3

JF260878

Uncultured bacterium isolate DGGE band LK5_24 (GQ336919)

98

Soil

Actinobacteria

B-6-1

JF260873

Uncultured bacterium clone zxh-8-4 (GU323607)

100

Freshwater

Actinobacteria

B-9-4

JF260891

Uncultured actinobacterium clone GR1H6 (EU117708)

98

Freshwater

Alphaproteobacteria

B-5-1

JF260874

Uncultured Novosphingobium sp. clone A08-08E (FJ542890)

99

Gut of Eisenia fetida

Alphaproteobacteria

B-14-1

JF260888

Uncultured alpha proteobacterium clone CB11G01 (EF471465)

98

Marine

99

Source

Freshwater

CFB Cytophaga–Flavobacterium–Bacteroides group

DGGE gels. Nineteen distinct partial 16S and 18S rRNA gene sequences were obtained, respectively (Tables 1, 2; Supplementary Fig. A, Electronic supplementary material). Thus, we were able to phylogenetically characterize 25.3% of prokaryotic and 36.5% eukaryotic communities, as detected by DGGE analysis. All prokaryotic 16S rRNA gene sequences obtained were affiliated with bacterial divisions commonly found in riverine ecosystems, e.g., Proteobacteria, Bacteriodetes (CFB group), Firmicutes, Actinobacteria, and Cyanobacteria (Sekiguchi et al., 2002; Winter et al., 2007). Five phylotypes identified as Betaproteobacteria were detected frequently, with one phylotype being a prominent member of the community in all sites (B-8-4) (Supplementary Fig. A, Electronic supplementary material). The high detection frequency of these Betaproteobacterial

phylotypes suggested that they were an important component of the prokaryotic community and widely distributed in the Jiulong River. This is in agreement with the findings of Zwart et al. (2002), who showed rivers have a specific planktonic bacterial community dominated by Proteobacteria. The phylotypes B-9-4 and B-6-1, belonging to Actinobacteria, were present in the majority of the sampling sites throughout the river (Supplementary Fig. A, Electronic supplementary material). This dominance of Actinobacteria in water samples is in concordance with the regular abundant appearance of some Actinobacterial groups in the plankton of rivers, although they have commonly been found in sediments and soils as well (Warnecke et al., 2004). A possible explanation for Actinobacteria abundance in the plankton is because they resisted protistan predation (Pernthaler et al.,

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Table 2 Phylogenetic affiliations of 18S rRNA gene sequences from DGGE bands Taxon

DGGE band

Accession number

Closest relative (accession number)

Similarity

Source

Dinophyceae

E-6-3

JF260897

Uncultured eukaryote clone ZX6.4 (FJ939121)

100

Freshwater

Dinophyceae

E-14-1

JF260904

Uncultured alveolate clone ZX5.2 (FJ939116)

99

Freshwater

Dinophyceae

E-14-2

JF260905

Heterocapsa triquetra (AF022198)

98

Marine

Dinophyceae

E-14-4

JF260907

Uncultured marine eukaryote clone NA2_4G2 (EF526806)

97

Marine

Dinophyceae

E-15-1

JF260909

Uncultured marine eukaryote clone NA2_4G2 (EF526806)

99

Marine

Ciliophora Ciliophora

E-6-1 E-7-1

JF260895 JF260901

Uncultured ciliate clone MLB76.163 (EU143873) Uncultured alveolate clone PAA2AU2004 (DQ244028)

99 97

Freshwater Freshwater

Chlorophyta

E-6-2

JF260896

Uncultured Chlorophyta clone ZX7.1 (FJ939122)

99

Freshwater

Haptophyceae

E-14-3

JF260906

Isochrysis galbana strain SAG 13.92 (HM149541)

99

Marine

Stramenopiles

E-2-1

JF260892

Uncultured freshwater eukaryote clone LG34-03 (AY919801)

91

Freshwater

Annelida

E-2-2

JF260893

Chaetogaster diastrophus (AF411874)

100

Freshwater

99

Freshwater

Rotifera

E-8-1

JF260902

Brachionus calyciflorus (GQ503607)

Arthropoda

E-8-2

JF260903

Invertebrate environmental sample clone G04 (GU070877)

100

Soil

Arthropoda

E-14-5

JF260908

Paracyclopina nana (FJ214952)

100

Brackishwater

Arthropoda

E-15-2

JF260910

Uncultured eukaryote clone SCM38C38(AY665127)

97

Marine

Unknown eukaryotea

E-5-1

JF260894

96

Freshwater

Unknown eukaryotea

E-6-4

JF260898

Uncultured eukaryotic picoplankton clone BA125 (EF196695) Uncultured eukaryote clone ZX6.4 (FJ939121)

94

Freshwater

Unknown eukaryotea

E-6-5

JF260899

Uncultured eukaryote clone WD0-14 (GQ844405)

99

Freshwater

Unknown eukaryotea

E-6-6

JF260900

Uncultured eukaryote clone 01RPZ110600079 (FN394949)

85

Soil

a

No sequence from known organism with similarity [90% in NCBI GenBank (only environmental sequences)

2001; Hahn et al., 2003). Nearly one-fourth of retrieved 18S rRNA gene sequences showed less than 97% identity to their closest relatives. In particular, bands E-2-1 and E-6-6 had a similarity of 91 and 84% to their closest relative, possibly representing novel eukaryotic lineages. Most phylotypes were affiliated with dinophyceae, ciliophora, chlorophyta, and metazoa, suggesting that they were main components of eukaryotic community in the Jiulong River. Dinoflagellate blooms Five dinoflagellate phylotypes were detected frequently in the Jiulong River. In recent years harmful algal blooms have increased globally, and most of the

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known toxic or harmful algal species are dinoflagellates (Anderson, 1989; Hallegraeff, 1993). A record dinoflagellate bloom, lasted for one and half months from late January to March in 2009 in the upper middle Jiulong River from our sites 4 to 6. The bloom event led to a strong reduction in water quality and threatened the water supply for both Xiamen and Zhangzhou cities. In Xiamen Bay, from 26 April to 10 June and again from 11 October to 20 November every year, the toxic dinoflagellate Alexandrium tamarense has a high chance to form blooms, which can threaten marine organisms and negatively impact human health (Lan et al., 2004). Notably, one of our dinoflagellate sequences obtained from estuarine sites (E-14-2) showed the highest similarity to that of


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Fig. 3 Cluster analysis and non-metric MDS ordination of prokaryotic (A, B) and eukaryotic (C, D) DGGE fingerprints. The numbers indicate the sampling sites Fig. 4 RDA ordination showing the microbial community composition in Jiulong River in relation to the statistically significant environmental factors (P \ 0.01), as revealed by prokaryotic (left) and eukaryotic (right) DGGE fingerprints. The numbers indicate the sampling sites. (Cond conductivity)

dinoflagellate Heterocapsa triquetra. H. triquetra is non-toxic, but it significantly contributes to the total phytoplankton biomass and may be harmful to the ecosystems (Litaker et al., 2002). Bloom of H. triquetra associated with A. tamarense frequently

occurred in the eastern coast of USA (Anderson & Stolzenbach, 1985) and Hong Kong waters (Lu & Hodgkiss, 2004). Therefore, it is urgent to improve the water management and to prevent the potential harmful algal blooms in the Jiulong River.

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Table 3 Variation partitioning and associated P values for microbial community matrices in Jiulong River, into different components Variation

Prokaryotic community Variance explained (%)

Eukaryotic community P

Variance explained (%)

P

[A ? S]

67.0

0.001

70.5

0.001

[A]

48.0

0.001

55.9

0.001

[S]

28.0

0.001

26.2

0.006

[A|S]

39.0

0.013

44.3

0.005

[S|A]

19.0

0.012

14.6

0.048

[A \ S] 1 - [A ? S]

9.0

11.6

33.0

29.5

The components are: [A ? S] = total variation explained by all agricultural pollution and saltwater intrusion variables together, [A] = variation explained by agricultural variables, [S] = variation explained by saltwater variables, [A|S] = pure agricultural variation, [S|A] = pure saltwater variation, [A \ S] = the agricultural variation that is shared by the saltwater variation, and 1 - [A ? S] = unexplained variation

Influence of agricultural pollution and saltwater intrusion Our data indicate that great variations in environmental conditions of the Jiulong River throughout space resulted in different microbial communities. The agricultural activities in the upper Jiulong River Watershed are the major sources of nitrogen and phosphorus due to heavy chemical fertilizer application and intensive livestock production (Guan et al., 2005). The genetic structure of both prokaryotic and eukaryotic microbial communities changed significantly from the upper Jiulong River sites to the estuarine sites. These changes were closely related to the agricultural pollution and saltwater intrusion because both NH4-N and conductivity was significantly correlated with both microbial communities (Fig. 4). Somewhat surprising was the finding that the genetic diversity patterns were similar between prokaryotic and eukaryotic communities. Agricultural pollution (nitrogen and phosphorus) and saltwater intrusion (conductivity and salinity) are the main factors influencing microbial community composition in the Jiulong River. Among the agricultural pollution factors, NH4-N was identified as being a significant one in explaining the spatial distribution of microbial communities in the river. NH4-N has

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previously been recognized as an important variable in structuring lake microbial communities (Yu et al., 2008). It is well known that nutrient concentrations may have a significant influence on microbial community composition because different microbial organisms are adapted to different nutritional conditions. For instance, the community composition of free-living microbial eukaryotes, such as algae and ciliates, varies in relation to the trophic status (Reynolds, 1984; Laybourn-Parry, 1992). Moreover, NH4-N is an important source of nitrifying bacteria, and large discharge of NH4-N in riverine ecosystem can stimulate the growth of nitrifying bacterial types, the fast-growing nitrifiers in turn may influence the composition of bacterial communities (Winter et al., 2007). By contrast, conductivity was comparatively higher in sites 13–15 than those in the other sites. In fact, the conductivity is closely related to salinity because the extent to which water conducts electricity depends on the concentration of dissolved salt. Thus, the microbial community composition of estuarine sites was also related to salinity. This finding was verified by the sequence analysis of excised DGGE bands because most sequences retrieved from sites 13 to 15 (e.g., B-14-1, B-14-2, E-14-2, E-14-3, E-14-4, E-14-5, E-15-1, E-15-2) were common to microbes from marine habitats. Salinity is a major regulatory factor of aquatic communities in the estuarine habitat, and the effects of salinity on microbial cells are related to osmoregulation and/or to metabolic changes triggered by salt, such as the ability to uptake different DOC compounds (Stepanauskas et al., 2000). Estuarine microbes are able to tolerate changes in salinity, resulting in different microbial community composition compared to the adjacent river and ocean habitats (Horne & Goldman, 1994). In the Columbia River estuary, Crump et al. (1999) showed that most particle-attached bacterial clones were rare in, or absent from, either the particle-attached or the freeliving bacterial communities of the river and the coastal ocean. He suggested that rapidly growing particle-attached bacteria develop into a uniquely adapted estuarine community. Similar findings were reported from geographically distinct estuarine systems such as the Rhone River estuary, France (Troussellier et al., 2002), the Parker River estuary, USA (Crump et al., 2004), the Tillamook Bay, USA (Bernhard et al., 2005), and the Moreton Bay, Australia (Hewson & Fuhrman, 2004). Additionally,


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increases in salinity resulted in a progressive reduction in the abundance and number of different groups of eukaryotic microorganisms, and an increase in the biomass of prokaryotes (Pedros-Alio et al., 2000). Thus, the greater of salinity in sites 13–15 contributed to a shift in the microbial community compositions between estuarine sites and other sites. However, it should be noted that almost one-third of the variation in microbial community composition can not be explained by partial RDA (Table 3). We are aware that this unexplained variation in microbial community composition could be related to other variables not measured here. For instance, virus, grazing and availability of organic carbon may regulate microbial community composition in ecosystems (Kent et al., 2006; Zhang et al., 2011), and the spatial distribution and the distance between ecosystems might influence the dispersal of microbial cells and alter the structure of aquatic microbial communities (Potapova & Charles, 2002).

DGGE, terminal restriction fragment length polymorphism (T-RFLP), and automated ribosomal intergenic spacer analysis (ARISA) are routinely applied to aquatic samples to estimate the number of microbial taxa and to investigate spatial and temporal dynamics of microbial assemblages (Okubo & Sugiyama, 2009). When compared to the other two methods, DGGE has a high ability to discriminate differences between microbial communities. Furthermore, selected bands from DGGE gel can be sequenced. Availability of the sequence information confers a remarkable advantage to this method over other methods in microbial community analyses. However, DGGE will only capture the dominate members of the assemblage. In addition, the choice of bands to excise may also be highly subjective, and there can be multiple sequences hiding in a single band. Thus, excising, cloning, and sequencing of a single clone from each band can miss hidden diversity (Diez et al., 2001).

Biomonitoring and assessment by DGGE

Conclusions

In an ideal situation, the quality of running waters should be assessed by the use of physical, chemical, and biological parameters to provide a complete spectrum of information for appropriate water management. The advantage of monitoring with the use of bioindicators is that biological communities reflect overall ecological quality and integrate the effects of different stressors providing a broad measure of their impact and an ecological measurement of fluctuating environmental conditions (Iliopoulou-Georgudaki et al., 2003). Microbial communities are considered one of the most promising indicators of water quality and aquatic ecosystem health due to their rapidly response to environmental than larger animals and plants (Stevenson & Smol, 2003; Jiang & Shen, 2007). In recent years, it has been recognized that community-based biomonitoring method could provide advanced warning of significant stress or degradation. By now genetic fingerprinting techniques allow the study of the diversity and dynamics of microbial communities in a rapid and rather accurate way (Muyzer, 1999). The use of fingerprinting analysis may provide a sensitive biomonitoring approach to evaluate environmental conditions in aquatic ecosystem. Currently, three fingerprinting methods including

The genetic diversity and community composition of prokaryotic and eukaryotic planktonic communities, with Betaproteobacteria and Dinophyceae being the most dominant taxa, showed remarkable spatial variation along the Jiulong River in relation to agricultural pollution (phosphorus and nitrogen) and saltwater intrusion (conductivity and salinity). Moreover, our results highlighted that PCR-DGGE fingerprinting could potentially represent an operable biomonitoring approach to assess riverine water quality and ecosystem health, thereby complementing the traditional physicochemical analyses. Acknowledgments We thank Xian Zhang for field sampling, Bo Wei, Huining Zhang and Xin Yu for determining nutrient concentrations (nitrogen, phosphorus, and carbon), and Qing Li for providing Fig. 1. We also thank David M. Wilkinson, Guangjie Chen, Wenjing Zhang, and two anonymous reviewers for their constructive comments and suggestions. This research was supported by the Knowledge Innovation Program of the Chinese Academy of Sciences (Nos. KZCX2-YW-QN401 and KZCX2-YW-Q02-04), the Xiamen Project of Science and Technology for Distinguished Young Scholars (No. 3502Z20116006), the Key Science and Technology Project of Fujian Province, China (No. 2009Y0044), the National Natural Science Foundation of China (No. 41006087), and the China International Science and Technology Cooperation Program (No. 2009DFB90120).

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