PCR-DGGE Fingerprinting Analysis of Plankton Communities and Its Relationship to Lake Trophic Status

© 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 1434-2944/09/510-0528

Internat. Rev. Hydrobiol. 94 2009 5 528–541

DOI: 10.1002/iroh.200911129

LI WU*, 1, 2, YUHE YU1, TANGLIN ZHANG1, WEISONG FENG1, XIANG ZHANG1, 2 and WEI LI1, 2

1Key Laboratory of Biodiversity and Conservation of Aquatic Organisms, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China;

e-mail: [email protected] of Life Sciences, Hefei Normal University, Hefei 230061, China

3Graduate School of the Chinese Academy of Sciences, Beijing 100039, China

Research Paper

PCR-DGGE Fingerprinting Analysis of Plankton Communities and Its Relationship to Lake Trophic Status

key words: freshwater plankton, community DNA analysis, 16S rRNA, 18S rRNA, water quality

Abstract

Plankton communities in eight lakes of different trophic status near Yangtze, China were character-ized by using denatured gradient gel electrophoresis (DGGE). Various water quality parameters were also measured at each collection site. Following extraction of DNA from plankton communities, 16S rRNA and 18S rRNA genes were amplified with specific primers for prokaryotes and eukaryotes, respectively; DNA profiles were developed by DGGE. The plankton community of each lake had its own distinct DNA profile. The total number of bands identified at 34 sampling stations ranged from 37 to 111. Both prokaryotes and eukaryotes displayed complex fingerprints composed of a large number of bands: 16 to 59 bands were obtained with the prokaryotic primer set; 21 to 52 bands for the eukaryotic primer set. The DGGE-patterns were analyzed in relation to water quality parameters by canonical cor-respondence analysis (CCA). Temperature, pH, alkalinity, and the concentration of COD, TP and TN were strongly correlated with the DGGE patterns. The parameters that demonstrated a strong correlation to the DGGE fingerprints of the plankton community differed among lakes, suggesting that differences in the DGGE fingerprints were due mainly to lake trophic status. Results of the present study suggest that PCR-DGGE fingerprinting is an effective and precise method of identifying changes to plankton community composition, and therefore could be a useful ecological tool for monitoring the response of aquatic ecosystems to environmental perturbations.

1. Introduction

Free-living planktonic organisms are a fundamental component of aquatic ecosystems, being comprised of a diverse assemblage of organisms commonly grouped as phytoplank-ton, zooplankton or bacteria. Because plankton are often quite sensitive to changes in the environment, they can be used to assess water quality and trophic status. Like most organ-isms, plankton has traditionally been identified by their morphological characteristics. How-ever, morphology-based taxonomy can be difficult since many of these organisms lack distinguishing features. Molecular approaches to investigate the genetic information in the

* Corresponding author

PCR-DGGE Fingerprinting of Plankton 529

© 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.revhydro.com

extracted DNA of environmental communities are called “the whole community DNA analy-sis” (RANJARD et al., 2000). Recent advances in molecular biology have provided new oppor-tunities for studies in microbial ecology (ZEHR and VOYTEK, 1999). MUYZER et al. (1993) first applied denaturing gradient gel electrophoresis (DGGE) techniques to the analysis of whole bacterial communities. Since then, it has been used extensively to profile prokaryotic community composition in a wide array of habitats, such as sediments (GREY and HERWIG, 1996), soil (MÜLLER et al., 2001), hot springs (FERRIS and WARD, 1997), wastewater (GIL-BRIDE et al., 2006; CURTIS and CRAINE, 1998), freshwater environments (e.g., LINDSTRÖM, 2000, 2001; VAN DER GUCHT et al., 2001) and marine environments (SÁNCHEZ et al., 2007). DGGE provides a quick method for comparing community composition in many different samples, and is less labor-intensive than the more traditional technique of sequencing of clone libraries. Application of DGGE permits the monitoring of microbial communities for which occurrence and relative frequency are potentially affected by a variety of environ-mental factors. Results observed in DGGE patterns have been confirmed by other molecular techniques. SMALLA et al. (2007) used DGGE, T-RFLP and SSCP fingerprints of polymerase chain reaction (PCR)-amplified 16S rRNA genes to assess soil bacterial diversity, concluding that the three methods provide similar results. Several authors have reported a correlation between the abundance of morphologically distinct organisms and the corresponding DGGE signal (NÜBEL et al., 1999; RIEMANN et al., 1999; CASAMAYOR et al., 2000, 2002). In addi-tion, SCHAUER et al. (2003) indicated that relative band intensities in DGGE fingerprints were informative for comparative purposes and could be used to track relative changes of particular populations.

Some recent studies have focused on the whole eukaryotic assemblage by using eukary-ote-specific primers (e.g., VAN HANNEN et al., 1998; MOON-VAN DER STAAY et al., 2000; DÍEZ et al., 2001; ESTRADA et al., 2004; SAVIN et al., 2004). However, despite the proven power of PCR-DGGE fingerprinting in microbial systems, it has not been applied with the same frequency to whole plankton communities (including bacteria and eukaryotic pelagic plankton) as it has to prokaryotes and microeukaryotes. Most previous studies have focused on the relationships between nutrients and bacterioplankton community composition (METHÉ and ZEHR, 1999; LINDSTRÖM, 2000; SCHAUER et al., 2003). YAN et al. (2006) indicated that molecular techniques based on a rRNA gene obtained from a natural environment have pro-vided new insights into the diversity of plankton communities (YAN et al., 2006). Recently, studies have used DGGE of PCR-amplified 16S rRNA and 18S rRNA genes to define plankton community composition and explore its relation to biotic and abiotic factors in one eutrophic lake. Those studies have indicated that significantly different plankton community composition among stations might be due to variability in water chemistry, including the nutrient levels in different lake areas (YAN et al., 2007; YU et al., 2008). Thus, if a signifi-cant link is found between nutrient availability and plankton community composition in one eutrophic lake, it is reasonable to assume that a similar relationship could exist in other lakes even if they differed in trophic status.

The goal of this study was to test this proposed relationship by comparing the DNA of the whole plankton communities in lakes that have substantially different nutrient levels. Plank-ton community composition data, obtained by DGGE of DNA coding for 16S rRNA (16S ribosomal RNA) and18S rRNA (18S ribosomal RNA), were analyzed through comparison of several water quality parameters in order to identify critical factors that affect plankton community structure.

530 L. WU et al.


2. Materials and Methods

2.1. Study Sites and Sample Collection

Eight shallow, freshwater lakes located in the middle reaches of the Yangtze River, China, were sampled in October 2006 to determine the composition of their plankton communities. All of the lakes are located in regions typified by a moderate subtropical monsoon climate with four distinct seasons. The lakes, which are relatively small (surface area 10–64 km2, maximum depth 1.3–3.4 m), have been affected by the rapid economic growth that has occurred in the surrounding urban areas in recent years. Based on geological and ecological characteristics of the eight aquatic ecosystems, samples were col-lected from five stations in Lake Tangxunhu (L. TXH) and Lake Xiaosihai (L. XSH), and four stations in Lake Niushanhu (L. NSH), Lake Luhu (L. LH), Lake Baoanhu (L. BAH), Lake Biandantang (L. BDT), Lake Nanbeizui (L. NBZ) and Lake Wuhu (L. WH). Plankton were collected using horizontal surface tows with a No. 25 plankton net (aperture size = 64 μm) for a minimum of 5 min. Since this is the net size traditionally used for collection of plankton samples, we were able to directly compare our results to other studies where genetic and non-genetic assessment methods were used.

2.2. Physicochemical Analysis

Water depth and temperature were determined in the field simultaneously with plankton collection. One liter of water was collected to measure pH, conductivity, hardness, alkalinity, chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP) and chlorophyll a (Chl a). All of these physicochemical parameters were measured according to standard methods (HUANG, 2000).

2.3. DNA Extraction and PCR-DGGE Analysis

Community genomic DNA was extracted within 12 h of sample collection. Before extracting the DNA of the whole plankton community, larger organisms were screened out through microscopic obser-vation to eliminate the possibility of interference from non-plankton. Although “ultra-clean” methods were not employed in this study, we believe sufficient care was taken during sample collection to eliminate significant biological contamination. DNA was extracted according to the method described by YAN et al. (2007). To analyse the DNA of the whole plankton community of the eight lakes, the 16S rRNA and 18S rRNA genes were amplified with bacterial primers (357fGC-518r) (MUYZER et al., 1993) and eukaryotic primers (1427fGC-1616r) (VAN HANNEN et al., 1998), respectively. Polymerase chain reaction (PCR) conditions for each 50 μL reaction mixture were 1 × PCR buffer, 2 mM Mg2+, 3.0 U of Taq DNA polymerase, 80 mM of each deoxynucleotide (Fermentas Inc., Hanover), 0.3 mM of each primer and approximately 40 ng of template DNA. Touchdown PCR was performed on a GeneAmp PCR System 9600 thermal cycler (Perkin Elmer Cetus, Waltham, MA, USA) with an initial denaturation at 94 °C for 5 min, continuing at 94 °C for 0.5 min, at the annealing temperature (listed in Table 1) for 0.5 min and an extension at 72 °C for 1 min. Finally, a primer extension at 72 °C for 10 min was performed. To control for the correct size of the PCR products, they were resolved on 1.5% agarose gels stained with ethidium bromide. A negative control was prepared in the same manner as the samples, except that DNA was excluded.

DGGE was performed with an INGENYphorU-2 system (INGENY International BV, Leiden, Neth-erlands) using 9% polyacrylamide (acrylamide:bisacrylamide, 37.5:1). PCR products containing approxi-mately equal amounts of DNA of similar sizes were separated on a gel containing a linear gradient of the denaturants (urea and formamide). The concentration of the denaturants increased from 30% at the top, to 70% at the bottom, of the gels; electrophoresis was performed at 60 °C, with 120 V for 16 h. The gels were then stained in 1 × TAE buffer containing 1 × SYBR Gold (Molecular Probes Europe BV, Leiden, Netherlands) and photographed using a UVP Imaging System (UVP Inc., Upland, CA, USA).



2.4. Statistical Analyses

Lakes were evaluated using Unweighted Pair Group with Arithmetic mean (UPGMA) cluster analysis based on the various water quality parameters collected. Digitized DGGE images were analyzed with the Diversity Database software (Bio-Rad). The software performs a density profile through each DGGE lane, detects the bands, and calculates the relative contribution of each band to the total band signal in the lane after applying a rolling disk for background subtraction. Bands with a relative intensity of less than 0.2% were discarded. Band position and intensity data for each sample were exported to a text file and then entered into an Excel spreadsheet for alignment prior to further statistical analyses. The DGGE band data (band occurrence and intensities) were imported from Excel spreadsheets into the canonical correspondence analysis (CCA) data files. To investigate relationships between DGGE fingerprints of plankton communities and measured environmental factors, CCA was performed using the software program CANOCO, version 4.5 (TER BRAAK, 1988). CCA has commonly been used to infer species–environment relationships and to find the pattern that is best explained by a particular environmental variable (TER BRAAK, 1986; LONGMUIR et al., 2007). The obtained ordination axes (based on community composition data) are linear combinations of environmental factors, assuming a unimodal species – envi-ronment relationship. Environmental factors significantly related to DGGE fingerprints of plankton communities were determined by forward selection and 999 unrestricted Monte Carlo permutations.

3. Results

3.1. Lake Characteristics

All of the lakes were shallow, with mean depths ranging from 1.4 m (L. XSH) to 3.3 m (L. NBZ) (Table 2). The lakes were slightly alkaline (pH of approx. 7.79 to 8.74) and the concentrations of total dissolved solids (TDS) were relatively low, as indicated by mean conductivities ranging from 0.146 mS/cm (L. NBZ) to 0.457 (L. BAH). According to the standard established by the Organisation for Economic and Co-operation and Development (OECD) (1982), the lakes can be assigned a trophic status based on the concentration of total phosphorus (TP) or chlorophyll a (Chl a). Based on TP, one lake is oligotrophic (L. NBZ),

Table 1. Oligonucleotide sequences and annealing temperatures used for touchdown PCR.

Primers Sequences (5′-3′) Annealing temperatures

References

357fGC GC clamp-CCTACGGGAGGCAGCAG 67 °C ~ 58 °C(10 cycles*)

MUYZER et al.

518r ATTACCGCGGCTGCTGG then 57 °C(20 cycles)

(1993)

1427fGC GC clamp-TCTGTGATGCCCTTAGATGTTCTGGG 69 °C ~ 60 °C(10 cycles*)

VAN HANNEN et al.

1616r GCGGTGTGTACAAAGGGCAGGG then 59 °C(18 cycles)

(1998)

Primer set 357fGC-518r used for 16S rDNA analysis; 1427fGC-1616r used for 18S rDNA analysis*With the temperature decreasing 1 °C each cycleGC clamp: 5′-CGCCCGCCGCGCCCCGCGCCCGGCCCGCCGCCCCCGCCCC-3′

532 L. WU et al.


Tabl

e 2.

W

ater

qua

lity

in th

e ei

ght l

akes

.

L. X

SHN

30°1

6′~3

0°17′

E114

°41′

~114

°42′

L. B

DT

N30

°17′

~30°

18′

E114

°42′

~114

°43′

L. B

AH

N30

°12′

~30°

18′

E114

°39′

~114

°46′

L. N

SHN

30°1

6′~3

0°22′

E114

°27′

~114

°38′

L. L

HN

30°1

2′~3

0°17′

E114

°09′

~114

°15′

L. N

BZ

N30

°09′

~30°

14′

E114

°25′

~114

°29′

L. W

HN

30°4

7′~3

0°50′

E114

°28′

~114

°33′

L. T

XH

N30

°23′

~30°

29′

E114

°19′

~114

°29′

Mea

nSD

Mea

nSD

Mea

nSD

Mea

nSD

Mea

nSD

Mea

nSD

Mea

nSD

Mea

nSD

Tem

pera

ture

(°C

)21

.20.

920

.20.

221

.60.

520

.60.

221

.81.

320

.70.

319

.60.

819

.00.

2

Wat

er d

epth

(m

)1.

40.

11.

90.

32.

60.

43.

10.

32.

10.

43.

30.

22.

00.

32.

10.

1

pH7.

970.

647.

790.

138.

320.

108.

300.

238.

170.

248.

740.

268.

150.

438.

320.

46

Con

duct

ivity

(ms/

cm)

0.24

70.

015

0.43

50.

001

0.45

70.

041

0.17

80.

010

0.26

20.

005

0.14

60.

003

0.31

00.

007

0.33

70.

021

CO

D (

mg/

L)2.

730.

133.

040.

193.

450.

212.

300.

123.

400.

121.

910.

053.

300.

183.

390.

34

Alk

alin

ity (

mg/

L)*

6612

803

772

5512

7412

394

985

765

Har

dnes

s (d

H)

6.33

0.40

11.6

20.

1013

.25

0.75

4.41

0.28

6.54

0.16

3.53

0.14

7.00

0.30

5.63

0.19

TP (

mg/

L)0.

011

0.00

50.

047

0.01

30.

045

0.00

70.

026

0.00

80.

044

0.00

50.

007

0.00

40.

075

0.04

60.

156

0.02

4

TN (

mg/

L)2.

552.

672.

211.

461.

320.

470.

860.

520.

600.

200.

560.

361.

080.

302.

390.

60

Chl

a (

ug/L

)3.

712.

486.

281.

0521

.02

8.66

4.51

3.32

14.1

12.

811.

641.

0017

.43

6.44

44.2

313

.74

* as

CaC

O3 m

g/L



TXH

NBZ

BAH

WH

LH

BDT

XSH

NSH

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Dissimilarity

Figure 1. UPGMA clustering analysis of the eight lakes on the basis of various water quality param-eters, showing the degree of dissimilarity.

Figure 2. DGGE patterns of the 16S rRNA (A) and 18S rRNA (B) genes amplified from the samples collected in Lake Xiaosihai. The arrow on the right indicates the band common to all five stations and

the left arrow on the indicates the band unique to single stations.

534 L. WU et al.


two are mesotrophic (L. NSH and L. XSH), four are eutrophic (L. LH, L. BAH, L. BDT, and L. WH) and one is hypereutrophic (L. TXH). Using Chl a, one is oligotrophic (L. NBZ), three are mesotrophic (L. NSH, Lake XSH, and Lake BDT), three are eutrophic (Lake LH, Lake BAH, and Lake WH) and one is hypereutrophic (Lake TXH). Using UPGMA cluster analysis, where the various water quality parameters shown in Table 2 were used as input variables, two initial groups of three lakes each were formed: Lakes NSH, XSH, and BDT in one group, and Lakes LH, WH, and BAH in another. Lake NBZ and Lake TXH did not initially cluster with any other lakes and TXH was the most dissimilar (Fig. 1). The cluster pattern of the lakes clustered is reflective of trophic status, determined using either TP or Chl a concentration.

Table 3. PCR-DGGE bands of 16S rDNA and 18S rDNA in eight lakes.

Primer set Mean SD Total bands

Unique bands

Common bands

L. TXH 357fGC-518r 32.8 3.5 40 4 24

1427fGC-1616r 29 2.6 40 8 21

Total bands 61.8 3.9 80 12 45

L. XSH 357fGC-518r 34.6 6.1 59 19 17

1427fGC-1616r 35 3.7 52 13 22

Total bands 69.6 6.6 111 32 39

L. LH 357fGC-518r 34.5 1.3 47 10 24

1427fGC-1616r 28.5 3.5 40 7 17

Total bands 63 3.2 87 17 41

L. NSH 357fGC-518r 32.5 4.7 50 9 19

1427fGC-1616r 26.3 6.7 39 8 17

Total bands 58.8 3.6 89 17 36

L. BDT 357fGC-518r 21.3 7.9 35 13 11

1427fGC-1616r 25 2.9 31 4 21

Total bands 46.3 8.0 66 17 32

L. BAH 357fGC-518r 21 4.1 26 2 16

1427fGC-1616r 24 3.4 33 8 16

Total bands 45 6.2 59 10 32

L. WH 357fGC-518r 24.3 3.9 31 4 14

1427fGC-1616r 19.8 2.8 34 10 5

Total bands 44 5.8 65 14 19

L. NBZ 357fGC-518r 13.8 2.5 16 1 9

1427fGC-1616r 14.3 5.1 21 6 6

Total bands 28 7.5 37 7 15



3.2. DGGE Profiles and Plankton Community Structure

A typical DGGE profile is shown in Figure 2. The total number of plankton bands identi-fied in the eight lakes ranged from 37 to 111 (Table 3). The DGGE bands that were unique to a single station ranged from seven to 32 (mean of 17.8), and the bands common to all stations ranged from 15 to 45 (mean of 32.6). Both prokaryotes and eukaryotes displayed complex fingerprints composed of a large number of bands. The prokaryotic primer set revealed 16 to 59 bands while 21 to 52 bands were produced using the eukaryotic primer set. The largest number of identified bands, 111 and 89, were found in L. XSH and L. NSH, respectively (Table 3). The lowest number of bands was in L. NBZ (37).

3.3. Relationship between DGGE Fingerprints and Water Quality Parameters

Forward selection and Monte Carlo testing yielded a subset of water quality param-eters, including temperature, pH, alkalinity, COD, TP and TN, that significantly (P < 0.05) accounted for the variability in the band data. Since many water quality parameters are linked, change in one parameter often is matched or followed by concomitant changes in other parameters. For example, the addition of nutrients, either directly or indirectly (e.g., fertilizer runoff from agricultural areas), will increase the concentrations of phosphorus, nitrogen and other constituents (depending upon the composition of the fertilizer) which, in turn, are likely to stimulate primary production (algae and aquatic macrophytes) and, sub-

Figure 3. Results from CCA ordination of plankton community composition, as revealed by PCR-DGGE fingerprinting.

536 L. WU et al.


sequently, secondary production. Chemical fertilizers themselves can cause an increase in COD, and the resulting increase in primary productivity may affect pH, alkalinity, biological oxygen demand and other parameters.

CCA ordination of the thirty-four sampling stations, based on six water quality param-eters, is shown in Figure 3. The first three CCA axes explained 75.70% of the cumulative variance of the bands-water quality relationship. The bands-water quality correlation was high, indicating a strong relationship between the water quality parameters and the DGGE bands (Table 4). Axis 1 was correlated primarily with pH (r = –0.35) and axis 2 was corre-lated primarily with COD and alkalinity (r = –0.75 and r = –0.87, respectively). For axis 3, the best correlations were found with temperature, TP and TN (r = –0.69, r = 0.63, and r = 0.52, respectively). All six of the water quality parameters measured in this study, there-fore, were correlated to some degree with plankton community composition as measured using the DGGE analysis. In the biplots, COD and alkalinity generally showed the strongest positive correlation with DGGE fingerprints of the plankton community at L. WH, but were negatively correlated at L. NSH and L. NBZ (Fig. 3). TP and TN concentrations played an important role in DGGE banding in samples from L. TXH, and temperature appeared to be correlated with DGGE banding in L. BAH and L. LH. The data also suggest that factors that affect plankton community composition may vary even in a single water body. For example, banding measured at two stations in L. XSH (I and II) and station III in L. BDT appeared to be strongly affected by TP and TN concentrations. However, data from other stations in these two lakes were more highly correlated with COD and alkalinity (Fig. 3).

4. Discussion

The critical role that plankton play in a palustrine ecosystem, as well as the sensitivity of many planktonic species to chemical, physical and biological stressors, make them a good choice to monitor trophic conditions and environmental perturbations (BIANCHI et al., 2003; BEAUGRAND, 2005; TERNJEJ and TOMEC, 2005). PCR-DGGE allows the simultaneous analysis of multiple samples, and it is one of the most commonly used fingerprinting meth-ods in community characterization studies. We used the presence and relative intensity of DGGE bands to analyze the DGGE fingerprints of plankton communities and explore the

Table 4. Correlation matrix showing the relationship between band axes and significant(P < 0.05) environmental variables.

Total inertia 1.04Sum of all canonical eigenvalues 0.39

Axis 1 Axis 2 Axis 3

Eigenvalues 0.17 0.06 0.06Bands-environment correlations 0.86 0.86 0.91Cumulative percentage varianceof bands data 16.70 22.80 28.10of bands-environment relation 44.80 61.30 75.70Temperature (°C) 0.41 0.29 –0.69pH –0.35 0.27 0.01COD (mg/L) –0.27 –0.75 –0.09Alkalinity (mg/L) –0.22 –0.87 –0.06TP (mg/L) –0.55 –0.36 0.63 TN (mg/L) 0.33 –0.11 0.52



relationship of these DGGE data to various water quality parameters in eight lakes of dif-ferent trophic status. Based on nutrient and chlorophyll a concentrations, the trophic status of the lakes ranged from oligotrophic to hypertrophic. Because these lakes are similar in morphology, but yet differ in their degree of nutrient enrichment, they are ideal systems in which to investigate the spatial dynamics of the plankton community. The taxonomic composition of plankton communities is known to differ under different trophic conditions (DRUVIETIS et al., 1998; WANG et al., 2004). In the present study, the DGGE patterns were highly variable in terms of band number, position and intensity (e.g., Fig. 2 and Table 3). The observed differences may be associated with variable nutrient levels in the lakes. Generally, species diversity tends to be highest in mesotrophic lakes, and decreases under either nutri-ent poor (oligotrophic) or nutrient rich (eutrophic) conditions. In these latter water bodies, only tolerant species survive (DODSON, 1991, 1992), and the more extreme the conditions, the more tolerant the remaining species. DORIGO et al. (2002) reported that a loss of diversity in the community reflects ongoing, significant contamination, whereas a change in species composition, but not diversity, is indicative of discontinuous, low-level contamination. From the UPGMA clustering of the water quality parameters (e.g., Fig. 1), lakes within a given trophic level were generally clustered into the same group. Among the eight lakes, L. XSH and L. NSH were considered mesotrophic; the band numbers of these two lakes were the highest. L. LH, L. BAH, L. BDT and L. WH were eutrophic lakes and, together with hyper-eutrophic L. TXH, reflected high nutrient conditions and resulted in fewer DGGE bands. L. NBZ was classified as oligotrophic lake and also had fewer bands.

The addition of nutrients altered the composition of the brackish-water bacterial com-munity, as indicated by the DGGE banding patterns. This effect has been reported by other researchers. The bacterioplankton community in marine mesocosms, for example, was affect-ed by nutrient enrichment (e.g., LEBARON et al., 2001; SCHÄFER et al., 2001; CARLSON et al., 2002; ØVREÅS et al., 2003). In the present study, the differences in DNA polymorphism of plankton communities in the eight lakes appeared to be primarily related to lake trophic status. Based on the CCA results, six factors (temperature, pH, alkalinity, COD, TP, and TN) were significantly related to the band composition. However, the factors that were strongly correlated with the DGGE fingerprints of the plankton community differed among lakes (e.g., Fig. 3). TP and TN were strongly correlated with hypertrophic L. TXH, and negatively correlated with L. NSH and L. NBZ. Both nitrogen and phosphorus are known to be closely associated with lake eutrophication as they stimulate primary algal production. Phosphorus has often been found to be the primary limiting nutrient in most lakes (SCHINDLER, 1977; HECKY and KILHAM, 1988; SHARPLEY et al., 1994; SMITH et al., 1999). Additionally, species richness and plankton diversity have been found to be closely related to TP (JEPPESEN et al., 2000). SIPURA et al. (2005) indicated that nitrogen and phosphorus additions increased the biomass of heterotrophic bacteria and picocyanobacteria and caused significant changes in their community composition, based on DGGE banding patterns. YU et al. (2008) reported that concentrations of TP and TN contributed to the band composition of the plankton com-munity in a eutrophic lake; TP concentration was the major factor affecting the plankton community at two hypereutrophic study stations during all seasons except spring.

L. TXH is located in the centre of the city of Wuhan and is stressed from multiple anthropogenic sources, including untreated domestic and industrial wastewater, which is discharged into the lake, and also from nonpoint source runoff which may contain nutrients plus undefined chemical constituents. Water quality data indicate nitrogen and phosphorus pollution in L. TXH (see Table 2); the CCA results indicated that the plankton community in L. TXH was affected primarily by the high nutrient loading. In contrast, L. NSH and L. NBZ are located on the outskirts of Wuhan and experience the lowest pollution levels of any of the studied lakes, resulting in a negative correlation between TP and TN concentrations and the plankton community.

538 L. WU et al.


For the CCA ordination (e.g., Fig. 3), most of the temperature-related variability was asso-ciated with samples from L. BAH and L. LH, since water temperature was higher in these two lakes (Table 2). Temperature is a major environmental factor influencing the growth and reproduction of plankton. Bacterioplankton composition, as defined by DGGE, has been reported to be significantly related to temperature (LINDSTRÖM, 2001). FERRIS and WARD (1997) indicated that similar DGGE patterns were found in samples collected at the same site, and for sites with the same temperature, regardless of the season. However, different DGGE profiles were found in samples from sites with different temperatures. Variability in relation to COD and alkalinity is associated primarily with samples collected from L. WH. COD is an indicator of water quality, higher COD is generally related to lower water qual-ity, often due to organic and/or chemical pollution. GILBRIDE et al. (2006) reported that BOD5 was one of the most likely parameters to influence the bacterial community structure in an activated sludge treatment plant from a bleached kraft pulp mill. Similarly, data from the current study show a relationship between BOD5 and plankton community composition. L. WH is the location of an aquaculture industry where fertilizer is added to support the growth of silver carp and bighead carp fingerlings. The direct (as in L. WH aquaculture industry) or indirect (e.g., agricultural runoff) addition of nutrients to aquatic ecosystems can have major impacts on water quality, and can impair the normal functioning of aquatic ecosystems (XIA et al., 2007). Alkalinity has also been shown to play an important role in plankton species composition and distribution in saline waters (ZHAO and HE, 1993, 1995; ZHAO et al., 1996).

In all lakes, a large number of bands were detected from all sampling stations, whereas only a few detected bands were restricted to single samples. Conversely, YAN et al. (2007) reported that the number of unique bands far exceeded the number of common bands in a study at five stations in a single lake. Tropic status at the five stations in the latter study were highly variable, being classified as hypertrophic, eutrophic or mesotrophic. The significant differences of plankton communities that were apparent among the five stations were likely due to the differences in nutrient concentration among the five stations. In contrast, in the current investigation there were few differences in nutrient levels among stations in one lake, which led to similar plankton compositions, and thus more common bands than unique bands in the DGGE analysis.

Following comparison of different DGGE primer sets for the study of marine bacterio-plankton communities, SÁNCHEZ et al. (2007) concluded that primer set 357fGC-907rM was the most suitable for the routine use in PCR-DGGE analyses in a marine environment, and also indicated that the most suitable set of primers should be determined for every habitat. Primer set 357fGC-518r has been used in several, including many recent studies to evalu-ate bacterioplankton community composition through DGGE profiles (e.g., MUYZER et al., 1993; DE WEVER et al., 2005; FLÓREZ and MAYO, 2006). Additionally, analysis of the DGGE fingerprints showed that primer set 357fGC-518r reflected the changes of the bacterioplank-ton community in the freshwater environment. Given this information, primer set 357fGC-518r appeared to be a good choice for analysis of the bacterial community in a freshwater environment (YAN et al., 2007; YAN et al., 2008; YU et al., 2008), and was selected for the current investigation.

In conclusion, we found that several environmental factors can affect the DNA polymor-phism of plankton communities. These results suggest that PCR-DGGE fingerprinting can be a useful ecological tool for characterizing an aquatic community and monitoring the response of the community to both direct and indirect environmental perturbations. Although PCR-DGGE fingerprinting, as demonstrated in the analyses described here, does not com-pletely replace other, more traditional biological evaluation techniques, it can be effectively applied in critical studies where precise community assessment is important. In order to obtain a more complete picture of the status of plankton communities, future studies should attempt to use group-specific 16S rRNA and 18S rRNA primers, targeted to specific portions



of the plankton community, and sequence the main bands that represent the predominant components of the plankton assemblage.

5. Acknowledgements

This study was supported by grants from the Major State Basic Research Development Program of China (2007CB109205), the National Natural Science Foundation of China (30770298), and the Founda-tion of the State Key Laboratory of Freshwater Ecology and Biotechnology (2008FB016).

6. References

BEAUGRAND, G., 2005: Monitoring pelagic ecosystems using plankton indicators. – ICES J. Mar. Sci. 62: 333–338.

BIANCHI, F., F. ACRI, F. B. AUBRY, A. BERTON, A. BOLDRIN, E. CAMATTI, D. CASSIN and A. COMASCHI, 2003: Can plankton communities be considered as bio-indicators of water quality in the Lagoon of Venice? – Mar. Pollut. Bull. 46: 964–971.

CARLSON, C. A., S. J. GIOVANNONI, D. A. HANSELL, S. J. GOLDBERG, R. PARSONS, M. P. OTERO, K. VERGIN and B. R. WHEELER, 2002: Effect of nutrient amendments on bacterioplankton produc-tion, community structure, and DOC utilization in the northwestern Sargasso Sea. – Aquat. Microb. Ecol. 30: 19–36.

CASAMAYOR, E. O., H. SCHÄFER, L. BAÑERAS, C. PEDRÓS-ALIÓ and G. MUYZER, 2000: Identification of and spatio-temporal differences between microbial assemblages from two neighboring sulfur-ous lakes: comparison by microscopy and denaturing gradient gel electrophoresis. – Appl. Environ. Microbiol. 66: 499–508.

CASAMAYOR, E. O., C. PEDRÓS-ALIÓ, G. MUYZER and R. AMANN, 2002: Microheterogeneity in 16S ribosomal DNA-defined bacterial populations from a stratified planktonic environment is related to temporal changes and to ecological adaptations. – Appl. Environ. Microbiol. 68: 1706–1714.

CURTIS, T. P. and N. G. CRAINE, 1998: The comparison of the diversity of activated sludge plants. – Water Sci. Technol. 37: 71–78.

DE WEVER, A., K. MUYLAERT, K. VAN DER GUCHT, S. PIRLOT, C. COCQUYT, J. P. DESCY, P. D. PLIS-NIER and W. VYVERMAN, 2005: Bacterial community composition in Lake Tanganyika: vertical and horizontal heterogeneity. – Appl. Environ. Microbiol. 71: 5029–5037.

DÍEZ, B., C. PEDRÓS-ALIÓ, T. L. MARSH and R. MASSANA, 2001: Application of denaturing gradient gel electrophoresis (DGGE) to study the diversity of marine picoeukaryotic assemblages and comparison of DGGE with other molecular techniques. – Appl. Environ. Microbiol. 67: 2942–2951.

DODSON, S. I., 1991: Species richness of crustacean zooplankton in European lakes of different sizes. – Verh. Int. Ver. Limnol. 14: 1223–1229.

DODSON, S. I., 1992: Predicting crustacean zooplankton species richness. – Limnol. Oceanogr. 37: 848–856.

DORIGO, U., A. BÉRARD and J. F. HUMBERT, 2002: Comparison of eukaryotic phytobenthic community composition in a polluted river by partial 18S rRNA gene cloning and sequencing. – Microb. Ecol. 44: 372–380.

DRUVIETIS, I., G. SPRINGE, L. URTANE and M. KLAVINS, 1998: Evaluation of plankton communities in small highly humic bog lakes in Latvia. – Environ. Int. 24: 595–602.

ESTRADA, M., P. HENRIKSEN, J. M. GASOL, E. O. CASAMAYOR and C. PEDROS-ALIÓ, 2004: Diversity of planktonic photoautotrophic microorganisms along a salinity gradient as depicted by microscopy, flow cytometry, pigment analysis and DNA-based methods. – FEMS Microbiol. Ecol. 49: 281–293.

FERRIS, M. J. and D. M. WARD, 1997: Seasonal distributions of dominant 16S rRNA-defined populations in a hot spring microbial mat examined by denaturing gradient gel electrophoresis. – Appl. Environ. Microbiol. 63: 1375–1381.

FLÓREZ, A. B. and B. MAYO, 2006: Microbial diversity and succession during the manufacture and ripening of traditional, Spanish, blue-veined Cabrales cheese, as determined by PCR-DGGE. – Int. J. Food Microbiol. 110: 165–171.

540 L. WU et al.


GILBRIDE, K. A., D. FRIGON, A. CESNIK, J. GAWAT and R. R. FULTHORPE, 2006: Effect of chemical and physical parameters on a pulp mill biotreatment bacterial community. – Water Res. 40: 775–787.

GREY, J. P. and R. P. HERWIG, 1996: Phylogenetic analysis of the bacterial communities in marine sedi-ments. – Appl. Environ. Microbiol. 62: 4049–4059.

HECKY, R. E. and P. KILHAM, 1988: Nutrient limitation of phytoplankton in freshwater and marine environments: a review of recent evidence on the effects of enrichment. – Limnol. Oceanogr. 33: 796–822.

HUANG, X. F., 2000: Survey, observation and analysis of lake ecology. China Standard Press, Beijing. (In Chinese).

JEPPESEN, E., J. P. JENSEN, M. SǾNDERGAARD, T. LAURIDSEN and F. LANDKILDEHUS, 2000: Trophic structure, species richness and biodiversity in Danish lakes: changes along a phosphorus gradient. – Freshw. Biol. 45: 201–218.

LEBARON, P., P. SERVAIS, M. TROUSSELLIER, C. COURTIES, G. MUYZER, L. BERNARD, H. SCHÄFER, R. PUKALL, E. STACKEBRANDT, T. GUINDULAIN and J. VIVES-REGO, 2001: Microbial community dynamics in Mediterranean nutrient-enriched seawater mesocosms: changes in abundances, activity and composition. – FEMS Microbiol. Ecol. 34: 255–266.

LINDSTRÖM, E. S., 2000: Bacterioplankton community composition in five lakes differing in trophic status and humic content. – Microb. Ecol. 40: 104–113.

LINDSTRÖM, E. S., 2001: Investigating influential factors on bacterioplankton community composition: results from a field study of five mesotrophic lakes. – Microb. Ecol. 42: 598–605.

LONGMUIR, A., J. B. SHURIN and J. L. CLASEN, 2007: Independent gradients of producer, consumer and microbial diversity in lake plankton. – Ecology 88: 1663–1674.

METHÉ, B. A. and J. P. ZEHR, 1999: Diversity of bacterial communities in Adirondack lakes: Do species assemblages reflect water chemistry? – Hydrobiologia 401: 77–96.

MOON-VAN DER STAAY, S. Y., G. W. M. VAN DER STAAY, L. GUILLOU, D. VAULOT, H. CLAUSTRE and L. K. MEDLIN, 2000: Abundance and diversity of prymnesiophytes in the picoplankton community from the equatorial Pacific Ocean inferred from 18S rDNA sequences. – Limnol. Oceanogr. 45: 98–109.

MÜLLER, A. K., K. WESTERGAARD, S. CHRISTENSEN and S. J. SØRENSEN, 2001: The effect of long-term mercury pollution on the soil microbial community. – FEMS Microbiol. Ecol. 36: 11–19.

MUYZER, G., E. C. DE WAAL and A. G. UITTERLINDEN, 1993: Profiling of complex microbial popula-tions by denaturing gradient gel electrophoresis analysis of polymerase chain reaction amplified genes coding for 16S rRNA. – Appl. Environ. Microbiol. 59: 695–700.

NÜBEL, U., F. GARCIA-PICHEL, M. KÜHL and G. MUYZER, 1999: Quantifying microbial diversity: mor-photypes, 16S rRNA genes, and carotenoids of oxygenic phototrophs in microbial mats. – Appl. Environ. Microbiol. 65: 422–430.

Organisation for Economic and Co-operation and Development (OECD), 1982: Eutrophication of Waters: Monitoring, Assessment and Control, Paris, France.

ØVREÅS, L., D. BOURNE, R. A. SANDAA, E. O. CASAMAYOR, S. BENLLOCH, V. GODDARD, G. SMERDON, M. HELDAL and T. F. THINGSTAD, 2003: Response of bacterial and viral communities to nutrient manipulations in seawater mesocosms. – Aquat. Microb. Ecol. 31: 109–121.

RANJARD, L., F. POLY and S. NAZARET, 2000: Monitoring complex bacterial communities using culture-independent molecular techniques: application to soil environment. – Res. Microbiol. 151: 167–177.

RIEMANN, L., G. F. STEWARD, L. B. FANDINO, L. CAMPBELL, M. R. LANDRY and F. AZAM, 1999: Bacterial community composition during two consecutive NE Monsoon periods in the Arabian Sea studied by denaturing gradient gel electrophoresis (DGGE) of rRNA genes. – Deep-Sea Res. 46: 1791–1811.

SÁNCHEZ, O., J. M. GASOL, R. MASSANA, J. MAS and C. PEDRÓS-ALIÓ, 2007: Comparison of different denaturing gradient gel electrophoresis primer sets for the study of marine bacterioplankton communi-ties. – Appl. Environ. Microbiol. 73: 5962–5967.

SAVIN, M. C., J. L. MARTIN, M. LEGRESLEY, M. GIEWAT and J. ROONEY-VARGA, 2004: Plankton diversity in the bay of fundy as measured by morphological and molecular methods. – Microb. Ecol. 48: 51–65.

SCHÄFER, H., L. BERNARD, C. COURTIES, P. LEBARON, P. SERVAIS, R. PUKALL, E. STACKEBRANDT, M. TROUSSELLIER, T. GUINDULAIN, J. VIVES-REGO and G. MUYZER, 2001: Microbial community dy -namics in Mediterranean nutrient-enriched seawater mesocosms: changes in the genetic diversity of bacterial populations. – FEMS Microbiol. Ecol. 43: 243–253.

SCHAUER, M., V. BALAGUÉ, C. PEDRÓS-ALIÓ and R. MASSANA, 2003: Seasonal changes in the taxo-nomic composition of bacterioplankton in a coastal oligotrophic system. – Aquat. Microb. Ecol. 31: 163–174.



SCHINDLER, D. W., 1977: Evolution of phosphorus limitation in lakes. – Science 195: 260–262.SHARPLEY, A. N., S. C. CHAPRA, R. WEDEPOHL, J. T. SIMS, T. C. DANIEL and K. R. REDDY, 1994:

Managing agricultural phosphorus for protection of surface waters: Issues and options. – J. Environ. Qual. 23: 437–451.

SIPURA, J., K. HAUKKA, H. HELMINEN, A. LAGUS, J. SUOMELA and K. SIVONEN, 2005: Effect of nutrient enrichment on bacterioplankton biomass and community composition in mesocosms in the Archi-pelago Sea, northern Baltic. – J. Plankton Res. 27: 1261–1272.

SMALLA, K., M. OROS-SICHLER, A. MILLING, H. HEUER, S. BAUMGARTE, R. BECKER, G. NEUBER, S. KROPF, A. ULRICH and C. C. TEBBE, 2007: Bacterial diversity of soils assessed by DGGE, T-RFLP and SSCP fingerprints of PCR-amplified 16S rRNA gene fragments: Do the different methods provide similar results. – J. Microbiol. Methods 69: 470–479.

SMITH, V. H., G. D. TILMAN and J. C. NEKOLA, 1999: Eutrophication: impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems. – Environ. Pollut. 100: 179–196.

TER BRAAK, C. J. F., 1986: Canonical correspondence analysis: a new eigenvector technique for multi-variate direct gradient analysis. – Ecology 67: 1167–1179.

TER BRAAK, C. J. F., 1988: CANOCO-an extension of DECORANA to analyze species-enviroment relationships. – Vegatatio 75: 159–160.

TERNJEJ, I. and M. TOMEC, 2005: Plankton community and related environmental factors in the oligo-trophic Lake Vrana. – Period Biol. 107: 321–328.

VAN DER GUCHT, K., K. SABBE, L. DE MEESTER, N. VLOEMANS, G. ZWART, M. GILLIS and W. VYVER-MAN, 2001: Contrasting bacterioplankton community composition and seasonal dynamics in two neighbouring hypertrophic freshwater lakes. – Environ. Microbiol. 3: 680–690.

VAN HANNEN, E. J., M. P. VAN AGTERVELD, H. J. GONS, H. J. LAANBROEK, 1998: Revealing genetic diversity of eukaryotic microorganisms in aquatic environments by denaturing gradient gel electro-phoresis. – J. Phycol. 34: 206–213.

WANG, J., P. XIE, N. TAKAMURA, L. Q. XIE, Z. J. SHAO and H. J. TANG, 2004: The picophytoplankton in three Chinese lakes of different trophic status and its relationship to fish populations. – J. Freshw. Ecol. 19: 285–293.

XIA, W. K., X. P. WU, J. S. LIU, Z. J. LI, T. L. ZHANG, S. W. YE and W. X. LI, 2007: Fisheries resources and propositions for fisheries management in Lake Wuhu. – Reservoir Fisheries 27: 38–40. (In Chi-nese with English abstract)

YAN, Q. Y., Y. H. YU and W. S. FENG, 2006: Genetic fingerprinting of plankton community provides new insights into aquatic ecology. – Prog. Nat. Sci. 16: 893–898.

YAN, Q. Y., Y. H. YU, W. S. FENG, W. N. DENG and X. H. SONG, 2007: Genetic diversity of plankton community as depicted by PCR-DGGE fingerprinting and its relation to morphological composition and environmental factors in Lake Donghu. – Microb. Ecol. 54: 290–297.

YAN, Q. Y., Y. H. YU, W. S. FENG, G. PAN, H. CHEN, J. CHEN, B. YANG, X. M. LI and X. ZHANG, 2008: Plankton community succession in artificial systems subjected to cyanobacterial blooms removal using chitosan-modified soils. – Microb. Ecol. DOI 10.1007/s00248-008-9434-3.

YU, Y. H., Q. Y. YAN and W. S. FENG, 2008: Spatio-temporal heterogeneity of plankton communities in Lake Donghu as revealed by PCR-DGGE and its relation to biotic and abiotic factors. – FEMS Microbiol. Ecol. 63: 328–337.

ZEHR, J. P. and M. A. VOYTEK, 1999: Molecular ecology of aquatic communities: reflections and future directions. – Hydrobiologia 401: 1–8.

ZHAO, W. and Z. H. HE, 1993: Rotifera from inland saline waters in north China. – J. Dalian Fisheries Univ. 8: 20–31. (In Chinese with English abstract)

ZHAO, W. and Z. H. HE, 1995: Protozoa from inland saline waters in north China. – Acta Hydrobiologica Sinica. 19: 193–202. (In Chinese with English abstract)

ZHAO, W., H. JIANG and Z. H. HE, 1996: Planktonic crustaceans of inland saline waters in Sanbei district, northern China. – J. Dalian Fisheries Univ. 11: 1–13. (In Chinese with English abstract)

Manuscript received January 25th, 2009; revised April 7th, 2009; accepted April 7th, 2009

PCR-DGGE Fingerprinting Analysis of Plankton Communities and Its Relationship to Lake Trophic Status

Documents

Transcript of PCR-DGGE Fingerprinting Analysis of Plankton Communities and Its Relationship to Lake Trophic Status