Abstract
Bacterial community structure along the Changjiang River (which is more than 2,500 km long) was studied by using denaturing gradient gel electrophoresis (DGGE) and clone library analysis of PCR-amplified 16S ribosomal DNA (rDNA) with universal bacterial primer sets. DGGE profiles and principal-component analysis (PCA) demonstrated that the bacterial community gradually changed from upstream to downstream in both 1998 and 1999. Bacterial diversity, as determined by the Shannon index (H′), gradually decreased from upstream to downstream. The PCA plots revealed that the differences in the bacterial communities among riverine stations were not appreciable compared with the differences in two adjacent lakes, Lake Dongting and Lake Poyang. The relative stability of the bacterial communities at the riverine stations was probably due to the buffering action of the large amount of water flowing down the river. Clone library analysis of 16S rDNA revealed that the dominant bacterial groups changed from β-proteobacteria and the Cytophaga-Flexibacter-Bacteroides group upstream to high-G+C-content gram-positive bacteria downstream and also that the bacterial community structure differed among the stations in the river and the lakes. The results obtained in this study should provide a reference for future changes caused by construction of the Three Gorges Dam.
The Changjiang River in the People's Republic of China is the third-longest river in the world. The Three Gorges Dam, which is 190 m high and 2 km wide and is being constructed in the middle reaches of the river, will create a lake that is 600 km long. Construction of the Three Gorges Dam and development of the river basin were predicted to lead to changes in river water quality. Until now, there have been no data on aquatic environmental changes associated with the construction of such a large dam except for data from one study of the Danube River (24).
In aquatic systems, it is important to evaluate changes in the microbial community structure, because the microbial community is the foundation of biogeochemical cycles (7, 45). In a previous study, we analyzed the bacterial community structure in the East China Sea adjacent to the estuary of the Changjiang River by using both culture-dependent and culture-independent methods (37). However, the temporal succession and geographical succession of the bacterial community structure along the Changjiang River itself have not been studied previously. While there have been some studies of the relationship between bacterial communities and biogeochemical cycles in large rivers, such as the Nile River, the Amazon River, and the Mississippi River (2, 6, 9), succession patterns in bacterial community structure along a large river have never been analyzed. Our objective in this study was to describe the bacterial community structure along the Changjiang River prior to construction of the Three Gorges Dam.
Although there are numerous lakes in the Changjiang River basin, Lake Dongting and Lake Poyang are two large lakes that are not separated from the Changjiang River. As a result, lake water and river water mix. In the rainy period, the area of the lakes expands considerably due to the amount of river water that flows into the lakes (27, 28). In addition, the flow rate decreases and water remains in the lakes over a longer period of time. Overall, the lakes appear to be a part of the Changjiang River and to play an important role as natural regulators of the Changjiang River. Therefore, we analyzed the bacterial community structure in the lakes to predict phenomena which may occur in the reservoir of the Three Gorges Dam in the future.
Previously, detection and analysis of bacteria in the environment were performed mainly by using culture-based methods. However, because it is difficult to culture most bacteria in environmental samples (26, 29, 43), evaluation of the changes in bacterial community structure by culturing is inadequate. Recently, analyses of bacterial community structure that do not depend on cultivation have been widely used (5, 18, 23). In 1993, Muyzer et al. introduced denaturing gradient gel electrophoresis (DGGE), a new genetic fingerprinting technique, to microbial ecology (32). DGGE enables researchers to analyze multiple samples simultaneously, and many studies have been carried out to obtain outlines of the bacterial communities associated with environmental perturbations or seasonal, spatial, and geographical variability (15, 25, 33, 35).
In this study, we used DGGE and cloning analysis of 16S ribosomal DNA (rDNA) amplified with universal bacterial primer sets to describe the geographical and temporal succession in the bacterial community structure along the Changjiang River in 1998 and 1999.
MATERIALS AND METHODS
Site description and sampling.
Surveys were carried out along the Changjiang River in October 1998 and October 1999, but a survey was conducted in Lake Dongting and Lake Poyang only in October 1999 (Fig. 1). Samples were collected twice with a 5-liter Niskin sampler (General Oceanics Inc., Miami, Fla.) at seven points along the river in 1998 (stations 5, 16, 20, 24, 28, 32, and 35), at eight points along the river in 1999 (stations 2, 7, 16, 20, 24, 28, 32, and 35), and in Lake Dongting and Lake Poyang in 1999. We collected all the samples from within 2 m of the surface, because it was impossible to collect samples of deeper water due to the fast flow of the river. A 9-liter (final volume) sample was mixed in a 10-liter polyethylene tank. Samples used for total bacterial cell counting were transferred to 15-ml test tubes from the tank and then fixed with glutaraldehyde (final concentration, 2%) immediately after sampling. Samples used for analysis of bacterial community structure were transferred to duplicate 500-ml polypropylene bottles from the tank and stored at −80°C until further processing within 1 month.
FIG. 1.
Map of the Changjiang River basin, showing the locations of the survey stations (reprinted with permission from Microsoft Corporation).
Subsamples of water used for analysis of nutrients (PO4-P, NH4-N, SiO2, NO2-N, and NO3-N), suspended solids (SS), and dissolved organic carbon were filtered with precombusted GF/F filters (Whatman International Ltd., Maidstone, United Kingdom). The nutrient and dissolved organic carbon concentrations were measured with a TRAACS-800 autoanalyzer (Bran+Luebbe, Tokyo, Japan) and a TOC5000A total organic carbon analyzer (Shimadzu, Kyoto, Japan), respectively. To determine the concentration of SS, filters were weighed after drying at 60°C, and the values were calculated as follows: [(weight of filter with particles) − (weight of blank filter)]/(volume of river water filtered).
Bacterial cell counts.
Bacterial cell densities were determined from sonicated (total bacteria) and nonsonicated (free-living bacteria) subsamples by direct counting with an epifluorescence microscope. The modified method of Crump et al. (11) was used to enumerate the free-living bacteria and total bacteria. Briefly, samples to which a Triton X-100 solution was added (1 drop of a 0.5% solution per ml of sample) were sonicated for 10 s (10 W, 1/8-in.-diameter tip) in an ice bath by using a Cell Disrupter 185 (Branson Ultrasonics Corp., Danbury, Conn.). The sonication conditions were optimized to obtain the highest discrete value. Sonicated samples were filtered with a 3-μm-pore-size filter (diameter, 25 mm; Nuclepore, Tübingen, Germany) to remove large particles, and then the filtrate was further processed to count the bacterial cells. Nonsonicated samples were prepared by the procedure described above but without sonication.
The direct cell count method of Hobbie et al. (20) was used. Bacterial cells in water samples prepared as described above were filtered onto a 0.2-μm-pore-size black Nuclepore filter. Bacterial cells on the filter were stained for 5 min with an acridine orange solution (1 μg/ml) and observed with an epifluorescence microscope (BX60; Olympus, Inc., Tokyo, Japan). The number of bacteria per milliliter of sample was estimated based on counts of at least 10 randomly chosen microscope fields and the volume of the filtered sample. The volume of each filtered sample was adjusted so that the total number of cells counted in 10 fields exceeded 300. The concentration of particle-attached bacteria was calculated by subtracting the concentration in nonsonicated subsamples from the concentration in sonicated subsamples.
DNA extraction, PCR, and DGGE.
Water samples (50 ml) were filtered with a 0.2-μm-pore-size filter (diameter, 25 mm; type JG; Millipore, New Bedford, Mass.). Total DNA was extracted from bacterial cells trapped on the filter by using a Fast DNA kit (Bio 101 Inc., Vista, Calif.). Bacterial cells were recovered from the filter as a cell suspension by washing the filter with 1 ml of CLS-TC buffer (Bio 101) and were transferred to a Fast DNA tube (Bio 101) containing a matrix designed for the lysis of most of the cell types. The mixture was processed in a Fast Prep 120 instrument (Bio 101) for 15 s at 4 m/s. The following procedures were performed as described in the manufacturer's instructions.
DGGE was performed as described previously (33). The V3 region of bacterial 16S rDNA fragments was amplified by using primers 357F-GC (5′-CGCCCGCCGCGCGCGGCGGGCGGGGCGGGGGCACGGGGGGCCTACGGGAGGCAGCAG-3′) and 518R (5′-GTATTACCGCGGCTGCTGG-3′) as the PCR primers. Each amplification reaction mixture (20 μl) consisted of 0.5 U of Ex Taq DNA polymerase (Takara Shuzo, Otsu, Japan), 1 μl of total DNA solution, 2 μl of 10× PCR buffer, each primer at a concentration of 0.25 μM, and a mixture containing each deoxynucleoside triphosphate at a concentration of 100 μM in a tube. A touchdown program (32, 33) was implemented as follows: after initial denaturation at 94°C for 5 min, 30 cycles of 94°C for 1 min, the annealing temperature for 1 min, and 72°C for 1 min were performed, and then the reaction mixture was kept at 72°C for 7 min. During the reaction cycle, the annealing temperature was decreased by 1°C from 65 to 56°C every second cycle in the first 20 cycles. The annealing temperature was 55°C in the last 10 cycles. The amplicons were purified with Wizard PCR preps (Promega, Madison, Wis.), and then aliquots (2 μl) of purified amplicons were analyzed by electrophoresis on 2% agarose gels and were quantified densitometrically. For DGGE, 250 ng of purified amplicons was used. DGGE was performed by using a D-Code system (Bio-Rad Laboratories, Inc., Hercules, Calif.).
Acrylamide (8%) gels were prepared and electrophoresed with 0.5× TAE buffer (1× TAE buffer is 0.04 M Tris base, 0.02 M sodium acetate, and 10 mM EDTA [pH 7.4]). The DGGE gel contained a 20 to 70% gradient of urea and formamide in the direction of electrophoresis as a denaturant, a condition common in many studies; 100% denaturant consisted of 40% (vol/vol) formamide and 7 M urea. DGGE was conducted at a constant voltage of 35 V at 60°C for 18 h. The gel was stained with SYBR Gold (Molecular Probes, Eugene, Oreg.) and photographed on a UV transilluminator.
Analysis of DGGE profiles.
The DGGE band profile was analyzed with an image-analyzing system (Image Master; Amersham Pharmacia Biotech, Uppsala, Sweden), and the densities and migration of the bands were calculated. Principal-component analysis (PCA) based on the density and migration of the bands was performed with the Pirouette 2.6 software package (Infometrix Inc., Woodinville, Wash.).
Sequencing of DGGE bands.
Sequencing of the DGGE bands was performed as described previously (38). A band in the DGGE gel was carefully excised with a razor blade under UV illumination and then placed in 100 μl of Tris-EDTA buffer. DNA was extracted from the gel piece by overnight incubation at 4°C, and then 0.5 μl of supernatant was used as the template DNA in a reamplification PCR performed with primers 357F-GC and 518R. The resulting amplicons were electrophoresed again on a DGGE gel to verify the position of the original band. This operation was repeated until the band appeared to be a single band. After this, a PCR with primers GC-2 (5′-GAAGTCATCATGACCGTTCTGGCACGGGGGGCCTA-3′) (33, 44) and 518R was performed to obtain a sufficient amount of template DNA for sequencing. Subsequently, the amplicons were purified with Wizard PCR preps (Promega) and sequenced directly by using a BigDye terminator cycle sequencing kit (Applied Biosystems, Foster City, Calif.). When the sequencing procedure failed due to the presence of many ambiguous peaks, the amplified DNA was cloned with an Ordinal TA cloning kit (Invitrogen, Carlsbad, Calif.), and a clone library was constructed and sequenced randomly.
Construction of 16S rDNA clone library.
A 16S rDNA clone library was constructed as described previously (37). The PCR primers used to amplify the 16S rDNA of bacteria were primers 27F (5′-AGAGTTTGATCCTGGCTCAG-3′) and 1492R (5′-TGACTGACTGAGGYTACCTTGTTACGACTT-3′). The amplification reaction mixture used was the same as that used for amplification of DNA for DGGE, as described above. The reaction conditions were as follows: after initial denaturation at 96°C for 4 min, 25 cycles of 96°C for 30 s, 50°C for 30 s, and 72°C for 1 min were performed, and then the reaction mixture was kept at 72°C for 7 min. The amplicons were visualized in a 1% agarose gel containing ethidium bromide at a concentration of 0.5 μg/ml and were recovered from the gel by using a DEAE-cellulose membrane (DE81; Whatman International Ltd.); then they were cloned into the pCR 2.1 vector with an Ordinal TA cloning kit (Invitrogen). Ligations were transformed into competent cells of Escherichia coli TOP10F′. White colonies were randomly picked and screened directly for inserts by performing colony PCR with primers for the vector (primers RV-K [5′-GTGGAATTGTGAGCGGATAACAATTTCACA-3′] and M13-U [5′-CGACGTTGTAAAACGACGGCCAGT-3′]).
Sequencing of 16S rDNA clone library and phylogenetic analysis.
Plasmid DNA was prepared from the clones with a QIAprep spin miniprep kit (Qiagen, Crawley, United Kingdom). Plasmid DNA was then sequenced according to the direction of insertion by using an ABI model 377 XL automated DNA sequencer (Applied Biosystems) and a BigDye terminator cycle sequencing kit (Applied Biosystems). All the sequences were compared with similar sequences of reference organisms by performing a BLAST search (3, 4). Phylogenetic trees were constructed by the neighbor-joining method (36) with the CLUSTAL X software package (41).
Nucleotide sequence accession numbers.
The sequences obtained in this study are available in the GenBank database under accession numbers AF428731 to AF428806 (CR98-5 clone library), AF428807 to AF428882 (CR98-24 clone library), AF428883 to AF428958 (CR98-35 clone library), AF428959 to AF429034 (CR99-35 clone library), AF429187 to AF429262 (CR99-2 clone library), AF429111 to AF429186 (CR99-24 clone library), AF429035 to AF429110 (CR99-7 clone library), AF428579 to AF428654 (CR99-D clone library), AF428655 to AF428730 (CR99-P clone library), and AY071871 to AY071902 (band 1-1 to band 14).
RESULTS
Changes in nutrient concentrations.
The concentrations of SS in the river decreased gradually from upstream to downstream (193 to 70 mg/liter in 1998, 323 to 240 mg/liter in 1999). The concentrations of SS in the lakes were markedly lower (70 mg/liter in Lake Dongting and 8 mg/liter in Lake Poyang) than those in the river. The NO2-N concentrations decreased dramatically between stations 20 and 24 (1.71 to 0.32 μmol/liter in 1998 and 1.30 to 0.06 μmol/liter in 1999) and remained low (<0.42 μmol/liter) from station 24 downstream. The NH4-N concentrations declined gradually from upstream to downstream (3.61 to 1.5 μmol/liter in 1998 and 4.59 to 0.68 μmol/liter in 1999). The concentrations of dissolved organic carbon were stable in the riverine stations (133.9 μmol/liter in 1998 and 118.2 μmol/liter in 1999 on average) and in the lakes (132.5 μmol/liter in Lake Dongting and 128.6 μmol/liter in Lake Poyang). The concentrations of NO3-N were also stable along the river, and the concentrations in 1999 (65.1 μmol/liter on average) were higher than those in 1998 (38.2 μmol/liter).
Bacterial cell densities along the Changjiang River and in the lakes.
Bacterial cell densities are shown in Fig. 2. The total bacterial cell densities fluctuated within a range from 105 to 106 cells/ml in both years. In addition, no appreciable tendency was observed along the river. The bacterial cell densities in 1999 were slightly higher than those in 1998. The percentages of particle-attached bacteria upstream (station 5 in 1998 and station 2 in 1999) and downstream (station 35 in 1998 and stations 32 and 35 in 1999) were markedly lower than those at the intermediate stations in both years. In the two lakes, the total cell densities and percentages of particle-attached bacteria (44% in both lakes) were markedly higher than those at riverine stations.
FIG. 2.
Bacterial cell densities along the Changjiang River in 1998 and 1999. The numbers indicate the stations.
PCR-DGGE analysis of bacterial community structure. The DGGE profiles of the bacterial communities for seven (1998) and eight stations (1999) along the river and for the two lakes (1999) are shown in Fig. 3. Many DGGE bands were observed in the profiles. The DGGE patterns appeared to be slightly different for the different stations in each year and were very similar in 1998 and 1999. Bacterial diversity, as determined by the Shannon index (H′), gradually decreased from upstream to downstream in both 1998 and 1999 (1.31 to 1.11 in 1998 and 1.28 to 1.17 in 1999). The Shannon index was derived from the following equation H′ = −∑Pi log Pi (13), where Pi is the importance probability of the bands in a gel lane. Pi was calculated as follows: Pi = ni/N, where ni is the band intensity for individual bands and N is the sum of the intensities of bands in a lane. The results indicate that from upstream to downstream, the variety of bacterial species decreased and specific bacterial strains began to dominate.
FIG. 3.
DGGE band profiles for samples obtained along the Changjiang River in 1998 and 1999 and in the two lakes in 1999. Lane numbers correspond to station numbers. Lane D, Lake Dongting; lane P, Lake Poyang; lanes M, DGGE marker. The numbers of the individual DNA bands are indicated in Table 1.
PCA analysis of the DGGE band profiles provided more information about these differences (Fig. 4) and showed that the bacterial communities gradually and continuously changed from upstream to downstream. When the plots of the two lakes were omitted, the pattern shifted along PC1 from station 2 or 5 to station 28 and then moved along the PC2 axis for the last two or three stations. Similar trends occurred in 1998 and 1999. The two lake communities were quite distinct from the communities at the riverine stations in 1999.
FIG. 4.
PCA of DGGE profiles. The numbers indicate the stations.
Sequencing and identification of DGGE fragments.
From upstream to downstream, the intensities of bands 8, 10, 11, and 12 increased, and the intensities of bands 1, 2, 4, and 5 decreased (Fig. 3). Bands 6 and 7 were observed at all the stations. Bands 2 and 7 were bright in the upstream samples, while bands 7, 12, and 14 were bright in the downstream samples. The prominent DGGE bands were recovered and sequenced, and their phylogenetic affiliations are shown in Table 1. Only 2 of the 14 numbered bands (bands 12 and 14) were directly sequenced. The other bands were sequenced following cloning, because they could not be sequenced directly due to comigration of DNA derived from different bacterial strains.
TABLE 1.
Sequence similarities to closest relatives and phylogenetic affiliations of DNA recovered from DGGE gel
| Banda | Closest relative
|
|||||
|---|---|---|---|---|---|---|
| No. | Accession no. | Similarity (%) | Organism | Accession no. | Phylogenetic affiliation | Source |
| 1-1 | AY071871 | 97 | Uncultured bacterium clone Ebpr10 | AF255634 | CFB group | Biological phosphorus removal process |
| 1-2 | AY071872 | 92 | Uncultured bacterium clone SR-425-3 | AF203939 | CFB group | Polluted groundwater |
| 1-3 | AY071873 | 86 | Uncultured bacterium clone SR-615-17 | AF203967 | CFB group | Polluted groundwater |
| 2-1 | AY071874 | 99 | Uncultured α-proteobacterium FL13A04 | AF446303 | α-Proteobacteria | Yellowstone hot springs |
| 2-2 | AY071875 | 99 | Uncultured Cytophagales clone PRD01a001B | AF289149 | CFB group | Freshwater |
| 3-1 | AY071876 | 98 | Uncultured β-proteobacterium ESR 9 | AF268293 | β-Proteobacteria | Freshwater |
| 3-2 | AY071877 | 100 | Caulobacter sp. strain A1 | AF361188 | α-Proteobacteria | Freshwater |
| 3-3 | AY071878 | 100 | Uncultured Crater Lake bacterium CL500-17 | AF316660 | High-G + C-content gram-positive bacteria | Lake |
| 4-1 | AY071879 | 99 | Microbacterium foliorum | AJ249780 | High-G + C-content gram-positive bacteria | Phyllosphere |
| 4-2 | AY071880 | 97 | Uncultured Crater Lake bacterium CL500-6 | AF316790 | CFB group | Lake |
| 5-1 | AY071881 | 100 | Uncultured Crater Lake bacterium CL500-95 | AF316665 | High-G+C-content gram-positive bacteria | Lake |
| 5-2 | AY071882 | 89 | Uncultured sludge bacterium A12b | AF234699 | Unknown | Nitrifying-denitrifying activated sludge |
| 5-3 | AY071883 | 90 | Unidentified eubacterium clone BSV07 | AJ229181 | Low-G+C-content gram-positive bacteria | Paddy soil |
| 6-1 | AY071884 | 90 | Unidentified eubacterium | AJ005996 | CFB group | Soil |
| 6-2 | AY071885 | 96 | Uncultured eubacterium clone IAFR119 | AF270947 | High-G+C-content gram-positive bacteria | Soil |
| 7-1 | AY071886 | 97 | Flavobacterium columnare | AB015480 | CFB group | Diseased fish |
| 7-2 | AY071887 | 98 | Uncultured firmicute clone GOBB3-CL124 | AF388884 | Firmicutes | Environmental samples |
| 7-3 | AY071888 | 99 | Flavobacterium sp. strain GOBB3-209 | AF321038 | CFB group | Estuary |
| 8-1 | AY071889 | 90 | Uncultured bacterium clone TDC-S1:26 | AF447142 | CFB group | Degradation of tetrachloroethene |
| 8-2 | AY071890 | 98 | Uncultured Crater Lake bacterium CL120-17 | AF316668 | High-G+C-content gram-positive bacteria | Lake |
| 9-1 | AY071891 | 87 | Uncultured CFB group bacterium clone CD13H9 | AF441869 | CFB group | Southern Caribbean Sea |
| 9-2 | AY071892 | 100 | Uncultured Crater Lake bacterium CL0-27 | AF316644 | High-G+C-content gram-positive bacteria | Lake |
| 9-3 | AY071893 | 100 | Uncultured soil bacterium C0111 | AF128708 | α-Proteobacteria | Soil |
| 10-1 | AY071894 | 94 | Unidentified gram-positive low-G + C-content bacterium | AJ239992 | Low-G+C-content gram-positive bacteria | Sulfurous lakes |
| 10-2 | AY071895 | 98 | Uncultured bacterium SY6-50 | AF296201 | CFB group | Lake |
| 10-3 | AY071896 | 95 | Uncultured Crater Lake bacterium CL0-93 | AF316708 | CFB group | Lake |
| 11-1 | AY071897 | 99 | Uncultured Crater Lake bacterium CL500-67 | AF316664 | High-G+C-content gram-positive bacteria | Lake |
| 11-2 | AY071898 | 97 | Uncultured bacterium clone Ebpr7 | AF255646 | CFB group | Biological phosphorus removal process |
| 12 | AY071899 | 98 | Bacterial 16S rRNA gene of isolate LD12 | Z99997 | α-Proteobacteria | Cosmopolitan freshwater bacteria |
| 13-1 | AY071900 | 89 | Uncultured bacterium clone LO13.22 | AF358015 | CFB group | Soil |
| 13-2 | AY071901 | 95 | Uncultured Crater Lake bacterium CL120-125 | AF316675 | High-G+C-content gram-positive bacteria | Lake |
| 14 | AY071902 | 99 | Uncultured Crater Lake bacterium CL500-91 | AF316679 | High-G+C-content gram-positive bacteria | Lake |
Most of the bands consisted of multiple sequences, and representative sequences in each clone library are shown.
The sequences of most of the bands sequenced were similar to 16S rDNA sequences reported for uncultured organisms obtained from environmental samples from such sources as lakes, freshwater, and soil (8, 12, 34, 42). They were affiliated with the Cytophaga-Flexibacter-Bacteroides (CFB) group, the high-G+C-content gram-positive bacteria, and the α-proteobacteria, which is known to be a common bacterial group in freshwater (23). No sequences affiliated with β-proteobacteria and the Verrucomicrobia were obtained.
Bacterial community structure as determined by clone library analysis.
The bacterial community structure was analyzed by the clone library method in detail. We constructed a clone library and sequenced 76 clones for stations 5, 24, and 35 in 1998 and for stations 2, 7, 24, and 35 in 1999. The community structures in Lake Dongting and Lake Poyang were also analyzed in 1999. The compositions of the libraries are shown in Fig. 5. In the Changjiang River the dominant bacterial groups were the α- and β-proteobacteria, the CFB group, and the high-G+C-content gram-positive bacteria; γ- and δ-proteobacteria, low-G+C-content gram-positive bacteria, Verrucomicrobia, and Planctomycetes were also present as minor groups.
FIG. 5.
Compositions of clone libraries at the division and subdivision levels. The numbers indicate the stations. D, Lake Dongting; P, Lake Poyang; C/F/B group, Cytophaga-Flexibacter-Bacteroides group.
The significant differences in bacterial composition from station to station in both years were as follows. The levels of β-proteobacteria decreased from upstream to downstream (41 to 12% in 1998 and 25 to 13% in 1999); the levels of the CFB group rapidly decreased in the midstream and recovered downstream; and the levels of high-G+C-content gram-positive bacteria gradually increased from upstream to downstream (16 to 29% in 1998 and 8 to 28% in 1999). The differences between the lakes were as follows: in Lake Dongting, β-proteobacteria significantly dominated and a low level of high-G+C-content gram-positive bacteria was observed, while the opposite pattern was observed in Lake Poyang.
DISCUSSION
This study is the first study to examine bacterial community structure along such a large river by using molecular biology techniques such as DGGE and the clone library method. We performed DGGE to compare the bacterial community structures (Fig. 3 and 4) and used the clone library method to analyze the bacterial community composition at each station (Fig. 5). These techniques could be associated with possible biases introduced by PCR, such as chimera formation (30), heteroduplex formation (16), template annealing (40), and differences in copy number of 16S rDNA (14). Despite these disadvantages, PCR-based approaches have provided valuable information which allows us to compare the communities in different environments. In addition, the physicochemical properties and the bacterial cell densities did not differ markedly among our sampling stations, and we treated all samples identically to ensure as far as possible that any biases occurred to the same degree throughout the analysis. Furthermore, the effects of biases can be minimized when relative changes are studied in a continuous habitat like that in this study. Therefore, the water samples could be compared.
Bacterial diversity and succession in freshwater bacterial communities have been examined in many studies. In the Ter River, Šimek et al. analyzed α- and β-proteobacteria, the CFB group, and high-G+C-content gram-positive bacteria by fluorescence in situ hybridization and reported that these groups accounted for the major proportion of the community (39). Dominance of the same bacterial groups has been found in most of studies of bacterial communities in lakes (17, 19, 42). Crump et al., who compared bacterial communities in the Columbia River, its estuary, and the adjacent coastal ocean, indicated that the dominant bacterial groups shifted from α- and β-proteobacteria, Verrucomicrobia, and gram-positive bacteria in the river water to α- and γ-proteobacteria in the coastal ocean (10). Methé et al., who summarized their results by comparing the bacterial communities of marine and freshwater environments, determined the frequencies of occurrence of the CFB group and β-proteobacteria and observed a low frequency of occurrence of γ-proteobacteria in freshwater (31). Furthermore, Hugenholtz et al. reviewed the bacterial community compositions in various natural environments and determined the frequencies of occurrence of α- and β-proteobacteria and actinobacteria (23). The bacterial community structure in the Changjiang River was therefore similar to that found in a freshwater environment.
The flora and fauna of estuaries tend to be poor in terms of species compared to the flora and fauna of the adjacent river and ocean habitats because of selection for salinity tolerance in brackish waters (22). Crump et al. reported the existence of high bacterial diversity near the head of a salt wedge due to the mixing of seawater and freshwater in the Columbia River estuary (10). However, the samples in this study were collected at riverine and lake stations, where the salinities were 0.14‰ (even at the station closest to the river mouth) and 0.21‰, respectively. Therefore, it is impossible to compare our results with the results described in these reports.
It has been suggested that in the Ter River the differences in bacterial communities result from complex interactions associated with several major factors, such as variations in hydrological and nutrient conditions, substrate availability, and bacterivory (39). Although similar interactions may occur in the Changjiang River, our information on the factors responsible for the interactions is limited, and further investigation is needed.
We anticipated that we would see dramatic changes in succession along the river, but PCA of the DGGE band profiles revealed an orderly and gradual succession of the bacterial community structure from upstream to downstream in both 1998 and 1999. This succession was observed mainly along the PC1 axis from stations 2 to 35 (distance, approximately 2,000 km). We used the PCA plots of the DGGE profiles obtained from Lake Dongting and Lake Poyang as a measure of the extent of succession at the riverine stations. As these lakes are the largest lakes in the Changjiang River basin and the only two lakes that are not separated from the Changjiang River by floodgates, they were adequate for this purpose. The lakes were some distance from the riverine stations on the same PCA plot (Fig. 4), although they were located adjacent to stations 16 and 24, respectively. This suggests that the bacterial community structure in the lakes was very different from that at the nearby riverine stations.
One would have expected the changes in bacterial communities to be related to changes in the water quality, such as changes in nutrient concentration, pH, and temperature. These water quality parameters depend on the inflow from surrounding tributaries and lakes and from rain. The Changjiang River basin surveyed in this study is a wet area with a humid temperate climate and many tributaries and lakes. However, changes in nutrient concentrations were not appreciable along the river. This is not surprising, considering the buffering action of the large amount of water flowing into the river compared to the small amount of inflow from the river basin.
Although the concentrations of nutrients (SS, NO3, and NH4) in Lake Poyang differed noticeably from those at the riverine stations, the nutrient concentrations observed at station 24, near the mouth of Lake Poyang, were similar to those at the adjacent riverine stations, indicating the effects of dilution by the large volume of water flowing along the river. This dilution may explain why the bacterial communities along the Changjiang River did not differ appreciably. Another reason may be the rapid flow of the river. The average flow rate of the Changjiang River is 4 m/s. It takes only 1 or 2 weeks for water to flow from Chongquing to Shanghai (2,500 km); this is not a long enough period to lead to changes in bacterial community structure.
Although PCA revealed that the bacterial community structure in each lake was very different from that at the riverine stations, the influence of the lake inflow on the bacterial communities at the riverine stations did not appear to be significant. This was confirmed by PCA performed without the DGGE profiles obtained from the lakes; that is, the succession patterns were almost the same in the presence and in the absence of the DGGE profiles for the lakes (data not shown). However, the fact that the bacterial community structure in the lakes differed from that in the river should be carefully considered, because the commissioning of the dam will lead to the formation of an extremely large reservoir along the river. In the lakes, the flow of water is slower than the flow at the riverine stations, and the SS concentration is therefore reduced by sedimentation. The high turbidity at the riverine stations restricts the growth of phytoplankton because of light limitation (21). A decrease in turbidity may alleviate this light limitation and promote the growth of phytoplankton, with a resulting change in the structure of the bacterial communities in the lakes. Furthermore, the reduced flow rates in the lakes should encourage the formation of organic particles, as evidenced by the higher proportion of particle-attached bacteria in the lakes than at the riverine stations. It has been reported that the community structures of free-living bacteria and particle-attached bacteria are different (1, 10). Thus, one would expect that the increase in the formation of particles may result in a difference between the bacterial community structures in the lakes and at the riverine stations.
The completion of the Three Gorges Dam will lead to the formation of a very large reservoir with a very slow flow. Although evidence that the lake bacterial community structure had any effect on the bacterial community structure in the river was scant, the lake and river communities were different. Because the reservoir will be part of the river, it will affect the bacterial community structure both upstream and downstream, including in the estuary. The resultant fluctuations in the bacterial community structure along the river may induce changes in the functional roles of bacterial communities in the biogeochemical cycles. Based on the results of our study, it is difficult to predict accurately the future structure of the bacterial communities in the reservoir. In general, sedimentation and formation of an anaerobic layer in lakes may increase NH4 generation by reducing nitrite and nitrate levels (46). Because we expect that an anaerobic layer may develop in the reservoir, it will be interesting to study the fate of the microorganisms involved in the nitrogen cycle. Although we did not perform a functional analysis in this study, further studies along these lines should provide accurate predictions for future water quality in the river.
Acknowledgments
This work was carried out within the framework of international collaborative research on integrated environmental in river catchment based on the agreement between the National Institute for Environmental Studies (NIES), Japan, and the Yangtze Valley Water Resources Protection Bureau, People's Republic of China, and was supported by the Special Research Project Fund of the Environmental Agency of Japan.
We thank S. Murakami, S. Hayashi, H. Koshikawa, and K. Xu, National Institute for Environmental Studies, Japan, for sample preparation and helpful discussions. We also thank the captain and crew of the research vessel for their assistance in the studies and all the members of this project for their collaboration.
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