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Applied and Environmental Microbiology, May 2001, p. 2284-2291, Vol. 67, No. 5
National Institute of Public Health and the
Environment (RIVM), Dept. MGB, NL-3720 BA Bilthoven, The
Netherlands
Received 26 October 2000/Accepted 6 March 2001
There is a paucity of knowledge on microbial community diversity
and naturally occurring seasonal variations in agricultural soil. For
this purpose the soil microbial community of a wheat field on an
experimental farm in The Netherlands was studied by using both
cultivation-based and molecule-based methods. Samples were taken in the
different seasons over a 1-year period. Fatty acid-based typing of
bacterial isolates obtained via plating revealed a diverse community of
mainly gram-positive bacteria, and only a few isolates appeared to
belong to the Proteobacteria and green sulfur bacteria.
Some genera, such as Micrococcus,
Arthrobacter, and Corynebacterium were
detected throughout the year, while Bacillus was found
only in July. Isolate diversity was lowest in July, and the most
abundant species, Arthrobacter oxydans, and members of
the genus Pseudomonas were found in reduced numbers in
July. Analysis by molecular techniques showed that diversity of cloned 16S ribosomal DNA (rDNA) sequences was greater than the diversity among
cultured isolates. Moreover, based on analysis of 16S rDNA sequences,
there was a more even distribution among five main divisions,
Acidobacterium,
Proteobacteria, Nitrospira,
cyanobacteria, and green sulfur bacteria. No clones were found
belonging to the gram-positive bacteria, which dominated the cultured
isolates. Seasonal fluctuations were assessed by denaturing gradient
gel electrophoresis. Statistical analysis of the banding patterns revealed significant differences between samples taken in different seasons. Cluster analysis of the patterns revealed that the bacterial community in July clearly differed from those in the other months. Although the molecule- and cultivation-based methods allowed the detection of different parts of the bacterial community, results from
both methods indicated that the community present in July showed the
largest difference from the communities of the other months. Efforts
were made to use the sequence data for providing insight into more
general ecological relationships. Based on the distribution of 16S rDNA
sequences among the bacterial divisions found in this work and in
literature, it is suggested that the ratio between the number of
Proteobacteria and
Acidobacterium organisms might be
indicative of the trophic level of the soil.
Agriculture is of prime
importance for The Netherlands where traditionally high-input
arable farming is being practiced. On high-input farms,
microorganisms are generally thought to play a minor role in soil
fertility because most nutrients in inorganic fertilizers are readily
available for the plants and do not require degradation or
mineralization. However, because the government aims to reduce tillage
and the use of pesticides and inorganic fertilizer, it is generally
thought that the role of soil microorganisms in the decomposition and
mineralization of complex organic compounds and in the reduction of
plant pathogens will increase (21, 25, 33). On the other
hand, it is expected that the application of genetically modified crops
(32a) and of genetically modified microorganisms will increase, which
might change bacterial community composition and affect microbial
processes (8, 9, 39). To date, only limited information
exists on microbial diversity and dynamics in agricultural soil
(3, 4, 41). In order to assess the magnitude of changes in
the bacterial community as a result of anthropogenic activity, it is
necessary to gain knowledge on bacterial diversity and seasonal changes
in "healthy" agricultural soil (7, 13, 41).
The object of our study was a field plot on the Lovinkhoeve
experimental farm, situated on reclaimed land in the Noordoostpolder (19, 21), on which summer and winter wheat has been grown alternately by using common agricultural practices since 1944. This
field has never been treated with pesticides, and only organic manure
has been applied. Studying the microbial community in this soil
provides information on the bacterial diversity, species richness, and
seasonal variation in bacterial composition of a nonpolluted
agricultural soil with a known history of cultivation. Previously,
microbial processes and food web interactions have been studied
intensively on various plots of this farm, and it was found that
decomposition processes were dominated by bacteria (4).
Bloem and coworkers (5) found that microbial biomass and
the ratio of dividing cells to divided cells in winter wheat fields
showed peak levels in spring and autumn, which could be indicative of
increased bacterial growth and activity.
To obtain knowledge on the microbial diversity, in this work both
cultivation- and molecule-based methods were applied to soil samples.
The fraction of the bacterial community in soil that can be cultured is
estimated to range from 0.1 to 1% of the total; using molecular
methods, a substantially larger diversity of the bacterial community
can be detected (11, 17, 38). Therefore, the use of
molecular techniques for the detection and analysis of microorganisms
in the environment is increasing steadily (6, 14, 16, 20, 22, 25,
28, 36). While typing of isolates and clones seems more suitable
for studying bacterial diversity in detail, these methods might not be
particularly suited to map seasonal changes. For this purpose the
analysis of large numbers of isolates or clones is needed; therefore,
denaturing gradient gel electrophoresis (DGGE) was performed to create
banding patterns representing the bacterial community
(27). DGGE banding patterns obtained using general
prokaryotic primers have been shown to reveal only the predominant
species and are expected to be more suitable for detecting significant
changes in the microbial community (27). Gelsomino and
coworkers (16) showed by DGGE analysis of soil samples
taken from one plot over a 1-year period that seasonal fluctuations
were small, suggesting that the soil bacterial community is dominated
by a limited number of dominant and stable microorganisms. Previously,
Felske and coworkers (14) also found little variation in
DGGE patterns over time in grasslands. However, both studies lacked a
statistical analysis of the variability of the banding patterns, which
is of the utmost importance for determining the magnitude of the
natural fluctuations.
In the present study, bacterial diversity and seasonal variability of
the bacterial community in wheat field soil were investigated both by
culturing techniques and by direct DNA analysis on four different dates
throughout the year. The most abundant isolates were typed by fatty
acid analysis, and the most abundant clones were identified by partial
sequencing and phylogenetic analysis. Whole-community diversity in soil
samples was visualized by DGGE of specific fragments of 16S DNA
sequences (29). The aim of this study was to analyze
bacterial diversity in Lovinkhoeve soil, to compare data obtained by
cultivation-based methods with data found using molecular techniques,
to investigate the magnitude of seasonal changes in the bacterial
community, and to use the data to search for general ecological
relationships (18, 41).
Sampling site.
To study the soil microbial community,
samples were obtained from the De Lovinkhoeve experimental farm in the
Noordoostpolder, The Netherlands. Detailed characteristics of the silt
loam soil at this location have been described previously (19,
21). The field was studied from September 1995 to August 1996. On November 1, 1995, the field was ploughed and subsequently sown with
winter wheat (Triticum aestivum var. Herzog). The field was
manured on April 25, 1996 (500 kg/ha), and no pesticides were used. The
wheat was harvested on September 9, 1996.
0099-2240/01/$04.00+0 DOI: 10.1128/AEM.67.5.2284-2291.2001
Copyright © 2001, American Society for Microbiology. All rights reserved.
Diversity and Seasonal Fluctuations of the Dominant Members of
the Bacterial Soil Community in a Wheat Field as Determined by
Cultivation and Molecular Methods
ABSTRACT
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
INTRODUCTION
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
MATERIALS AND METHODS
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
Plating of soil samples and analysis of cultured isolates.
Aliquots of 5 g of soil were used for determining the number of
culturable cells. Soil moisture content was determined by weighing
fresh and dried soil (100°C for 24 h). Samples were plated onto
10-fold-diluted tryptic soy agar (Oxoid) containing 100 µg of
cycloheximide ml
1 to inhibit fungal growth, as
described by Smit and coworkers (36). Plates were
incubated at 28°C, and the number of CFU was counted daily from day 2 to day 8 to include data on copiotrophs versus oligotrophs
(10). The geometric means of the log number of CFU per
gram of dry soil was calculated. The percentage of fast-growing
bacteria was determined by dividing the number of CFU counted on day 2 by the number of CFU on day 8.
70°C.
DNA extraction from soil, PCR amplification and cloning of 16S ribosomal DNA (rDNA). Total DNA was extracted from 10-g aliquots of soil by using a bead-beater as described by Smalla et al. (35), and the DNA was purified as described by Smit et al. (36). To date it is unknown which percentage of the soil bacteria is lysed; however, bead-beating has been shown to lyse spores of Bacillus, which are generally difficult to break (26).
To obtain a collection of 16S rDNA clones, total DNA extracts from the 10 samples taken at the same date were pooled before PCR. Amplification was performed with a primer set for eubacteria: 338 to 355, 5'-ACTCCTACGGG[A/G][G/C]GCAGC-3' (12), and 1491 to 1473, 5'-GGTTACCTTGTTACGACTT-3' (36). PCR mixtures of 50 µl contained 5 µl of PCR buffer 2 (10-fold concentrated; Boehringer); 1.7 mM MgCl2; 200 µM concentrations of each deoxynucleoside triphosphate, 300 nM primer, and 2.6 U of Expand Long Template enzyme mix (Boehringer); 1 µl of template DNA; and sterile Millipore water up to 50 µl. For incubation of the PCR mixtures, a temperature touchdown program was used which consisted of incubation at 94°C for 1 min, 64°C for 1 min, and 72°C for 3 min for two cycles. Then the annealing temperature was lowered in 2°C steps with each two cycles until 54°C was reached, and at this annealing temperature, 30 more cycles were performed. The incubation was finished with a 72°C, 10-min incubation step. PCR-amplified 16S rDNA sequences from the soil microbial community were separated on a 1% agarose gel in Tris-borate-EDTA buffer (89 nM, 89 mM, and 2 mM). Bands were excised, and the DNA was purified by centrifuging the agar pieces for 15 min at 16,000 × g in a Wizard column without resin. The flowthrough containing the DNA was collected and used without further purification. DNA fragments were ligated into a pGEM-T vector, which has 3'-T overhangs to facilitate cloning of PCR products (Promega, Madison, Wis.). Ligation mixes were transformed into ultracompetent Escherichia coli XL1-Blue cells (Stratagene, Cambridge, United Kingdom) according to the manufacturer's instructions. White colonies on 5-bromo-4-chloro-3-indolyl-
-D-galactopyranoside (X-Gal) medium were picked, and the insert size was determined by
performing direct PCR on the cell material using M13 forward and
reverse primers. Clones containing an insert with the expected size
were cultured in 2 ml of Luria broth and then stored at 70°C.
For DGGE analysis, PCR primers F-968 and R-1401, which were described
by Nübel et al. (29), were used and amplification was performed in a Hybaid PCR Express thermocycler. Samples were first
denatured at 94°C for 5 min, followed by 40 cycles of 94°C for 1 min, 60°C for 1 min, and 72°C for 1 min. Amplification was finished
by a final extension step at 72°C for 10 min.
ARDRA to select the most abundant isolates and clones. In order to select the dominant members from both the clone and isolate collection, amplified rDNA restriction analysis (ARDRA) was performed. DNA was liberated from bacterial isolates using a method which was shown to be effective for both gram-negative and gram-positive bacteria (M. Vaneechoutte, State University Ghent, Ghent, Belgium, personal communication). Briefly, bacterial colonies were grown on 10-fold-diluted tryptic soy agar at 28°C for 2 to 3 days. Cells were suspended in 20 µl of lysis buffer (0.05 M NaOH, 0.25% sodium dodecyl sulfate) and heated for 15 min at 95°C. The resulting lysate was diluted with 200 µl of distilled water and centrifuged for 5 min at 16,000 × g. One microliter of the cleared supernatant was used for PCR amplification (see above). The amplified DNA was digested with 20 U of Taq I for 2 h at 65°C to generate ARDRA profiles. Digests were separated on 2% agarose gels. Both clones and isolates were grouped based on their digestion patterns using the Biogene (V96.15) software package (Vilber Lourmat, Marne-la-Vallee, France).
Partial sequencing and phylogenetic analysis of the most abundant soil clones. Clones obtained from Lovinkhoeve soil samples were partially sequenced using the primers F-968 and R-1401 previously described by Nübel et al. (29). Fragments for sequence analysis were obtained by cycle sequencing using the ABI PRISM Big Dye Terminator Cycle Sequencing Ready Reaction Kit. Samples of both forward and reverse primers were analyzed on an ABI 373 DNA sequencer. Consensus sequences were obtained using the DNAsis software package (version 2.5; Hitachi Ltd., San Francisco, Calif.). The partial 16S rDNA sequences were screened against those in GenBank/EMBL by using Blast (1). The most homologous sequences were used to construct a multiple alignment using ClustalW (an online program at the Institute Pasteur website [http: //www.pasteur.fr]). A phylogenetic tree was made from these aligned sequences by neighbor joining using the Treecon program (version 1.3b; Yves van de Peer). The clones sequenced in this study are coded LC (Lovinkhoeve clone), followed by the month of sampling and a number. The tree was rooted using Verrumicrobium as the outgroup (see Fig. 2).
Fatty acid analysis to identify the most abundant isolates.
Isolates were streaked on Trypticase soy broth agar in four quadrants,
and the plates were incubated at 28°C for 24 h. A loopful of
cell material of late-log-phase cells was harvested. Fatty acids were
extracted and methylated according to the procedure described by the
manufacturer (Microbial ID, Inc.). Samples were analyzed using the
Microbial Identification System on a Hewlett-Packard 5898A gas
chromatograph (Palo Alto, Calif.). Chromatograms were compared to a
large database of well-known reference cultures previously grown on
Trypticase soy broth agar. Species names of the organisms with the most
similar chromatograms are given (Table 1).
|
1/logN, in which N represents the
total number of isolates and S is the number of different species. To
calculate diversity in relation to the sampling size, the Shannon index was used: H = 
(ni/N) (log
ni/N) (34). The evenness of the species distribution was calculated using the equation E = H/logS (31).
DGGE analysis of the bacterial soil community. Partial 16S rDNA sequences were amplified from soil extracts using the primers F-968 and R-1401 described by Nübel et al. (29). DGGE gels were made using the Bio-Rad Gradient Delivery System establishing a gradient from 30% to 60% denaturant. Two 6% (wt/vol) polyacrylamide (acrylamide-N, N'-methylenebisacrylamide 37; 5:1) stock solutions were made, one with 30% denaturant containing 1× TAE (40 mM Tris, 20 mM acetic acid, 1 mM EDTA [pH 8.3]), 12% (vol/vol) formamide, and 2 M urea and one with 60% denaturant containing 24% formamide and 4.2 M urea. Polymerization was achieved by adding 0.26% (vol/vol) ammonium persulfate (10% solution) and 0.15% (vol/vol) N,N,N',N'-tetramethylethylenediamine (TEMED). On top of this gradient gel a 1-cm stacking gel was poured, consisting of 6% polyacrylamide in 1× TAE without denaturant.
An approximately 6-µl PCR sample was applied on the gel, and gels were run at 60°C at a constant voltage of 100 V for 16 h in a DCcode Universal Mutation Detection System (Bio-Rad Laboratories, Hercules, Calif.). Gels were stained in 25 ml of 1:10,000 diluted Sybr Gold (Molecular Probes, Eugene, Oreg.) in 1× TAE for 15 min and were destained in ultrapure water for 45 min. Banding patterns were visualized on a Dark Reader (Clare Chemical Research, Denver, Colo.), and pictures were digitized using a charge-coupled device camera and the Biocapt software program (Vilber Lourmat).Analysis of the DGGE community profiles. Banding profiles of duplicate samples from September, January, May, and July from Lovinkhoeve soil were analyzed using the Bionumerics program (Applied Maths, Kortrijk, Belgium). Normalized intensity values and positions of detected bands of all lanes were used for cluster analysis and statistical analysis. Similarity coefficients calculated based on detected bands according to Dice were used to construct a complete linkage dendrogram (see Fig. 4). Band positions and intensity values were also used for statistical analysis. The Euclidean distance was calculated and visualized by multidimensional scaling (not shown). In order to test if the banding profiles of the repeat samples (i.e., samples taken simultaneously) were more similar than those of samples taken at different months of the year, a permutation approach was used (1a). Data consisting of density values and band positions of the eight samples were grouped in all four possible groups of two. The average distance of the four pairs was calculated for all possible 40,320 combinations. The null hypothesis is that the duplicates behave like a random grouping of 4 × 2 profiles. We then considered the distances between the four matched pairs and calculated the probability that the average distance is less than or equal to the observed distance. When the duplicates behave randomly, the average distance would lie in the middle of this distribution, whereas if the duplicate profiles were more similar, the average distance would be smaller.
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RESULTS |
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Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
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Analysis of cultured bacteria.
The log number of CFU per gram
of dry soil was similar on all sampling dates. The log number of CFU
was 7.7 (0.23) in September, 7.8 (0.15) in January, 7.7 (0.19) in May,
and 7.7 (0.20) in July. The portion of fast-growing cells was low in
January (17%) and relatively high in July (35%). The mean air
temperature during the sampling months was September, 14°C; January,
2°C; May, 8°C; and July, 17°C; and the soil moisture content
was September, 22%; January, 26%; May, 15%; and July, 17%.
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Phylogenetic analysis of the dominant 16S rDNA clone
sequences.
Clones belonging to the most abundant ARDRA groups were
selected for sequence analysis, since they were thought to represent bacteria which were present in Lovinkhoeve soil in relatively high
numbers. Phylogenetic analysis of these 16S rDNA sequences revealed
that they displayed close relationships to a wide range of clones or
bacterial species of various divisions (Fig.
2). There is no correlation between
sampling date and clone types found, and apart from the
Acidobacterium division, no distinct, closely related clone groups can be recognized. The great
diversity and low number of clones analyzed can be responsible for this phenomenon. The majority of clones appeared to cluster in bacterial taxonomic groups which are generally found in soil. In most analyses of
16S rDNA sequences from soil, members belonging to the
Acidobacterium division dominated the clone
collection (11, 20). It was remarkable that a number of
clones, e.g., those belonging to the 
- and
-Proteobacteria, resemble clones previously
found in freshwater or sediment samples. However, others have also
detected sequences affiliated with isolates or clones from water and
more extreme environments, such as hydrothermal vents and hot springs
(25). In order to compare the diversity of the isolates
with the diversity of the 16S rRNA clones, an overall diversity of the
16S rRNA clones was calculated, based on the assumption that sequences
with three or more different base pairs belong to different operational
taxonomic units representing certain species. Using this criterion,
bacterial diversity calculated using the Shannon-Weaver index (H) was
found to be 1.34 and the evenness was 0.985. In the July sample, the
number of clones belonging to the Proteobacteria was also
relatively low, although considering the small sample size, conclusions
based on these statistics cannot be made. To compare the results from
the culture-based analysis and the sequence analysis, the percentage of
isolates or clones belonging to the different bacterial divisions was
determined (Fig. 3). The large difference
between the results obtained by both methods is clear. The majority of
the bacteria detected by cultivation-based methods belong to the high-
and low-GC gram-positive bacteria and, to a lesser extent, to the
Proteobacteria. The 16S rDNA clones detected with
molecularly based methods are more evenly distributed among the
Proteobacteria, the
Acidobacterium division, and the
Nitrospira, cyanobacteria, and green sulfur bacteria.
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DGGE profiles of Lovinkhoeve soil samples and seasonal
fluctuations.
At first glance the DGGE patterns from the different
months look similar because the most intensely stained bands appear in all lanes (Fig. 4). This indicates that
there is probably a relatively large, stable population of
microorganisms detectable by molecular techniques. High similarities
between DGGE banding patterns of soil samples taken on different months
in the year are indicative of the existence of a stable, dominant
community; previously, similar results were also found in other soils
(14, 16). Nevertheless, a more thorough analysis showed
quite a number of low-intensity bands which differed in the various
samples. These low-intensity bands are responsible for the differences
between the samples, which can be analyzed using the Bionumerics
software package. Cluster analysis revealed that the patterns generated
from duplicate samples are more similar to each other than to those
from other months. The September and May profiles are relatively
similar and cluster together with the January one, while the July
profile differs substantially from all other samples (Fig. 4).
Multidimensional scaling confirmed that the July sample differs from
those of the other months (not shown). In order to test if the profiles
of duplicate samples were more similar than the profiles from different data, a permutation approach was used. The null hypothesis was that the
duplicates would behave like a random grouping of 4 × 2 profiles.
Data from all eight profiles were grouped in all 40,320 possible
combinations, and the average distance between the pairs was
calculated. The average distance of 1,726 random pairings was
smaller than the observed one. The one-sided P value
is then 1,726/40,320 (0.04), which is <0.05 and
indicates that the null hypothesis should be rejected. This indicates
that duplicate profiles are significantly more similar to each other
than to the profiles of the other dates.
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DISCUSSION |
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Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
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Bacterial diversity and dynamics in Lovinkhoeve soil samples were
assessed by fatty acid-mediated typing of isolates. While the log
number of CFU appeared to be stable around 7.7 to 7.8 per g of dry soil
on all sampling dates, both the percentage of fast-growing cells and
the diversity varied (Table 1). The data suggest that the culturable
bacterial population in January differed from the population on the
other dates, although the differences might also have been caused by
differences in cell physiology in certain microorganisms. Certain
bacterial species or genera isolated from soil in a cold period could
experience a shock when plated and incubated at relatively high
temperatures, which could prevent them from growing. The same species
might grow very well on plates when isolated from soil in a warm
period. Such drawbacks are intrinsic to the use of culture-based
methods for the analysis of microbial communities. The percentage of
fast-growing cells reached the highest value in the July sample, and
diversity was greater than in the January one, although below the
values of September and May. This coincides with the appearance of
Aureobacterium and Bacillus species. The most
dominant bacterial genera detected by plating appeared to be
Micrococcus and Arthrobacter. These genera
are often found in various soils, such as those of wheat fields,
deciduous woodlands, grasslands, and sand dunes (23, 37).
These organisms seem to be typical inhabitants of bulk soil; data from
a study by Olssen and Persson (30) showed that gram-positive bacteria were reduced in the rhizosphere, as opposed to
the bulk soil. In another study, plant roots were shown to have a
selective effect towards -Proteobacteria
(pseudomonads) to the detriment of gram-positive bacteria and those of
the Acidobacterium division
(24). Nevertheless, a number of Pseudomonas
species were detected throughout the year except for the July sample. Many Pseudomonas species are R strategists and have
copiotrophic characteristics, and many are associated with plant roots
which provide them with nutrients (40). Nutrient-limited
and relatively warm and dry conditions in bulk soil are thought to be
unfavorable to pseudomonads and might be responsible for this decline
(40). The culturable bacterial population in July seems to
be different from those on the other months, since only gram-positive
bacteria were detected (Table 1; Fig. 1). The disappearance of the
pseudomonads from the isolates coincides with the appearance of several
bacilli in July. Figure 1 was made to obtain a more comprehensive view of the distribution of the isolates over the major bacterial divisions in the different months. When the distribution of the isolates over the
bacterial divisions is considered, May and September have high values,
seven and five divisions, respectively, as opposed to January and July,
which have members in, respectively, three and two divisions (Fig. 1).
These high values might be attributed to the fertilization, ploughing,
and harvest. Bloem and coworkers (4) showed enhanced
microbial activity and levels of organic C in Lovinkhoeve soil in
spring and autumn. In both periods, nutrients become available for
microorganisms from fertilization (spring) and from decaying plant
material left in the soil after harvest (autumn). The relatively high
diversity values in September and May suggest that various different
microorganisms profit from these nutrients.
Seasonal changes in microbial community diversity were also visualized by the DGGE banding profiles. Cluster analysis of the profiles revealed distinct differences between soil samples of the different months, while duplicate samples clustered closely together (Fig. 4). The July profiles were very different from those from the other samples, while both the May and September patterns cluster together. Results from the statistical analysis of the banding profiles confirm that the observed differences exceed the variations in banding patterns present in duplicate samples. This observation was in line with the seasonal changes observed in the isolates, although probably a completely different part of the community was sampled by both methods. Results on seasonal fluctuations obtained by cultivation-based methods might be more sensitive to detect changes, since the culturable part of the community might react more rapidly to changes in temperature or humidity. Changes in cell physiology resulting from differences in environmental conditions could select for a certain fraction of the microbial community which might outgrow the rest of the community. Ferroni and Kaminski (15) found a relationship between seasonal temperature changes and the number of psychrophylic and mesophylic isolates from the sediment-water interface. However, in soil other parameters such as humidity and nutrient supply could also play a crucial role.
Studying bacterial diversity by molecular methods revealed a different
population from that found by cultivation-based methods. In order to
compare our results with those obtained by cultivation, diversity of
the data was reduced by grouping the clones into the major bacterial
divisions (Fig. 3). Both methods are able to detect bacteria belonging
to the - and
-Proteobacteria. The high percentage of
high-GC gram-positive bacteria which is detected by cultivation is
remarkable as opposed to a more even distribution of the rDNA sequences
over several bacterial divisions. Apparently, these bacteria are only a
minor fraction of the total community, since no sequences of high-GC
gram-positive bacteria were found. Other work has shown that the DNA
extraction method combined with the primers used in this study can
detect gram-positive bacteria (14, 26, 29). These results
suggest that the culturable fraction of the community is only a small
part of the total community.
Phylogenetic analysis of the 16S rDNA sequences showed that most clones have a high homology to isolates or clones previously obtained from various soil types (Fig. 2).
There are only a few studies that present data on phylogenetic analysis of 16S rDNA sequences from soil allowing a comparison to the data presented in this work (6, 11, 20, 25). A large proportion of the sequences from Lovinkhoeve soil, approximately 30%, belonged to the Acidobacterium division, of which sequences are detected in soil worldwide (2, 11, 17, 20). Approximately 35% of our clones belong to the Proteobacteria, and no gram-positive bacteria were found. Dunbar and coworkers (11) found that approximately 50% of their clones isolated from natural soil belonged to the Acidobacterium division, 10% to the gram-positive organisms, and 10% to the Proteobacteria. McCaig et al. (25) analyzed clones from intensively fertilized grassland and found that 13% of their clones belonged to the gram-positive bacteria, 50% belonged to the Proteobacteria, and 7% to the Acidobacterium division. Similarly, Borneman et al. (6) detected only 16% Proteobacteria in relatively oligotrophic grass pasture soil; however, the Acidobacterium division was at that time not yet recognized as a separate division. In our search to discover more general ecological relationships, the ratio between the Proteobacteria and the Acidobacterium division was calculated in order to compare our data with results found in literature. This ratio might be indicative for the nutrient status of the soil ecosystem, since the ratio was 0.16 in oligotrophic soil (11, 20); 0.34 in low-input agricultural soil (6); 0.46 in this work, which also represents a low-nutrient system; and 0.87 in a high-input agricultural system (25). However, in the study by McCaig and coworkers (25), no major differences between community diversities of improved and unimproved grassland soil were found. Although the assumption that the ratio between the Proteobacteria and the Acidobacterium division is dependent on the trophic level of the soil is based on only a few studies, results from another investigation seem to confirm this conclusion. Marilley and Aragno (24) found that the rhizosphere, which is a relatively nutrient-rich niche for bacteria, has a positive selection for the Proteobacteria and reduced the percentage of the Acidobacterium division. However, it must be noted that small differences in the percentages could occur, since the methods used for DNA extraction and primers for PCR differ among the various studies.
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ACKNOWLEDGMENTS |
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This investigation was performed by order of the Dutch Ministry of Housing, Spatial Planning and the Environment (VROM), Directorate-General for Environmental Protection (DGM).
We thank J. Schröder for his cooperation in taking Lovinkhoeve soil samples and N. Nagelkerke for performing the statistical analysis of the DGGE banding patterns. We are indebted to J. D. van Elsas and J. Bloem for their inspiring discussions and for critically reading the manuscript.
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FOOTNOTES |
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* Corresponding author. Mailing address: National Institute of Public Health and the Environment (RIVM), Dept. MGB, Antonie van Leeuwenhoeklaan 9, NL-3720 BA Bilthoven, The Netherlands. Phone: (31) 30 274 3924. Fax: (31) 30 274 4434. E-mail: Eric.Smit{at}RIVM.NL.
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Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
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Applied and Environmental Microbiology, May 2001, p. 2284-2291, Vol. 67, No. 5
0099-2240/01/$04.00+0 DOI: 10.1128/AEM.67.5.2284-2291.2001
Copyright © 2001, American Society for Microbiology. All rights reserved.
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