Applied and Environmental Microbiology, December 2003, p. 6961-6968, Vol. 69, No. 12
0099-2240/03/$08.00+0 DOI: 10.1128/AEM.69.12.6961-6968.2003
Copyright © 2003, American
Society for
Microbiology. All Rights Reserved.
Molecular Microbial Ecology Laboratory, CEH-Oxford, Oxford OX1 3SR,1 Department of Agricultural and Environmental Science, The University of Newcastle Upon Tyne, Newcastle Upon Tyne NE1 7RU, United Kingdom2
Received 19 March 2003/ Accepted 1 September 2003
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Soil moisture directly affects the physiological status of bacteria (21). Water availability affects the osmotic status of bacterial cells and can indirectly regulate substrate availability, diffusion of gases, soil pH, and temperature. Further, moisture deficit will stress plants and, as a result, may affect bacterial communities through changes in rhizodeposition and nutrient allocation below ground (29). Ultimately, periods of moisture limitation may affect bacterial communities through starvation, induced osmotic stress, and resource competition, eliciting a strong selective pressure on the structure and functioning of soil bacterial communities.
The involvement of soil moisture in controlling fluxes of important greenhouse gases has been shown for methane (19, 41), nitrous oxide (4, 24), and carbon dioxide (9, 12, 32, 38). While these processes are not entirely the result of microbial activity, it has recently been suggested that the underlying mechanisms may be due to changes in the diversity and activity of bacterial populations (8, 40). In bioremediation studies, degradation rates have been correlated to the culturable activities of specific organisms, which are in turn influenced by moisture availability (7, 20). The role of moisture in regulating the activity of specific culturable populations has also been observed following inoculation of marked bacteria into soil (31). However, such studies on culturable bacteria may not adequately reflect changes occurring in the total community.
Many studies have attempted to analyze microbial responses to drying and rewetting by using chemical determinants of biomass or microbial activities. These studies have generally revealed that drying or drying and rewetting causes a decrease in the total soil biomass (5, 27, 45). Furthermore, rewetting of dried soils is known to cause increased mineralization of carbon (28) and nitrogen (1, 6) coupled with a flush of CO2 efflux (14). The exact roles of microbes in mediating these processes is still largely unresolved, since biomass estimations may be hampered by methodological constraints (13), and so it is difficult to determine whether the effects are biological or physically driven. However, CO2 evolution, as a general measure of gross microbial activity, is known to reflect changes in water potential (36). While these studies have revealed the effect of moisture on broad-scale microbial properties, no inference has been made to changes in diversity of the total bacterial community which underlie these processes. The use of molecular methodologies currently offers the most potential for assessing the diversity of soil communities, yet only a single article was found applying such approaches in investigating moisture effects on soil bacterial populations (22).
Our present study aimed to monitor the molecular diversity and physiological status of soil bacteria subjected to drying and rewetting in intact soil monoliths collected from the Natural Environment Research Council (NERC) Soil Biodiversity field site located at Sourhope (Scotland, United Kingdom). The experimental regimen was applied to allow examination of the bacterial community response to soil drying and recovery following rewetting. The activity of culturable bacteria was measured by performing plate counts on different media, and the substrate utilization potential was assayed by using BIOLOG GN-2 plates (15, 39). To examine culture-independent effects on the extractable cell biomass, total cell counts were compared by flow cytometric analysis of SYBR II-stained cell preparations. Community structure was assessed by denaturing gradient gel electrophoresis (DGGE) of PCR-amplified 16S rRNA genes and 16S rRNA directly extracted from the soil (18). It was anticipated that the RNA transcript might be a more responsive biomarker in this study, due to the known relationship between rRNA abundance and the physiological status of bacterial cells (3, 34).
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In total, nine microcosms were constructed, comprising three replicates of three treatments, and maintained in the open air at Oxford. The three treatment regimens were continual wetting (treatment A), drying (treatment B), and drying and rewetting (treatment C). The experiment ran for 3 months between July and October 2001, and 11 samplings were taken in total at approximately weekly intervals (subsequently referred to as S1 to S11). For samples 1 to 3 (S1, S2, and S3), no experimental treatment was imposed and monoliths were exposed to natural rainfall together with regular watering with filtered distilled water. On 14 August, UV-transparent polyethylene covers were constructed to eliminate rainfall for experimental manipulation of water content. Drying treatments (no watering) commenced after a final watering event on 20 August (following S3). A watering regimen of 2 liters every 2 days (approximately) was adopted for the continually wetted treatments to maintain water content close to field capacity. After approximately 1 month without water, treatment C (rewetted) was established by rewatering on 18 September (following S6). This treatment was subsequently watered with the same regimen as the continually wetted treatment (treatment A).
All microcosms were sampled regularly to determine the effect of the drying regimen on soil moisture content and bacterial activity. Briefly four 1-cm-diameter cores were taken randomly from each pot with a number 4 cork borer to a depth of approximately 7 cm. These samples were mixed to homogeneity in plastic bags, and large roots and plant material were extracted by hand. Subsequently, 0.5-g (wet weight) aliquots of soil were then weighed for dry weight determination and microbiological analyses.
Culturability and total cell
count of bacterial community.
Soil (0.5 g wet weight) was dispersed
in 10 ml of phosphate-buffered saline (PBS) with 1.5 g of
sterile 5-mm-diameter glass beads and mixed by vortexing for 1 min.
Samples were decimally diluted, and 100-µl aliquots were spread
onto full-strength tryptic soy broth agar (TSBA; Difco,
Oxford, United Kingdom), 1/10-strength TSBA, and
pseudomonad-selective agar (PSA; Difco). All media were supplemented
with 100 µg of cycloheximide/ml to suppress fungal growth.
Plates were incubated for 10 days at 18°C prior to colony
counting. The diversity of cultured bacteria was also assayed on the
1/10-strength TSBA plates by extracting all of the colony biomass from
plates of the same dilution. Three milliliters of PBS was added to each
plate, and the biomass was dislodged with a sterile scraper.
Approximately 1.5 ml of cell suspension was then transferred to 1.5-ml
Microfuge tubes and stored at -0°C for
nucleic acid extraction.
Total bacterial cells were enumerated according to the methods of Whiteley et al. (46). Briefly, 0.5 g of soil was dispersed in PBS, and the resulting supernatant was loaded onto a 1.3-g/ml Nycodenz density cushion. Following centrifugation, cell preparations were extracted, washed, and then fixed with a 1% final concentration of paraformaldehyde. Cells were stained with 0.3 µl of the nucleic acid stain SYBR Green II (Molecular Probes) for 20 min in the dark. Positively stained bacterial cells were then enumerated with a FACSCalibur sorting flow cytometer (Becton Dickinson Immunocytometry Systems, Oxford, United Kingdom).
Substrate
utilization analyses.
Nine
milliliters of a 0.5% (wt/vol) soil suspension was prepared and
washed twice by dilution to 50 ml in sterile PBS, mixing, and
centrifugation (Jouan BR4i) for 5 min at 4,000 x g.
Following the cell washes, pelleted cells were resuspended in 20 ml of
sterile PBS, and 100-µl aliquots were dispensed into each of
the 96 wells of the BIOLOG-GN plates (Oxoid). Plates were incubated at
18°C and were manually scored at daily intervals to determine
the number of substrates utilized per day. For each reading, a well was
scored as positive based on visual inspection of color
change.
Statistical comparison of counts
and substrate utilization.
All quantitative data were
statistically analyzed to address three hypotheses. (i) Does short-term
drying decrease bacterial counts? (ii) Does rewetting of temporarily
dried soils reverse the effects of drying? (iii) Are there significant
differences between the three treatments at the termination of the
imposed regimens? To address the first two questions, the change over
time for each monolith was first calculated, followed by comparison of
the mean differences in change between treatments by analysis of
variance. This form of analysis was chosen to circumvent difficulties
in repeated measurement designs where there may be differences between
replicate monoliths prior to the start of the experiment. For the count
data, the estimate used to address the effects of drying was calculated
by subtracting the mean of counts from samples S2 and S3 (before
drying) from the mean of samples S5 and S6 (after drying). To test
rewetting effects, the means of samples S6 and S7 were subtracted from
the means of samples S10 and S11. For the substrate utilization data,
only rewetting effects were tested, and the change over time here was
taken to be the difference between samples S7 and S11. To assess
whether the treatments had an overall effect on culturable activity at
the end of the experiment, analysis of variance analysis was performed
on data from the final sample date (S11). Here, Fisher's
least-significant difference method was used to ascertain differences
between the three treatment means. Prior to statistical analysis, count
data were log transformed, whereas the percentages of substrates
utilized were arcsine transformed. All analyses were performed within
the MINITAB statistical software package (version 13.32; Minitab, Inc.,
State College, Pa.).
Nucleic acid
extraction and amplification.
Total nucleic acids were extracted
for DGGE analyses by the method of Griffiths et al.
(18). Briefly, 0.5 ml of
cetyltrimethylammonium bromide extraction buffer and 0.5 ml of
phenol-chloroform-isoamyl alcohol (25:24:1 [pH 8.0]) were
added to 0.5 g of soil sample or 0.5 ml of cell culture in
BIO-101 multimix bead-beating tubes. Following mechanical lysis and
subsequent solvent extraction, nucleic acids were precipitated from the
extracted aqueous layer with two volumes of 30% polyethylene
glycol 6000 (Fluka BioChemika)-1.6 M NaCl. Pelleted nucleic
acids were washed in ice-cold 70% (vol/vol) ethanol, air dried,
and resuspended in 50 µl of RNase-free Tris-EDTA buffer (pH
7.4). Extracted nucleic acids were then inspected by gel
electrophoresis prior to enzymatic separation of DNA and RNA and PCR or
reverse transcription-PCR amplification (total soil-extracted nucleic
acids only). Following DNase treatment, 16S rRNA was reverse
transcribed with the universal 16S primer 519r
(5'-GTA TTA CCG CGG CTG CTG-3') as
described previously
(18). DNA and cDNA were
then PCR amplified with the GC-clamped forward primer 338f
(5-CGC CCG CCG CGC CCC CGC CCC GGC CCG CCG CCC CCG CCC ACT
CCT ACG GGA GGC AGC-3') and 519r reverse primer
according to the method of Griffiths et al.
(18).
DGGE.
DGGE was performed by using the
De-Code system (Bio-Rad) with a 10% (wt/vol) acrylamide gel with
a 30 to 60% (wt/vol) denaturing gradient (urea and formamide)
running for 6 h at 200 V. All solutions and procedures were
standardized before the running of each gel to optimize consistency
between gels. Gels were stained with SYBR Gold (Molecular Probes, Inc.)
and visualized by UV transillumination. Gel images were analyzed
densitometrically with the Phoretix one-dimensional software package
(Nonlinear Dynamics, Newcastle upon Tyne, United Kingdom), and profiles
were compared by using the multivariate statistical package MVSP
(Kovach Computing, Anglesey, United
Kingdom).
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FIG. 1. Soil
moisture contents for each treatment over the duration of the
experiment. Sampling points are indicated at the base of the graph.
Error bars represent standard deviations of the means (n
= 3). Treatments are indicated by symbols as follows:
, continually wetted; , dried; , dried and
rewetted. Jul, July; Aug, August; Sep, September; Oct, October; Nov,
November.
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FIG. 2. Responses
of culturable bacteria to moisture treatments on three different
culture media: 1/10 TSBA, TSBA, and PSA. Sampling points are indicated
at the base of the graph. Error bars represent standard errors of the
means (n = 3). Treatments are indicated by symbols as
follows: , continually wetted; , dried; ,
dried and rewetted. Jul, July; Aug, August; Sep, September; Oct,
October; Nov,
November.
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View this table: [in a new window] |
TABLE 1. Total
cell counts for all treatments and sample dates determined by flow
cytometric counting of SYBR II-stained cells
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FIG. 3. Recovery
in substrate utilization following rewetting. Plots show the percentage
of the 95 substrates in BIOLOG plates utilized after 4 and 7 days of
incubation of plates at 18°C (a and b, respectively). Sampling
points are indicated at the base of the graph. Error bars represent
standard errors of the means (n = 3). Treatments are
indicated by symbols as follows: , continually wetted;
, dried; , dried and rewetted. Sep, September; Oct,
October; Nov,
November.
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FIG. 4. DGGE
analyses of total extracted rRNA genes (a), rRNA transcripts (b), and
total culturable bacteria (c) for the dried and rewetted treatment
only. Sampling points are indicated above each gel, and the three lanes
for each time point represent the individual profiles obtained from
each replicate pot. Marker lanes comprising amplicons from bacterial
isolates are located at either side of each
gel.
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In contrast to the physiological methods, no moisture-related changes were observed following molecular profiling of the culturable diversity or total nucleic acids extracted from soil. The lack of change in diversity profiles is surprising in light of the effect of moisture upon the community physiology discussed above. For the analysis of culturable diversity, moisture perturbation may not have sufficiently impacted the total community to influence the reported high variation in species assembly on agar plates (11). The absence of change in community profiles based upon extracted rRNA genes or the rRNA transcripts may be partly explained by the large genotypic diversity of bacteria present in soil (42) and the fact that only the dominant templates are detected in DGGE profiles (16). Assuming the total diversity to be log normally distributed (10), it is conceivable that variation within diverse populations of low numerical abundance may not be detected by primers targeting the whole community. This may have been the case for the rRNA gene-based analysis, since no consistent changes in total cell counts were observed, indicating negligible cell growth or death in response to the moisture treatments (as was found in reference 47).
The similarity of rRNA transcript-based community profiles cannot be explained by the lack of variation in total cell counts, since rRNA transcript concentrations should vary independently of biomass and in relation to cellular physiological state (34) and growth stage (3). However, aside from pure culture studies, there is little information on the variation of rRNA content in bacterial cells present in natural environments such as soils. Therefore, it may be possible that small increases in the rRNA content of active cells are masked by more abundant rRNA from quiescent cells. Furthermore, recent research on marine isolates has revealed that RNA levels may not always relate to growth rate, especially during non-steady-state growth (26). Additionally, RNA/DNA ratios have been shown to not always relate to microbial activity in heterogeneous environmental samples such as sediment (25). These findings therefore raise questions on the relative advantages of using rRNA transcript analysis over rRNA gene analysis as a more-responsive biomarker to study soil bacterial communities.
Despite these methodological constraints, our data enforce the belief that soil bacteria may be preadapted to resist moisture variations by regulation of cellular activity (31). The ability of soil bacteria to withstand such perturbations may relate to the so-called starvation state (2). This state is thought to represent a survival strategy for bacterial persistence in harsh, low-nutrient environments and may be mediated by starvation gene expression, cell shrinkage, or sporulation. Specific responses to osmotic stress include sensing mechanisms coupled with the uptake or synthesis of compatible solutes to reestablish cell turgor pressure (33, 48). If our diversity assessments are representative, then it may be that the predominant soil community responds to moisture availability in the same manner, i.e., all the dominant bacterial species are equally capable of surviving drying and no competition occurs after rewetting. While this does not agree with the concepts of copiotrophy and oligotrophy (43), it does reinforce the idea that soil bacteria are able to cope with both high and low nutrient conditions equally well (34).
To conclude, our data implicate the marked resistance of soil bacteria to water stress based upon physiological criteria (culturability and substrate utilization analyses). However, we did not observe significant changes within the total community from a molecular standpoint when directing the analyses at the population level, with both rRNA gene- and rRNA transcript-based 16S profiling. Under the experimental regimen employed (controlled slow perturbation), these latter approaches may lead to an unrepresentative picture of what is occurring in the natural environment. This is likely to be a facet of the large diversity present within these environments, diversity changes occurring within small fractions of the community, and potential physiological adaptations which have yet to be resolved. Indeed, we have recently shown that differences could be detected in the community structure of active cells by prior isolation by using cell sorting of 5-cyano-2,3-ditolyl tetrazolium chloride (CTC)-stainedcells (46). It may therefore be likely that changes occurring in these operationally defined active communities may be more relevant in terms of ecosystem functioning. Further, technologies which directly link the activity of microbes with ecosystem processes, such as the labeling of plant material with 13CO2 (37) and phylogenetic analysis of isotopically enriched rRNA (30), may be a more appropriate way of subdividing the community for more-resolved analyses of the response to perturbation.
We thank Damien Mayoux and Graham Burt-Smith for help with sample collection and Nick Ostle and Niall McNamara for advice on setting up the mesocosms. We extend our gratitude to David Elston and an anonymous reviewer for suggestions which improved the statistical analysis.
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