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Applied and Environmental Microbiology, February 2001, p. 702-712, Vol. 67, No. 2
0099-2240/01/$04.00+0 DOI: 10.1128/AEM.67.2.702-712.2001
Copyright © 2001, American Society for Microbiology. All rights reserved.
Impact of Dilution on Microbial Community Structure and
Functional Potential: Comparison of Numerical Simulations and
Batch Culture Experiments
Rima B.
Franklin,1
Jay L.
Garland,2
Carl H.
Bolster,3 and
Aaron L.
Mills1,*
Laboratory of Microbial Ecology, Department
of Environmental Sciences, University of Virginia, Charlottesville,
Virginia 229041; Dynamac Corporation,
Kennedy Space Center, Florida 328992; and
School for Forestry and Environmental Studies, Yale
University, New Haven, Connecticut 065113
Received 31 July 2000/Accepted 5 December 2000
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ABSTRACT |
A series of microcosm experiments was performed using serial
dilutions of a sewage microbial community to inoculate a set of batch
cultures in sterile sewage. After inoculation, the dilution-defined communities were allowed to regrow for several days and a number of
community attributes were measured in the regrown assemblages. Based
upon a set of numerical simulations, community structure was expected
to differ along the dilution gradient; the greatest differences in
structure were anticipated between the undiluted-low-dilution communities and the communities regrown from the very dilute (more than
10
4) inocula. Furthermore, some differences were expected
among the lower-dilution treatments (e.g., between undiluted and
101) depending upon the evenness of the original
community. In general, each of the procedures used to examine the
experimental community structures separated the communities into at
least two, often three, distinct groups. The groupings were consistent
with the simulated dilution of a mixture of organisms with a very
uneven distribution. Significant differences in community structure
were detected with genetic (amplified fragment length polymorphism and
terminal restriction fragment length polymorphism), physiological (community level physiological profiling), and culture-based (colony morphology on R2A agar) measurements. Along with differences in community structure, differences in community size (acridine orange direct counting), composition (ratio of sewage medium counts to R2A
counts, monitoring of each colony morphology across the treatments), and metabolic redundancy (i.e., generalist versus specialist) were also
observed, suggesting that the differences in structure and diversity of
communities maintained in the same environment can be manifested as
differences in community organization and function.
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INTRODUCTION |
Ecological diversity, the variety
and abundance of species in different habitats and communities, is one
of the central themes of ecology. Diversity is commonly thought to be a
useful indicator of the well-being of an ecological system; however,
there is considerable debate over the role diversity plays in ecosystem
function (4, 18, 23, 24, 26, 32, 33, 36). Most of this
uncertainty arises from the practical limitations of measuring and
manipulating diversity for experimental studies. Testing the effects of
diversity on any community property or ecosystem function requires
knowledge of the diversity of the community under examination; however, there are no methods currently available that allow microbial diversity
to be measured. Numerous procedures are available for monitoring
changes in community structure (e.g., culture-based analyses, community
level physiological profiling (CLPP), analysis of the lipid content of
microbial cells, and molecular genetic techniques); these approaches
each have biases and limitations that are well documented (for reviews,
see references 1, 3, 8, 13, 16, 28, 39, 41, and 46).
Despite the inability to directly measure diversity, Garland et al.
(9, 11) and Morales et al. (25) successfully
used dilution to manipulate microbial diversity for several
applications. The premise behind these studies was that dilution of a
relatively diverse community would remove rare organism types, creating
mixtures of cells differing in species richness. Regrowth of the
diluted mixtures should then produce cultures of roughly the same
biomass but differing in overall diversity. In these studies, the
various dilution-diversity communities responded differently to
invasion attempts (11, 25) and to environmental stress
(11), with more diverse (less dilute) communities being
more stable and better able to withstand invasion. However, with no
good way to place a numerical value on microbial diversity, the
magnitude of the differences being evaluated remains unknown.
The present work sought to define the relationship between dilution and
resultant changes in diversity and community structure. First, several
numerical simulations were performed in order to develop a set of
expectations about how overall diversity (expressed as the
Shannon-Wiener index), richness (number of species or types of
organisms in the community), and evenness (the relative distribution of
individuals among these types) change with dilution. Next, a series of
microcosm experiments was conducted using batch cultures of sterile
sewage inoculated with serial dilutions of fresh sewage. After regrowth
of these batch cultures, several methods were used to characterize the
communities, including traditional microbiological procedures, CLPP,
and molecular genetic techniques. The regrown communities differed
along the dilution gradient, and the results followed a pattern similar
to that observed in the simulated dilution of a relatively uneven
mixture of organisms. The results of this work will be useful in
planning future studies, as the ability to create natural communities
systematically differing in complexity could allow researchers to
manipulate diversity, perhaps in a quantifiable way, while evaluating
its relationship to other community level properties (e.g., stability,
invasibility, or ecosystem function).
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MATERIALS AND METHODS |
Numerical simulations.
To examine the theoretical effect of
dilution on community inoculum composition, a series of numerical
simulations (coded in MATLAB) was performed. Communities were
constructed by assigning each of 106 individuals a random
species identification based on a normal distribution of integers from
1 to 1,000. The mean of this distribution was set at 500; the variance
(var) was adjusted in order to create communities of different initial
evenness (Fig. 1A). The
var levels used were 100, 250, 1,000, and 20,000, and a perfectly even
initial community was also generated (1,000 types, each containing
1,000 individuals). Dilution of each of these five initial communities was simulated by randomly selecting 1/10 of the individuals from the
array representing the undiluted community. The species identification of each individual in this subset was copied to a second array, which
served as the initial community for the next dilution in the series
(dilutions extended through 105). For each community, at
each dilution level, richness, evenness, and diversity were calculated.
Richness (S) was taken to be the number of organism types
(species) in the community. Diversity was expressed as the
Shannon-Wiener index (H') as
fol- i lows: H' = 
pi
ln pi, where i indicates each species
or category and pi is
the 1 proportion of individuals of each species (37). Evenness (E) was calculated as E = H'/H'max, where
H'max = In S (31).
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FIG. 1.
(A) Distribution of individuals among 1,000 types
in the initial communities used in simulations. Note that both
total abundance and richness were the same in each of the five
communities. The mean of each distribution was set at 500, and the
variance was altered to simulate communities with a dominant
(var = 100, 250, or 1,000) or relatively even (var = 20,000 or even) distribution. (B to D) Simulation results showing how
community structure differed in the various initial communities
for each serial dilution. The x axis represents the
negative exponent of the dilution factor (e.g., 4 corresponds
to a 104 dilution), and the y axis represents
richness (number of types or species) (B), evenness (C), or the
calculated value of the Shannon-Wiener diversity index (D).
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Batch culture experiments. (i) Microcosm setup.
Raw sewage
was collected from the Cape Canaveral Air Station Wastewater Treatment
Facility (Kennedy Space Center, Fla.) and used as the inoculum for the
microcosm experiments. A single large sample (2 liters) was collected
from the equilibration basin and allowed to settle for approximately
2 h to remove large particles. Tenfold serial dilutions (through
106) of the supernatant were prepared in autoclaved
sewage; each of these different dilutions then served as an inoculum
for a series of batch culture incubations. Before dilution, the
concentration of cells in the supernatant, determined by acridine
orange direct counting (AODC; 15), was 1.8 × 106 cells/ml.
The batch cultures were established by adding 1 ml of inoculum to 60 ml
of autoclaved sewage in a 125-ml Erlenmeyer flask.
Seven treatments
(10
0 through 10
6) were maintained in this
experiment, with three replica communities
at each dilution. All flasks
were capped with sterile foam plugs
to prevent contamination and kept
on a shaker table, operated
at 150 rpm, to maintain aerobic conditions.
Each day, 20 ml of
liquid was removed from each flask and replaced with
20 ml of
sterile sewage. After 9 days (three retention times), flasks
were
harvested and
analyzed.
(ii) Cultural counts and diversity of colony morphology.
For
each flask, a serial dilution of the regrown community was plated, in
duplicate, onto both R2A agar (35) and sterile sewage
medium (SM; prepared by mixing 15 g of agar per liter of sewage
supernatant). Plates were incubated at room temperature (ca. 23°C),
and the number of CFU on R2A agar was determined after 3 days. Growth
on SM was evaluated after 6 days.
The diversity of colony morphologies on R2A was compared across the
different dilution treatments. For each flask, two plates
were
selected; on each plate, 25 colonies were randomly chosen
and colony
morphology was described based on size, pigmentation,
form, elevation,
and surface. Richness (number of distinct colony
morphologies),
evenness, and diversity (Shannon-Wiener diversity
index) were then
calculated.
(iii) CLPP.
For each flask, a 101 dilution of
the microbial community was prepared (in sterile water) and inoculated
into Biolog GN microplates, which were then incubated at room
temperature (ca. 23°C). Color formation in each of the 96 wells of
each plate was monitored by periodically (every 2 to 4 h)
measuring the A590 using a Biotek EL 320 microplate reader. Data were normalized using a blank-corrected average
well color development of 0.75 absorbance unit and analyzed using a
principal-components analysis (PCA) (8, 10).
(iv) Dilution-extinction analysis of CLPP.
Dilution-extinction analysis was performed on a subset of the regrown
communities (one replicate flask from each of the 100,
102, 104, and 106 dilution
treatments) to determine the relationship between cell density
(I) and functional richness (number of positive wells, R) (9). Serial dilutions of the microbial
suspensions were inoculated into Biolog GN microplates and incubated at
room temperature for 7 days, and A590 was
measured. A positive response was defined as any value greater than
0.25 absorbance unit (after correction for the control well), and a
hyperbolic model, R = (Rmax × I)/(KI + I), where
Rmax equals the maximum (asymptotic level) of
R and KI is the value of I
when R is one-half of Rmax, was
fitted to the data (9).
(v) Molecular analysis of whole-community DNA. (a) DNA extraction
and quantification.
At harvest, an approximately 40-ml sample was
collected from each flask and the suspended microbial community was
concentrated by centrifugation (23,000 × g, 20 min).
The cell pellet was resuspended in 200 µl of phosphate-buffered
saline and stored at 
20°C. Whole-community DNA was extracted using
the High Pure PCR Template Preparation Kit (Boehringer Mannheim,
Indianapolis, Ind.) and quantified using the PicoGreen double-stranded
DNA quantification reagent (Molecular Probes, Eugene, Oreg.).
(b) AFLP.
Amplified fragment length polymorphism (AFLP) was
completed using the Perkin-Elmer Microbial Fingerprinting Kit (PE
Applied Biosystems, Foster City, Calif.) following the manufacturer's instructions for analysis of individual bacterial strains. Three different pairs of primers, each with a different fluorescent label,
were used for selective AFLP amplification: EcoRI-AA (JOE labeled) with
MseI-CA, EcoRI-AC (FAM labeled) with MseI-CC, and EcoRI-AT (NED
labeled) with MseI-CT. For the complete primer and adapter sequences
and an explanation of the primer selection criteria used, see the
PE Applied Biosystems AFLP Microbial Fingerprinting Protocol
(PE Applied Biosystems, Foster City, Calif.).
Selective amplification products were resolved using an ABI Prism
310 Genetic Analyzer by following the manufacturer's instructions
with
slight modification; for each sample with each primer pair,
1 µl of
PCR product was analyzed using a sample injection time
of 10 s.
Data were analyzed using the Genotyper software (PE Applied
Biosystems), and the presence or absence of each peak in each
sample
was coded as 1 or 0. Such a data matrix was prepared for
each primer
pair, and the information from the three primer pairs
was pooled into a
single large data set. The Jaccard coefficient
was used to determine
distances between samples (relative similarity),
and a cluster analysis
(unweighted pair group clustering using
arithmetic averages and
between-groups linkage) was performed.
A bootstrapping analysis was
then used to assess the significance
of each group and subgroup in the
cluster analysis (
6).
A PCA was also performed on the original pooled data matrix (SPSS 9.0),
and plots of the first two principal components (PCs)
were made. As PCA
is not mathematically appropriate for use with
binary data, its
application in this study was solely to aid in
visualization of the
relationships among the samples and not for
statistical evaluation.
Such an approach has been used several
times to compare samples
profiled using a variety of similar genetic
techniques (
6,
7,
47,
48); PCA generally provides the
same information (groupings and
relative distances among samples)
as the above-outlined cluster
analysis.
(c) T-RFLP.
Each T-RFLP (terminal restriction fragment
length polymorphism) reaction mixture (50 µl) contained 25 ng of
community DNA; 10 mM Tris-Cl (pH 8.3); 50 mM KCl; 1.5 mM
MgCl2; 200 µM each dATP, dCTP, dGTP, and dTTP; each
primer at a concentration of 0.1 µM; and 1.25 U of Taq DNA
polymerase (21). The bacterial 16S rRNA gene was amplified
using two primers: 1392 Reverse (5' ACGGGCGGTG TGTRC) and 27 Forward (5' AGAGTTTGATCCTGGCTCAG [labeled with the fluorescent
tag 6-FAM]). The PCR program was 94°C for 3 min, followed by
30 cycles of 94°C for 30 s, 56°C for 45 s, and 72°C for
2 min, with a final extension at 72°C for 3 min. PCR products were
purified using the Wizard PCR Preps DNA Purification System (Promega,
Madison, Wis.) and eluted in a final volume of 50 µl. Portions (10 µl) of the purified PCR product were then digested with either the HhaI or MspI restriction enzyme, using the
manufacturer's recommended reaction buffer and 20 U of enzyme (New
England Biolabs, Beverly, Mass.). Digests were incubated at 37°C for
4 h.
The lengths of the fluorescently labeled terminal restriction
fragments were determined for each sample using the ABI 310
Genetic
Analyzer. Three-microliter portions of each digested product
were mixed
with 24 µl of deionized formamide and 1 µl of GeneScan-1000
size
standard (PE Applied Biosystems), denatured at 95°C for 5
min, and
quickly chilled on ice. Electrophoresis was performed
using the same
conditions as for AFLP but with a 40-min run time.
Data were analyzed
using the GeneScan software with a peak height
detection of 100. As
with the AFLP analysis, the presence or absence
of each T-RFLP in each
sample was determined and the data from
each restriction enzyme were
pooled for cluster analysis, bootstrapping
analysis, and
PCA.
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RESULTS |
Numerical simulations.
The results of the numerical
simulations show that while microbiologists generally consider dilution
to be a linear process, the response of various community level
parameters (richness, evenness, and diversity) to such a manipulation
may produce nonlinear results (Fig. 1B, C, and D). There was no change
in the diversity (H') of the even community with dilution
until the number of individuals (103 individuals) equaled
the number of species (103 types) at the 103
dilution. With further dilution, H' decreased in response to the rapid loss of species richness (Fig. 1B) although evenness remained
1.0 (Fig. 1C). In each of the other initial communities, a similar
trend was observed; in general, H' remained constant until
the 103 (var = 250, 1,000, and 20,000) or
104 (var = 100) dilution. After this point,
diversity decreased, corresponding to the loss of species richness
and an increase in community evenness. At the end of the dilution
series (105), the H' of all of the communities
decreased to the maximum theoretical value of H' for a
perfectly even distribution among 10 organisms. The sole exception to
this was in the community created by setting var = 1,000; here,
the value of H' at the 105 dilution was 2.16 because only 9 species were recovered in that particular simulation
whereas 10 were recovered in all of the others.
The richness of the simulated communities decreased with increasing
dilution, but the magnitude of this change varied depending
on the
initial evenness of the mixture (Fig.
1B). For communities
with low
initial evenness (e.g., var = 100 or 250), richness decreased
rapidly with the first dilution. For initial communities that
were more
even, the number of species lost in the first dilution
was small; in
the perfectly even community, no species were lost.
Moreover, the
relative distribution of organisms in the perfectly
even community did
not change upon dilution (Fig.
1C). For all
of the other communities,
evenness increased with dilution, approaching
a theoretical maximum of
1.0 at the 10
5 dilution (except for var = 1,000, as
discussed
above).
Batch culture experiments. (i) Microscopic and cultural
counts.
After 9 days of regrowth, each experimental community was
sampled and AODC and cultural counts were performed (Table
1). An analysis of variance was used to
determine whether each parameter varied significantly across the
different dilution-diversity treatments, and a modified least
significant difference (Bonferroni) test was used for multiple
comparisons. Total cell concentrations were similar across the first
several dilution treatments (100 through
104), but in the communities regrown from each of the
higher-dilution inoculum (105 and 106),
abundance was significantly greater (df = 20, F = 25.7, P < 0.00001). Cultural counts on both SM and R2A agar showed
a trend similar to the AODC, with significantly greater
concentrations of organisms in the higher-dilution-lower-diversity
treatments (R2A, df = 20, F = 4.3, P = 0.0116; SM, df = 19, F = 3.6, P = 0.0259).
Percent culturability on R2A agar (R2A counts divided by AODC) showed a
major increase from the undiluted inocula to the 10
6
dilution (Table
1). The various treatments could be separated
into
three statistically significant subgroups as follows: 1,
undiluted
(10
0) through 10
4; 2, 10
3
through 10
5; 3, 10
6 (df = 20,
F = 61.01,
P < 0.00001). The average percent
culturability
for each subgroup was as follows: 1, 9%; 2, 20%; 3, 100%. Percent
culturability on SM was also calculated, and although
the results
were not statistically significant (df = 19,
F = 1.97,
P = 0.1445),
the same general trend was
observed. Using the subgroupings defined
in the R2A analysis, average
percent culturabilty on SM varied
as follows: 1, 12%; 2, 17%; 3, 44%. The average ratio of the SM
counts to the R2A counts was also
calculated from these data (Table
1). For the lower-dilution
treatments, growth was greater on
SM than on R2A agar although the
difference between the two was
generally not large. However, this trend
was reversed in the high-dilution-low-diversity
treatments
(10
5 and 10
6) where growth on R2A plates
was substantially greater than on
SM.
(ii) Diversity of colony morphologies.
The diversity,
richness, and evenness of R2A colony morphotypes were calculated for
each of the dilution treatments (Fig. 2).
Because of the high growth that occurred on all of the plates spread
from the regrown 106 dilution community, it was not
possible to evaluate these characteristics for that treatment. However,
only three colony types could be distinguished on these plates and all
three were distinctly different from the colony morphologies described
in the other treatments. Overall, colony diversity was highest in the
communities regrown from the undiluted inoculum and decreased with
increasing dilution (Fig. 2A). The greatest change in colony diversity
was observed between the undiluted (100) and
101 regrown communities. Richness decreased along the
dilution-diversity gradient, and the most types were also lost after
the first dilution (Fig. 2B). In general, evenness increased along the
dilution-diversity gradient (Fig. 2C) although a decrease in evenness
was observed between the undiluted (100) and
101 dilution treatments. The distribution of each colony
type across each treatment was also examined, and 40% of the colony
morphologies encountered in the low-dilution-high-diversity treatments
(the 100 and 101 treatments were pooled for
this calculation) were not recovered from any of the other regrown
communities (see Table 3). Furthermore, the colony morphologies identified in the
highest-dilution-lowest-diversity (106) treatment were
all unique.
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FIG. 2.
Results of R2A colony morphology comparison. All values
are reported as the average per R2A plate ± 1 standard error.
Each value was calculated by comparing 25 randomly selected colonies on
each plate, using two replicate plates per flask and three flasks for
each treatment. The sole exception to this was the 104
treatment, where only two of the replicate flasks were compared. The
x axis in each of these graphs represents the negative
exponent of the dilution factor used to create the original inoculum
(e.g., 4 corresponds to a 104 dilution). The y
axis represents diversity of colony morphologies based upon the
Shannon-Wiener diversity index (A), richness (the number of distinct
colony morphotypes) (B), or evenness (C).
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(iii) CLPP.
PCA of the CLPP data showed that the various
communities differed in their overall patterns of carbon utilization
(Fig. 3). An analysis of variance was
performed on the scores from the first two PCs, and two homogeneous
subsets were established. The communities regrown from the undiluted
(100) through the 104 dilution inocula were
significantly different from those regrown from the 105
and 106 dilution treatments. This difference was due
primarily to variation in the PC 1 scores (df = 20, F = 18.2, P < 0.00001); PC 2 did not contribute significantly to
this separation (df = 20, F = 2.23, P = 0.102).
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FIG. 3.
Results of PCA of the CLPP data. Each point represents
the average for three replicate flasks maintained at each dilution;
error bars represent ± 1 standard error. Each treatment is
identified by the negative exponent of the dilution factor used to
create the original inoculum (e.g., 4 corresponds to a
104 dilution). The percentage of variance explained by
each PC is provided.
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(iv) Dilution-extinction analysis of CLPP.
The undiluted
(100) and 102, 104, and
106 dilution treatments were examined using
dilution-extinction analysis of functional characters in the CLPP
assays. Plots were made of the number of positive tests obtained in the
dilutions made from each regrown community versus the number of cells
(AODC) inoculated into each well of the BIOLOG plate (Fig.
4), and the data were fitted with a
rectangular hyperbola to estimate the parameters
Rmax and KI (Table
2). Rmax decreased
along the dilution-diversity gradient; however, considering the
confidence intervals about these estimates, it cannot be concluded that
this decrease was significant. KI decreased
significantly along the dilution-diversity gradient; higher values of
KI were found for communities that were
predicted to have a higher diversity based upon the extent of dilution.
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FIG. 4.
Results of dilution-extinction analysis of CLPP for each
regrown community. The x axis represents the inoculum
density (as measured by AODC) used in each CLPP assay (presented on a
log10 scale). The y axis is the number of
positive tests for each incubation. The results are presented as fitted
lines generated by modeling the untransformed data with a right
rectangular hyperbola; the associated regression statistics for this
fit are given in Table 2. The curvature of the regression lines at
lower inoculum levels is an artifact of the log scaling of the
x axis.
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(v) AFLP.
Combined, the three AFLP primers generated a total
of 106 unique PCR fragments. On average, each sample contained 22 fragments; nearly all (90%) of the bands encountered in the
low-dilution treatments (100 and 101) were
unique, while 50% of the fragments observed in the high-dilution treatments (105 and 106) were not
encountered in any of the other treatments (Table 3).
PCA and cluster analysis of the AFLP data showed that the microbial
communities in this experiment could be separated into
three distinct
groups based on overall genetic composition. The
communities regrown
from the undiluted inocula (and one of the
replicas from the
10
1 dilution) were most unique, a second group was formed
from the
communities regrown from the middle-dilution inocula
(10
1, 10
2, 10
3,
10
4, and one of the 10
5 replicas), and the
third cluster included the communities regrown
from the very dilute
inocula (10
5 and 10
6). This pattern is most
easily visualized on the PC plot (Fig.
5), although cluster analysis produced
the same separations (results
not shown). A bootstrapping procedure
(using 100 replications)
was performed to assess the significance of
the groupings obtained
in the cluster analysis (
6). The
three clusters outlined above
were recovered 93% of the time; this
high value suggests that
the separation was very well supported by the
data and represents
a significant difference in overall structure among
the three
sets of communities.
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FIG. 5.
Results of PCA of the AFLP profiles. Data are presented
for each of three replicate flasks for each treatment, and each value
corresponds to the negative exponent of the dilution factor used to
create the original inoculum (e.g., 4 corresponds to a
104 dilution). The percent variance explained by each PC
is provided.
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(vi) T-RFLP.
Upon digestion with the HhaI
restriction enzyme, 43 different T-RFLP fragments were produced; on
average, an individual sample contained 16 of these fragments. When the
MspI enzyme was used, 42 different fragments were detected;
on average, a sample contained 12 of these. The number of fragments
observed across the dilution gradient did not differ for either enzyme
(Table 3). The proportion of bands unique to each of the three
dilution-diversity groups (100 with 101,
102 through 104, and 105 with
106) was also compared (Table 3).
Based on the PCA (Fig.
6) and cluster
analysis (results not shown) performed on the combined
MspI
and
HhaI data sets, two
groups could be distinguished; the
first group contained the communities
regrown from the undiluted
(10
0) inoculum through the 10
3 inoculum,
while the second group contained the lower-diversity-higher-dilution
treatments. Again, a bootstrapping procedure (using 100 replications)
was used to assess the significance of the results of the cluster
analysis. The highest bootstrap value obtained (not considering
bootstrap values associated with the subgrouping of the replicate
flasks) was associated with the division of the communities into
three
groups: 10
0 through 10
3, 10
4,
and 10
5 with 10
6. However, the bootstrap
value for these groupings was only 47
and it cannot be concluded that
these three groups were significantly
different. It is possible that
the number of fragments compared
in the cluster analysis was
insufficient and that a higher bootstrap
value might have been obtained
if additional restriction digests
(using different enzymes) had been
performed and the data had
been pooled prior to statistical analyses.
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FIG. 6.
Results of PCA of T-RFLP profiles. Data are presented
for each of three replicate flasks for each treatment, and each value
corresponds to the negative exponent of the dilution factor used to
create the original inoculum (e.g., 4 corresponds to a
104 dilution). Due to experimental difficulties with the
T-RFLP analysis, only one of the flasks from the 104
dilution community and only two of the flasks from the
106 dilution community were analyzed. The percent
variance explained by each PC is provided.
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DISCUSSION |
Numerical simulations.
The results (Fig. 1) show that
dilution of a complex microbial community does not change the
overall diversity of each resultant mixture, regardless of
the evenness of the original community, until the size of the
community is decreased so much that the number of individuals in the
mixture approximates the original number of species. After this point,
diversity must decrease with subsequent dilution because each
individual that is removed from the system always removes a species
from the community. This result was anticipated for relatively even
communities, but it was initially surprising to discover that diversity
did not change upon dilution of the more dominant mixtures of organisms
(e.g., var = 100 or 250 in Fig. 1) for the early stages of the
dilution series (through 103). It had been expected that
dilution of these communities would remove rare organisms from the
mixture, causing overall diversity to drop; in fact, the decrease in
species richness upon dilution of the highly dominant communities was
substantial (Fig. 1B). However, this decrease in species richness
was accompanied by a concurrent increase in community evenness,
resulting in little change in overall diversity until the
104 or 105 dilution.
These results suggest that a dilution approach may be used to create
communities differing in diversity by comparing undiluted
(or barely
diluted) mixtures with communities regrown from very
dilute inocula.
This approach should be successful, regardless
of the diversity and
dominance relationship of the starting community;
however, greater
differences are to be expected for more even
initial communities.
Actual experimental communities regrown from
diluted mixtures are not
expected to exactly mimic these simulations,
which only accounted for
the dilution of the inoculum and not
for any variance in regrowth.
Synergistic and mutualistic interactions
among organisms may be
disrupted by the dilution procedure, and
as a result, not all of the
organism types carried through a dilution
series to an inoculum may be
able to regrow. The dilution procedure
also decreases competition among
organisms, and this could permit
types that were not important in the
original community to grow
to unanticipated high abundances. Different
growth rates among
organisms may also impact the diversity of the
regrown communities,
changing evenness from that of the
inoculum.
Batch culture experiments.
The results of the numerical
simulations were used to make specific predictions about the behavior
of the diluted-regrown communities in the batch culture experiments. If
the initial community was evenly distributed, the community structure
of the regrown mixtures would not be expected to change along the
dilution gradient until the number of cells in the diluted inoculum
approximated the total number of types of organisms in the original
community. If the initial community was unevenly distributed, the first
dilution during inoculum preparation would have removed a large number of types of organisms (e.g., in the var = 100 community, the first dilution removed 827 types out of 1,000). However, this loss of richness would have been offset by a simultaneous increase in community
evenness, resulting in no net change in overall inoculum diversity (as
calculated using the Shannon-Wiener index). Nevertheless, regrowth of
communities so different in richness may have resulted in a measurable
change in diversity or community structure between the undiluted and
101 dilution treatments. After the initial dilution of an
unevenly distributed community, evenness would be greatly increased,
and so, differences in subsequent dilutions (102,
103, and 104) were predicted to be
small
until the dilution factor exceeds the original number of types
of organisms in the community as described above.
(i) Traditional microbiological methods.
The regrown microbial
communities were assayed using a number of traditional microbiological
methods; each showed that the microbial communities regrown from the
very dilute inocula (105 and 106) were
unique. Abundance (as determined by AODC and plates counts on R2A and
SM) was always significantly greater in these communities. It is
possible that this variation in bacterial concentration was the result
of differential grazing pressure along the dilution gradient (dilution
of the inoculum would also have changed the amount of predation
pressure in each treatment) (17, 29, 38). However, given
the low concentration of ciliates and other grazers in the undiluted
inoculum (direct microscopic observation, 1.5 × 102
organisms/ml), the impact of these organisms on bacterial abundance should have been small, especially in comparisons of treatments beyond
the 102 dilution.
Ecological theory predicts that when interspecific competition is
decreased (as was done by dilution in this study), populations
can
increase substantially in abundance (
12). In the present
work, the inverse relationship observed between final community
size
and inoculum dilution suggests that interspecific competition
is more
important than intraspecific competition in controlling
total
abundance. In the barely diluted communities (10
0 and
10
1), where diversity, and therefore interspecific
competition, was
higher, community size was much smaller compared
to that of the
very dilute communities (10
5 and
10
6), where diversity and interspecific competition were
lower.
Percent culturability on each growth medium was much higher in the
communities regrown from the high-dilution inocula. Given
that
microbial growth on culture media recovers a limited number
of
organisms, due to inappropriate incubation conditions or an
inability
of certain types of organisms to metabolize the supplied
substrates,
enhanced growth by the 10
6 dilution community suggests
that those organisms are not as limited
in their metabolic capabilities
as those in the undiluted-low-dilution
communities. Furthermore, the
ratio of SM to R2A counts changed
along the dilution-diversity
gradient; communities regrown from
the high dilutions
(10
5 and 10
6) preferred R2A agar, while the
other communities either had no
preference or grew to higher abundances
on SM; this result provides
further evidence that the communities in
the various dilution
treatments were physiologically
distinct.
The comparison of colony morphology on R2A plates showed that the
microbial diversity and richness of the recoverable fraction
of the
communities decreased along the dilution gradient; the
evenness of the
communities increased. Based upon the dilution
simulations, the
greatest difference in community structure was
expected between the
very dilute (10
4 or 10
5) communities and
all of the others, regardless of the structure
of the original
community; each of the other analytical methods
employed in this
research showed this to be true. However, the
greatest difference for
the diversity on R2A plates was between
the undiluted (10
0)
inoculum and the 10
1 dilution treatment. The fact that
any difference in community
structure was detected between these two
dilution treatments suggests
that the original sewage community had
high dominance; the fact
that there was no discernible change in the
overall diversity
of colony types for the high-dilution treatments
(e.g., 10
4, 10
5, and 10
6)
implies that the procedure is not useful for making inferences
about
microbial community structure in low-diversity
situations.
One of the main criticisms of culture-based studies is that the carbon
and nutrient sources found in a single culture medium
are not as
diverse as those found in nature, so only a small fraction
of the
organisms in a sample actually form colonies on a spread
plate; using a
large number of different medium types might increase
the variety of
organisms recovered with a cultural approach. In
this study, a sterile
SM was also used in an attempt to increase
the number of types to
compare when calculating the diversity
index. Unfortunately, the
colonies that grew on the SM were quite
small and generally lacked
morphological distinctiveness, making
a comparison among treatments
impossible. Another concern with
regard to the use of culture-based
procedures is the difficulty
in accurately and consistently identifying
community members,
given the fact that very similar colony morphologies
can occur
among taxonomically distinct groups of organisms. However,
recent
studies have shown that colony morphology can, in fact, provide
an accurate basis on which to define "recoverable diversity"
(
14,
19).
(ii) Genetic measures.
The DNA fingerprinting approaches used
in this study showed a significant difference in overall microbial
community structure along the dilution gradient; in particular,
analysis of both the AFLP and T-RFLP data showed that the very dilute
(105 and 106) communities were distinctly
different from the communities regrown from less-dilute inocula (Fig. 5
and 6). AFLP also separated the undiluted community from the remaining
treatments. The fact that AFLP distinguished a difference in microbial
community structure between the undiluted and 101
dilution communities provides further evidence that the original microbial community (before dilution) had high dominance.
With T-RFLP, PCR is used to amplify the 16S rRNA genes directly from
each community DNA sample using a pair of primers and
analysis of a
community sample produces a fingerprint wherein
each individual band
is, theoretically, derived from a different
organism type (a different
ribotype). However, it is well known
that T-RFLP underestimates the
species richness of a community
because populations that are not
numerically dominant are not
represented if their template DNA
comprises too small a fraction
of the total community DNA
(
5,
21). Moreover, due to the
conservation of
restriction site positions in the 16S rRNA gene;
the resolution of
T-RFLP analysis is not at the species level
but instead reflects the
distribution of higher-order groups.
Another limitation to the
resolving power of T-RFLP is the actual
universalness of primer pairs,
as none of the available universal
primers can hybridize to all of the
known eukaryotic, bacterial,
or archaeal 16S rRNA genes (
2,
52). Despite these limitations,
researchers commonly use T-RFLP
to compare microbial community
structures and frequently interpret the
"number of T-RFLP peaks"
to be reflective of (minimum) community
richness (
21,
22).
In this study, the total number of T-RFLP peaks was expected to
decrease along the dilution-diversity gradient, corresponding
to a loss
of species richness; instead, this number remained essentially
constant
(Table
3). However, the identity of the T-RFLP peaks
changed and this
shift is illustrated by comparing the number
of unique fragments found
in each dilution-diversity group (Table
3). The fact that several
unique peaks were observed in the low-diversity
treatments
(10
5 and 10
6) was surprising. Probability
suggests that the dominant organisms
in the original community are the
ones that should persist through
the dilution procedure and would
therefore be used to inoculate
these flasks. Consequently, nearly all
of the organisms observed
in the very dilute treatments should also
have been detected in
the less-dilute treatments. The fact that this
was not found with
T-RFLP suggests either (i) a lack of discriminative
power of T-RFLP
due to the bias of the procedure toward specific and/or
more-abundant
community members (a bias that seems to change, depending
on the
evenness of the community being analyzed) or (ii) a failure of
these organisms to survive in the less-dilute treatments, despite
their
dominance in the original community and their ability to
thrive in
culture in the more-dilute
treatments.
Since the original sewage community contained 1.8 × 10
6 cells/ml (AODC), the most-dilute community maintained
in this experiment
(10
6) should have been inoculated with
approximately 2 cells. After
regrowth, diversity in this community was
expected to be very
low. On the R2A plates, percent culturability was
high (100%)
and only three colony morphologies were observed, further
suggesting
that diversity in these flasks was quite low. Given this
information,
it was surprising to find that the average number of
T-RFLP fragments
in the 10
6 treatment was so high (8 for
MspI and 10 for
HhaI). It is possible
that this
discrepancy is the result of a technical error with
the T-RFLP, e.g.,
incomplete restriction digestion, which could
produce a number of
differently sized T-RFLP fragments for each
organism type. However,
experimental controls in which the T-RFLP
analysis was applied to DNA
from a pure culture were also performed
and a single T-RLFP peak was
generated in each case. Another possible
explanation is that because an
individual organism can contain
multiple, heterogeneous copies of the
16S rRNA gene (
20,
27,
30,
43,
44), each organism type
could actually have been
responsible for more than one T-RFLP peak.
However, the extent
to which such sequence deviations occur has not
been well studied
and it is unlikely that the detection of multiple,
divergent copies
of a 16S rRNA gene can account for the results
presented
here.
Recent work has discovered that related strains of bacteria can have
the same 16S rRNA gene but may not have the same physiological
profiles
or the same ecological strategies in the environment
(E. Jaspers and J. Overmann, Abstr. ASM Conf. Microb. Diversity,
abstr. B30, 1999).
Presumably, this additional variability is
coded elsewhere on the
bacterial chromosome. Consequently, analysis
of a single gene may not
provide as much resolution when distinguishing
among communities,
compared to procedures that can survey the
entire genome. With AFLP, a
restriction digest is performed on
a DNA sample (similar to RFLP) and
then a set of primer recognition
sequences (adapters) is used to
amplify the restriction fragments
using PCR (
51); the
primers and restriction enzymes used are
not specific for a given gene
or group or genes but can, theoretically,
interact in numerous random
places throughout a genome. AFLP is
very similar in premise and
application to randomly amplified
polymorphic DNA fingerprinting, which
has been used a number of
times to compare microbial community
structures (
6,
7,
47-50).
AFLP is fundamentally different from each of the other procedures
applied in this work, and from most other techniques used
to
compare microbial community structures; in that it is sensitive
to
overall differences between communities
including taxonomic
distances
between organisms. Watve and Gangal (
45) pointed out
that
most procedures would not detect a difference in diversity
between one community composed of four biotypes of coliforms and
another composed of one coliform, one archaebacterium, one
myxobacterium,
and one actinomycete
although many microbial
ecologists would
agree that the latter should be treated as more
diverse. The ability
to differentiate between such mixtures is
important, and it has
been suggested that one way to incorporate this
additional information
is to simply calculate the mean taxonomic
distance between all
pairs of isolates in a community as a diversity
index (
34);
however, the problem of isolation and
taxonomic characterization
of individuals remains. In this study, AFLP
was used to compare
overall diversity, considering richness, evenness,
and taxonomic
relatedness of community members without attempting to
evaluate
each of these elements
separately.
(iii) CLPP.
CLPP compares patterns of carbon substrate
utilization among communities by evaluating the extent to which a
community metabolizes each of 95 different sole carbon sources
(10). When CLPP was applied in this study, the different
dilution communities separated into two distinct groups
(105 and 106 were unique; Fig. 3). These
results are important, as they demonstrate that there were
phenotypic differences among the regrown communities, as well as
the genetic differences already described. This means that the dilution
process not only changed the identity of the organisms in the
communities, as revealed by the genetic analyses, but also changed the
communities' overall metabolic capabilitiesthe most-diverse
(undiluted) community did not have the same functional potential as the
low-diversity (106) community. The fact that the genetic
and phenotypic measurement methods gave similar results in this study
is also meaningful, as the correlation between the two suggests that
genetic differences among communities actually have the potential to be
manifested as differences in function.
Dilution-extinction analysis of CLPP was used to compare the relative
structural diversities of different regrown communities.
This procedure
uses the dilution to extinction of a heterotrophic
microbial community
to evaluate the rate of character loss from
the mixture; assuming that
the rate of character loss is somehow
proportional to the diversity of
the original community, the relative
diversity of the sample can be
estimated (
9). In this study,
dilution-extinction analysis
showed no significant change in the
maximum functional richness
(
Rmax) of each community along the
diversity
gradient. The other regression parameter,
KI, the half-maximum
richness, describes the
rate at which functional characters can
be diluted out of a mixed
community and has been used to assess
relative structural diversity in
a number of different experimental
systems; a higher
KI correlates with a higher diversity and also
with increased niche specialization (
9). In this study,
KI decreased along the dilution-diversity
gradient, confirming that
the communities regrown from the less-dilute
inocula were more
diverse. The community regrown from the undiluted
inoculum was
able to perform a wide variety of functions but lost this
ability
rapidly upon dilution; this suggests a community composed
primarily
of specialists. The low-diversity community, regrown from the
10
6 dilution inocula, had a much lower
KI; this increased conservation
of function
among the individuals in the group may suggest a community
of
generalists. The results of the percent culturability calculations
also showed the high-dilution-low-diversity communities to be
more
generalized in their metabolic
capabilities.
Oftentimes when researchers are advocating the use of molecular
techniques over culture-based procedures, the reason presented
is that
culture-based analyses are too biased toward certain groups
of
organisms. As such, they underestimate the total richness of
a
community in an inconsistent and unpredictable manner. In this
research, 23 colony types were observed on R2A agar (across all
treatments) and only 42 unique T-RFLPs were encountered. Certainly,
the
actual total number of organism types in the original sample
was much
greater. Considering the fact that each of the analytical
methods
employed in this study showed a clear change in overall
community
structure between the 10
4 and 10
5 dilution
treatments (Table
4), the numerical
simulations suggest
that there were between 1,000 and 10,000 types of
organisms in
the original sewage community. This value is consistent
with results
of Torsvik et al., who found that a soil sample can
contained
between 4,000 and 10,000 different bacterial types (
40,
42).
A more precise estimate of the number of types in the
originial
community might have been obtained if a different dilution
scheme
had been used (e.g., intervals smaller than 10-fold).
In this study, significant differences in community structure were
detected using genetic (AFLP and T-RFLP), physiological
(CLPP), and
culture-based (colony morphology on R2A agar) measurements.
Along with
this difference in community structure, differences
in community size
(AODC), composition (ratio of SM counts to R2A
counts, monitoring of
each colony morphology across the treatments),
and metabolic redundancy
(generalist versus specialist) were observed,
suggesting that
structure-diversity differences between communities
maintained in
the same environment can manifest differences in
community organization
and function. Although differences in microbial
community structure
were detected with every measurement method
employed, each procedure
has different methodological limitations
that should be recognized when
that technique is applied. The
results of the experimental incubation
demonstrated that the dilution-regrowth
approach might be a useful way
of generating communities differing
in diversity (richness and
evenness) and varying in overall community
structure. Moreover, the
procedure may be a useful way of analyzing
communities
in this study,
a great deal of information was gained
about the richness and
distribution of the original sewage community
from analysis of the
regrown
communities.
| |
ACKNOWLEDGMENTS |
This work was supported by a NASA/ASEE Summer Faculty Fellowship
(to Aaron Mills), a NASA Planetary Biology Internship (to Rima
Franklin), and a NASA GSRP (grant NGT10-52620).
We gratefully acknowledge the assistance of Jennifer Adams, Ravi
Bashyal, and Michael Roberts.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: University of
Virginia, Department of Environmental Sciences, 291 McCormick Rd., P.O. Box 400123, Charlottesville, VA 22904-4123. Phone: (804) 924-0564. Fax:
(804) 982-2137. E-mail: amills{at}virginia.edu.
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Applied and Environmental Microbiology, February 2001, p. 702-712, Vol. 67, No. 2
0099-2240/01/$04.00+0 DOI: 10.1128/AEM.67.2.702-712.2001
Copyright © 2001, American Society for Microbiology. All rights reserved.
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Abstract
Introduction
