Previous Article | Next Article 
Applied and Environmental Microbiology, January 2001, p. 190-197, Vol. 67, No. 1
0099-2240/01/$04.00+0 DOI: 10.1128/AEM.67.1.190-197.2001
Phylogenetic Specificity and Reproducibility and New Method
for Analysis of Terminal Restriction Fragment Profiles of 16S rRNA
Genes from Bacterial Communities
John
Dunbar,1,*
Lawrence O.
Ticknor,2 and
Cheryl R.
Kuske1
Biosciences Division1
and Technology and Safety Assessment
Division,2 Los Alamos National Laboratory,
Los Alamos, New Mexico 87545
Received 25 July 2000/Accepted 10 October 2000
 |
ABSTRACT |
Terminal restriction fragment (TRF) analysis of 16S rRNA genes is
an increasingly popular method for rapid comparison of microbial communities, but analysis of the data is still in a developmental stage. We assessed the phylogenetic resolution and reproducibility of
TRF profiles in order to evaluate the limitations of the method, and we
developed an essential analysis technique to improve the interpretation
of TRF data. The theoretical phylogenetic resolution of TRF profiles
was determined based on the specificity of TRFs predicted from 3,908 16S rRNA gene sequences. With sequences from the
Proteobacteria or gram-positive division, as much as 73%
of the TRFs were phylogenetically specific (representing strains from
at most two genera). However, the fraction decreased when sequences
from the two divisions were combined. The data show that phylogenetic
inference will be most effective if TRF profiles represent only a
single bacterial division or smaller group. The analytical precision of
the TRF method was assessed by comparing nine replicate profiles of a
single soil DNA sample. Despite meticulous care in producing the
replicates, numerous small, irreproducible peaks were observed. As many
as 85% of the 169 distinct TRFs found among the profiles were
irreproducible (i.e., not present in all nine replicates). Substantial
variation also occurred in the height of synonymous peaks. To make
comparisons of microbial communities more reliable, we developed an
analytical procedure that reduces variation and extracts a reproducible
subset of data from replicate TRF profiles. The procedure can also be
used with other DNA fingerprinting techniques for microbial communities
or microbial genomes.
| |
INTRODUCTION |
Comparative analysis of complex
microbial communities in natural environments has been hampered by the
lack of effective ways to rapidly measure community diversity,
composition, and structure. The shortcomings of methods that rely on
cultivation are well known, and although DNA-based, culture-independent
techniques have provided new ways to examine the microbial world, the
methods that are effective in community analysis are still quite
limited in number and scope (23). Terminal restriction
fragment (length polymorphism (T-RFLP or TRF) analysis is currently one
of the most powerful methods in microbial ecology for rapidly comparing the diversity of bacterial DNA sequences amplified by PCR from environmental samples (19, 23). The method relies on
variation in the position of restriction sites among sequences and
determination of the length of fluorescently labeled TRFs by
high-resolution gel electrophoresis on automated DNA sequencers
(1, 16). The method's many strengths include speed and
high sample throughput, which enables replicated experiments with
statistical analysis to be conducted. Highly precise fragment length
determination is achieved by use of an automated DNA sequencer with
internal size standards in every profile and provides numerical data of exceptional resolution. In theory, data from the method can also be
compared with data predicted from rapidly expanding sequence databases
in order to infer the potential composition of a sample.
The TRF method has been used successfully for differentiation of
bacterial communities in marine samples (20), the
digestive tracts of fish (24), soil samples (3, 6,
7, 21), and enrichment cultures over time (4, 5,
14) and for differentiation of wastewater treatment plant
sludge, laboratory bioreactor, aquifer sand, groundwater, and termite
gut communities (16). It has also been compared with
denaturing gradient gel electrophoresis (DGGE) and 16S rRNA gene (rDNA)
cloning for its effectiveness and consistency in differentiating
microbial communities (7, 20, 23). Given the increasing
popularity of the TRF method as a tool for community analysis,
widespread use of the method in field ecology studies is anticipated.
However, while the method is easy to perform, analysis of the data
generated is still in a developmental stage, with several technical and
theoretical issues yet to be addressed. At this juncture, a thorough
understanding of the strengths and current limitations of the method is
essential so that we can correctly assess the value of TRF analysis,
improve its capabilities, and interpret the results properly.
The TRF method could conceivably be used in three ways for analysis of
microbial communities. TRF profiles could be used for differentiation
of communities, for comparison of the relative phylotype richness and
structure of communities, and for identifying specific organisms in a
community. The method's robust ability to differentiate microbial
communities has been validated (7, 20). However, for
highly complex soil bacterial communities, the method has been shown to
be ineffective in assessing relative phylotype richness and structure
(7). In the present study, we extended the work of
previous studies (16, 19) by providing a more thorough and
detailed evaluation of the capacity for inferring the phylogenetic
composition of a community from TRF profiles. This was done in part by
examining the phylogenetic relationship of every group of 16S rDNA
sequences that yielded the same TRF size. We also addressed two
critical questions in data analysis: how reproducible are TRF profiles,
and how can TRF data be analyzed such that samples of equal size are
compared? In theory, replicate profiles (i.e., aliquots of a single
restriction digest) should be identical, but in practice, we found a
substantial amount of variability that could lead to erroneous
conclusions if unreplicated profiles were used to compare microbial
communities in different environmental samples. Here we present
strategies for increasing the utility of TRF profiles for phylogenetic
inference and present a data analysis method that improves the
reliability of TRF data for differentiation of communities. The
analysis techniques can also be used with other methods (e.g., DGGE and
amplified fragment length polymorphism) that generate DNA profiles of
microbial communities or microbial genomes.
| |
MATERIALS AND METHODS |
Prediction of TRFs from 16S rDNA sequences.
TRF data were
obtained primarily from version 7.1 (v7.1) (18) of the
Ribosomal Database Project (RDP), with key updates based on the RDP
v8.0 (17). A set of 2,156 aligned small-subunit (SSU) rRNA
sequences were obtained from the RDP v7.1 (18) for analysis. The sequences represented bacteria from the gram-positive and
Proteobacteria divisions only and matched the 3'-terminal 10 nucleotides of PCR primer 8-27f (pA; 5'-AGAGTTTGATCCTGGCTCAG [9] according to the criterion that the sequences
had 100% identity with the 3'-terminal 3 nucleotides of primer 8-27f
and one or no mismatches with the remaining 7 nucleotides. From the set
of 2,156 sequences, 129 sequences with suspicious sequence gaps were
eliminated. Suspicious gaps were defined as gaps of three or more
nucleotides that were phylogenetically random (i.e., appearing in the
sequence of one strain of a species but no other strains of the same
species) or that occurred in regions of the SSU rRNA gene known to be
highly conserved among bacteria. To maximize the number of sequences
available for TRF analysis, the final set of 2,027 sequences was not
matched with a reverse PCR primer. Many partial sequences that can be
used for prediction of TRFs are too short to be matched against both
forward and reverse PCR primers. TRFs were predicted for the enzymes
HaeIII, BstUI, HhaI, MspI,
and RsaI by identifying restriction site positions with the
program Patscan (22). The TAP-TRFLP function from the RDP
was used to update a subset of the data based on the new release (v8.0)
(17) of the RDP in order to demonstrate the impact of the
larger sequence database on TRF analysis.
Soil sample.
Soil was collected in 1994 from a site in the
Coconino National Forest, Arizona (15). The site is a
pinyon pine-juniper woodland with light sandy loam soil
(11), and the interspaces (areas between widely spaced
trees) are sparsely covered with grass and forb species. A composite
soil sample for the interspaces was collected by combining 10 subsamples collected at a depth of 10 to 15 cm from different locations
(15).
DNA extraction.
DNA was extracted from the soil sample using
a four-step procedure that included three cycles of freeze-thaw,
incubation at 70°C with sodium dodecyl sulfate, bead mill
homogenization, and ethanol precipitation as described previously
(15). Precipitated DNA was cleaned by phenol-chloroform
extraction. The DNA was stored frozen at 
20°C, then further
purified by passage through Sephadex G-200 spin columns, and
precipitated with ethanol for use in this study.
TRF profiles.
16S rDNA for TRF analysis was amplified with
primer 8-27f fluorescently labeled with 6'-carboxyfluorescein (ABI,
Perkin-Elmer, Foster City, Calif.) and with primer 1507-1492r
(5'-TACCTTGTTACGACTT [25]). Each 50-µl
reaction mixture contained 30 mM Tris (pH 8.4), 50 mM KCl, 1.5 mM
MgCl2, 50 µM each deoxynucleoside triphosphate, 50 pmol
of each primer, and 0.75 U of Taq polymerase (AmpliTaq LD;
Perkin-Elmer). Cycling conditions were as follows: 2 min of denaturation at 94°C, 35 cycles of 30 s at 50°C, 1 min at
72°C, and 10 s at 94°C, and a final cycle of annealing at
55°C for 1 min and extension at 72°C for 5 min. Three independent
50-µl PCRs were performed for each sample; the products were combined
and purified with a Qiaquick PCR cleanup kit (Qiagen, Inc., Chatsworth, Calif.). Approximately 50 ng of purified 16S rDNA was digested in a
20-µl reaction volume with 5 U of RsaI for 3 h.
Following restriction digestion, the DNA was passed through a Sephadex
G-200 Centrisep column (Princeton Separations Inc., Princeton, N.J.) for purification.
Nine replicate TRF profiles were obtained from the digested DNA by
loading three aliquots of the digested DNA on each of three separate
polyacrylamide gels. Replication at this level was performed to measure
the degree of variation in TRF profiles arising solely as a result of
experimental error during electrophoresis of digested DNA samples. For
each gel, three 1-µl aliquots of the digested DNA were dried,
suspended in 1.75 µl of loading buffer (0.25 µl of Genescan 2500 TAMRA size standard [ABI], 1.25 µl of deionized formamide, and 0.25 µl of a 3% [wt/vol] blue dextran-25 mM EDTA solution), denatured
at 94°C for 2 min, and placed immediately on ice for 2 min. The
aliquots were electrophoresed in denaturing 4% polyacrylamide gels
with an ABI 377 DNA sequencer. Between runs, the stock of digested DNA
was stored frozen at 20°C. Reagents for polyacrylamide gel
electrophoresis were purchased from Bio-Rad (Hercules, Calif.).
Terminal restriction fragment sizes between 94 and 827 bp with peak
heights of 
25 fluorescence units were determined using Genescan
analytical software v2.02 (ABI).
Analysis of TRF profiles.
A five-step analysis procedure for
comparison of TRF profiles was performed as follows, using
S+ v3.2 (MathSoft, Inc., Seattle, Wash.).
(i) Alignment of replicate profiles.
A clustering algorithm
was used to identify synonymous fragment sizes in replicate profiles
and to align the profiles. Genescan analysis software calculates DNA
fragment sizes to 1/100 of a base pair. The resulting values cannot
simply be rounded up or down to the nearest integer value because in
replicate profiles the measured value of a fragment size may float
sometimes above and sometimes below the median of two integers (e.g.,
133.38 and 133.53 bp). In this example, comparison of numerical values
rounded to the nearest integer would incorrectly suggest the presence of two fragments of distinct sizes. Clustering values that fit within
empirically determined margins of error circumvents this problem. The
error in determining fragment sizes with our ABI 377 automated DNA
sequencer was less than 0.5 bp, and typically the error was less than
0.2 bp. Therefore, TRFs that differed by less than 0.5 bp in different
profiles were considered identical and were clustered. All fragments
within a cluster were assigned the average of the sizes within the
cluster. In some cases, distinct peaks differing by 0.5 bp or less
occur in a single profile and can be reproducibly resolved, suggesting
the presence of at least two DNA fragments that either differ in
sequence composition or differ in length but migrate close together. In
an attempt to avoid clustering such fragments among a set of profiles,
the maximum number of fragments assigned to a cluster was limited to
the number of profiles being aligned. Thus, as soon as a cluster is
filled with the maximum number of fragments with the smallest
differences in measured lengths, a new cluster is created.
(ii) Standardization of DNA quantity between replicate
profiles.
The sum of all peak heights of 25 fluorescence units
(i.e., the total fluorescence; 25 fluorescence units is the baseline noise threshold) in each replicate profile was calculated as an indication of the total DNA quantity represented by each profile. DNA
quantity was standardized between replicate profiles to the smallest
quantity by proportionally reducing the height of each peak in larger
profiles. To accomplish this, the proportion of the smallest DNA
quantity (i.e., total fluorescence) and a larger DNA quantity was
calculated and used as a correction factor to adjust each peak height
in the profile representing the larger DNA quantity. For example, given
two profiles with total fluorescence values of 24,000 and 40,000, respectively, each peak in the latter profile would be multiplied by a
correction factor of 0.6 (i.e., the quotient of 24,000/40,000). This
procedure often eliminated peaks from larger profiles by reducing some
peak heights below the baseline noise threshold (25 fluorescence
units). Therefore, after adjustment of a profile, the new sum of peak
heights of 25 fluorescence units was calculated, and the
standardization procedure was repeated until, by iteration, the DNA
quantity (i.e., total fluorescence) of the larger profile was equal to
the quantity of the smaller profile.
In rare cases, the total DNA quantity represented by a larger profile
cannot be made exactly equal to the quantity of the
smaller profile. In
these cases, the total DNA quantity of the
larger profile fluctuates
between a value above and a value below
the quantity of the smaller
profile in successive iterations of
the standardization routine. This
occurs when one or more peaks
fall below the noise threshold (25 fluorescence units) in one
iteration, resulting in a total DNA quantity
less than that in
the smaller profile, and then rise above the
threshold in the
next iteration, resulting in a total quantity greater
than that
in the smaller profile. In these cases, the average of the
two
iterations is calculated in order to make the larger profile as
close to the small profile as
possible.
(iii) Creation of a derivative, reproducible sample profile.
For each sample, a derivative profile containing only the most
conservative and reliable TRF information was created by identifying the subset of TRFs that appeared in all replicate profiles of a sample.
Irreproducible TRFs (i.e., fragments observed in less than 100% of the
replicate profiles of a sample) were discarded. The average peak height
(abundance) of each reproducible TRF from a sample was calculated from
the peak heights observed in individual replicates. The resulting list
of TRFs and the average height of each TRF were used as the derivative
sample profile.
(iv) Standardization of DNA quantity between different
environmental samples.
To compare different samples, the
derivative profiles for a set of samples were standardized as described
in step iii for replicate profiles. In brief, the sum of all peak
heights of 25 fluorescence units in each derivative sample profile
was calculated as an indication of the total DNA quantity represented
by the profile. The DNA quantities for a set of samples were then
standardized using the iterative standardization procedure described above.
(v) Alignment of standardized, derivative sample profiles.
Following standardization, derivative sample profiles were aligned as
described in step i for replicate profiles. The average size of TRFs in
each alignment cluster was calculated to produce a single, composite
list of the TRF sizes found among all samples. By comparison of each
average sample profile with the composite list, a binary vector was
constructed for each sample representing the presence or absence of the
TRFs in the composite list.
(vi) Comparison of binary sample profiles.
The Jaccard
coefficient was used as a measure of similarity of binary vectors, and
a matrix of pairwise comparisons was constructed (13). The
Jaccard coefficient was used for the binary data because it describes
the similarity of each sample pair based only on TRFs that are present
in one or both samples (in other words, TRFs that are not present in
either of two samples being compared do not contribute to the
similarity of two samples). Agglomerative hierarchical clustering was
performed using the similarity matrix of Jaccard coefficients and the
unweighted pair-group average method and was displayed as dendrograms
(13).
| |
RESULTS AND DISCUSSION |
Inference of phylogenetic composition of natural samples.
The
ability to use a TRF profile to infer the phylogenetic composition of a
sample depends greatly on two factors: the phylogenetic resolution of
TRFs (i.e., the similarity of all organisms that can produce a given
TRF size) and the number and quality of reference sequences available
for comparative analysis. In previous studies, the capacity for TRFs to
discriminate among sequences has been summarized either by reporting
the maximum number of sequences predicted to generate the same TRF size
(i.e., the maximum redundancy) (16, 19) or by reporting
the fraction of sequences represented by the five most redundant TRFs
(19). Both measures are partial indications of the amount
of skew in the distribution of sequences among a set of predicted TRF
sizes. However, these measures provide information for only one or a
few TRFs (the least informative TRFs) in a distribution and provide no
general information about the phylogenetic relationships between
sequences that yield the same TRF size. As a result, one might
mistakenly conclude that an enzyme which yields a set of predicted TRFs
with relatively low redundancy might be useful for inferring some of
the phylogenetic composition of a community.
In this study, we have attempted to provide more detailed information
for assessing the use of TRF profiles for phylogenetic
inference.
Toward this end, we evaluated not only the frequency
distribution of
sequences among predicted TRF sizes as others
have done but also the
phylogenetic information that could be
derived from each predicted TRF
size. Fragment sizes that represented
strains from three or fewer
species were counted as species-specific
TRFs, and fragment sizes that
represented strains from two or
fewer genera were counted as
genus-specific TRFs. These arbitrary
criteria for describing
phylogenetic specificity were chosen as
a compromise. For any given
enzyme, extremely few TRFs are truly
specific for a single species
(i.e., representing numerous strains
of a single species) or members of
a single genus. Therefore,
TRF specificity was evaluated by using
groups that are larger
than a single species or genus but are small
enough to be informative
for comparative community
diversity.
The phylogenetic specificities of TRFs predicted for five different
enzymes are summarized in Table
1. The
sequences represented
370 named genera and 1,288 named species. The
importance of strategically
choosing restriction enzymes to give an
optimal distribution of
TRFs has been discussed previously (
16,
19); however, the
data in the present study provide more
detailed indications of
the phylogenetic specificity that can be
achieved with different
enzymes. The data also demonstrate that
combining two enzymes
in a single digest does not significantly improve
the utility
of TRF profiles. For example, the total number of TRFs
predicted
from
Proteobacteria sequences is not significantly
increased by
double digestion compared to the results achieved with
single
enzyme digests. Whereas 148
RsaI TRFs (between 94 and
827 bp)
were predicted from
Proteobacteria sequences, only 9 and 17 additional
TRFs were produced by combining either
HhaI or
MspI with
RsaI,
respectively.
Combining
RsaI with
HaeIII resulted in a decrease
in the number of TRFs in the analysis range (94 to 827 bp) compared
to
the number that could be achieved with
RsaI alone. The data
also show that fewer phylogenetically informative TRFs are obtained
from double digestion than by combining results from separate
single
enzyme digests. Whereas 116 species or genus-specific TRFs
occurred in
the profile derived from
MspI and
RsaI double
digestion
of the
Proteobacteria sequences, a total of 217 informative TRFs
were obtained by combining the data from separate
MspI and
RsaI
digests. These data demonstrate
that use of combinations of single
enzyme digests will typically be the
best strategy for general
profiling of bacterial communities and for
phylogenetic inference.
As shown in Table
1, the fraction of phylogenetically informative TRFs
that are theoretically possible in a TRF profile can
often be quite
high. For example, with the enzymes
HhaI,
MspI,
and
RsaI, the fraction of species-specific TRFs predicted
from
gram-positive and
Proteobacteria sequences combined
ranged from
61 to 68%. For each enzyme, the fraction of
phylogenetically informative
TRFs was increased to 70% by including
genus-specific TRFs, demonstrating
that the criteria used to define
phylogenetic specificity can
alter the perceived utility of TRF
profiles for phylogenetic inference.
Relaxing the definition of
specificity to include TRFs that represent
members of more than two
genera but belong to the same phylogenetic
subgroup or assemblage would
increase further the percentage of
informative TRFs (data not shown),
although the utility of this
type of information in community analysis
is
questionable.
The fraction of phylogenetically informative TRFs was highest when TRFs
were predicted from sequences representing a single
bacterial division.
For example, 77% of
RsaI TRFs were species
or genus
specific when predicted from
Proteobacteria sequences
only
or from gram-positive sequences only. Combining the two divisions
decreased the fraction of phylogenetically specific TRFs to 70%.
The
total number of informative TRFs that could be derived from
the
sequences also decreased. A total of 257 informative TRFs
could be
derived by analyzing the divisions separately, while
only 192 occurred
when the divisions were combined. The extent
of division level
diversity observed thus far in natural environments
is typically much
higher than two divisions. For example, 9 divisions
were identified in
an anaerobic digestor (
10), an average of
16 divisions
were identified in soil from two different sites
in Arizona (J. Dunbar,
S. M. Barns, J. A. Davis, G. Fisher, and
C. R. Kuske,
unpublished data), and 25 divisions were detected
in a Yellowstone hot
spring sample (
12). Thus, the average phylogenetic
resolution of TRFs in general profiles of natural environments
may be
so low that phylogenetic inference of community composition
is not
feasible.
In practice, the number of phylogenetically informative TRFs that are
possible in a profile will be lower than the number
predicted from
sequence databases. Since the migration of fragments
in polyacrylamide
gels is influenced to some extent by sequence
composition,
discrepancies may occur between observed and predicted
fragment sizes.
Discrepancies have been noted between predicted
and observed fragment
sizes (
6,
16,
20). Due to the inaccuracy
of fragment size
measurements from gels, observed fragment sizes
must be matched with a
bin of predicted, contiguous fragment sizes.
Thus, although each
individual fragment size in a bin of predicted
TRFs may be
phylogenetically specific, the range of organisms
represented by all
TRFs in the bin may be quite broad. Applying
a bin size of ±1 bp
(i.e., each bin contains three consecutive
fragment sizes such as 350, 351, and 352 bp) to the TRFs predicted
from either the
Proteobacteria sequences or gram-positive sequences
reduced
the fraction of species- or genus-specific
RsaI TRFs from
approximately 77% (Table
1) to 53 or 59%, respectively. These
data
underscore the importance of amplifying sequence mixtures
from single
bacterial divisions or smaller groups, since even
more substantial
reductions in bin specificity occur when TRFs
are derived
simultaneously from multiple
divisions.
The predicted fraction of phylogenetically informative TRFs and TRF
bins may decrease even further as the variety of sequences
available
for analysis increases. For example, the fraction of
phylogenetically
informative TRFs in profiles predicted from 1,007
Proteobacteria or gram-positive division sequences (analyzed
separately)
from the RDP v6.0 decreased from an average of 80 to 77%
after
adding 482 sequences from the RDP v7.1 (1,489 sequences analyzed
in total). With the RDP v8.0, only 71% of TRFs predicted from
3,908 aligned
Proteobacteria or gram-positive division sequences
were phylogenetically specific. The fraction of informative TRFs
from
these two bacterial divisions is clearly decreasing as the
number and
variety of reference sequences increase. For TRF profiles
obtained
using universal 16S rDNA primers, even greater impacts
on predicted TRF
specificity can be anticipated as the variety
of sequences in the
eubacterial domain
expands.
If phylogenetic inference from TRF profiles is confined (as it should
be) to profiles created with primers specific for a
single division or
smaller group, the fraction of bacterial communities
that could be
studied is presently quite small. The RDP v8.0 recognizes
30 major
eubacterial divisions but is severely limited in coverage
of these
divisions. For example, the aligned sequences that reasonably
match
primer 8-27f represent 20 of the 30 major divisions, but
the
Proteobacteria, gram-positive, and
Flexibacter-Cytophaga-Bacteroides divisions account for 90%
(45, 39, and 7%, respectively) of the
sequences, while the remaining
17 divisions are represented by
an average of 26 sequences each (Table
2). Natural environments
may often be
dominated by microbes belonging to divisions other
than the
Proteobacteria and gram-positive bacteria. For example,
16S
rDNA sequences representing members of the
Acidobacterium division were the most common sequences amplified from four different
soils (including the one used in this study) from Arizona (
8,
15; Dunbar et al., unpublished). Members of the
Acidobacterium division accounted for 49% of 766 16S rDNA
sequences obtained
from the four Arizona soils, while
Proteobacteria and gram-positive
sequences accounted for
only 17 and 6%, respectively (Dunbar et
al., unpublished). Primers for
amplification of 16S rDNA sequences
specifically from members of the
Acidobacterium division are available
(
2).
However, attempts to infer the composition of this fraction
(the
largest fraction) of the community are clearly constrained
by the
limited supply of reference sequences currently available
in sequence
databases. Although this situation will certainly
improve over time, at
present the use of 16S rDNA clone libraries
in conjunction with the TRF
method will provide the most reliable
phylogenetic information from
microbial communities (
4,
5,
14,
19,
24).
Reproducibility of TRF profiles.
In previous studies, either
TRF profiles of different samples were not replicated or the data and
analysis methods were not described in sufficient detail to permit
evaluation of the reproducibility of TRF profiles (6, 16, 19, 20,
24). Thus, the general quality of TRF profiles as reliable
fingerprints of microbial communities is unknown. We examined the
reproducibility of replicate TRF profiles by comparing the total
quantity of DNA represented by different profiles, the number of TRFs
in each profile, and the height (abundance) of individual TRFs in each
profile. The quantity of DNA represented by each of nine replicate
RsaI TRF profiles was determined by calculating the sum of
peak heights in each profile. Although the profiles were produced by
using aliquots of a single RsaI digest of 16S rDNA, the
total fluorescence (representational DNA quantity) measured from each
TRF profile by summing the heights of individual peaks varied by a
factor of 2 and ranged from 14,845 to 35,207 fluorescent units. Similar variation was observed with the sum of peak areas for each profile (data not shown), demonstrating that choice of variable (height versus
area) was unimportant in assessing variation in total fluorescence between replicates. Even when extreme care is taken to handle DNA
samples uniformly, variation in the total fluorescence between replicates can arise routinely from small pipetting errors when aliquots are withdrawn from a restriction digest and loaded on a gel.
Variation in the relative abundance (fluorescence) of individual fragments can also be introduced during use of Genescan analysis software to track gel lanes and extract fragment abundance data (peak
area and peak height data) from each lane. A similar degree of
variation probably occurs in other DNA fingerprinting methods but has
not been examined. Variation among replicate TRF profiles (in
comparison to profiles from other methods) is especially apparent because of the uniquely high detection sensitivity, fragment
resolution, precision in fragment sizing, and numerical data obtained
with the TRF method.
To reduce artifacts arising from variation in DNA quantity between
replicate profiles, a procedure was developed to standardize
DNA
quantities after data collection. Effects of the standardization
procedure on the number and average height of peaks in replicate
profiles are shown in Fig.
1 and
2. Prior to standardization,
the
number of TRFs ranged from 42 to 96 (median = 76 [Fig.
1]),
with
an average variation in peak number (i.e., the average of
pairwise
comparisons with the smallest profile having only 42
TRFs) of 71%.
Following standardization, the number of TRFs in
the replicate profiles
ranged from 42 to 79 (median = 50), with
an average variation of
26%. Figure
2 shows the standard deviation
(SD) of the average height
of 24 separate TRFs consistently detected
in nine replicate profiles.
The average SD prior to standardization
was 336 (range = 13 to
1,400), whereas after standardization it
was 77 (range = 4 to
254). On average, the SD of each peak height
was reduced from 24% of
the mean peak height to 19% of the mean.
For replicate profiles run on
the same gel, standardization reduced
the SD of each peak height from
an average of 13% of the mean
(a value similar to that reported by
others [
21]) to 7% of the
mean. These data demonstrate
that although the standardization
procedure does not eliminate all of
the variation between replicate
profiles, it significantly reduces
variation both in the number
of TRFs observed in each profile and in
the height of individual
peaks among replicate profiles.
View larger version (34K):
[in this window]
[in a new window]
|
FIG. 1.
Effects of fluorescence standardization on variation in
the number of TRFs observed in each of nine replicate RsaI
TRF profiles of 16S rDNA amplified from a single soil DNA sample.
|
|
View larger version (19K):
[in this window]
[in a new window]
|
FIG. 2.
Effects of fluorescence standardization on variation in
the height of TRFs observed in nine replicate RsaI TRF
profiles of 16S rDNA amplified from a soil DNA sample. Each bar
represents the SD of the mean height of a given peak.
|
|
Reproducibility of the nine replicate profiles is illustrated in Fig.
3. Although a total of 169 distinct TRF
sizes were observed
among the nine profiles combined, only 24 TRF sizes
were consistently
detected in all profiles. Standardization of the DNA
quantities
represented by each profile reduced the total number of
distinct
TRFs from 169 to 132 but did not alter the number of TRFs that
were consistently detected. This degree of noise was unexpected
and
arose mostly from the presence of small peaks, 90% of which
had
fluorescence values (after standardization) between 25 and
67 units.
The 24 reproducible peaks had relatively high fluorescence
values
(median = 299) compared to the irreproducible peaks (median
= 35), with all but one reproducible peak having a fluorescence
value of
77 units or greater. The small, irreproducible peaks
arise from unknown
components of the DNA samples loaded on the
sequencing gels, not from
background noise from gel components.
Blank gel lanes containing size
standards but no sample DNA have
maximum noise spikes of approximately
15 fluorescence units, which
is significantly less than the height of
the irreproducible peaks
routinely observed in our sample profiles.
Since the set of reproducible
peaks in a profile cannot be clearly (or
reliably) distinguished
from the set of irreproducible peaks by a
simple height threshold,
the reproducible peaks must be identified by
comparison of at
least two or three replicate profiles.
View larger version (14K):
[in this window]
[in a new window]
|
FIG. 3.
Composite profile showing the average abundance of all
the TRFs (169 in total) observed among nine replicate, standardized
RsaI TRF profiles of 16S rDNA amplified from a soil DNA
sample. The values along the x axis indicate the number of
replicates in which each TRF was observed. Error bars are SDs of the
mean height of each TRF.
|
|
Reproducibility of TRF profiles has been rigorously examined in one
other published study. Osborn et al. (
21) carefully
and
systematically examined the influence of many different experimental
factors (including, for example, template concentration, PCR cycle
number, and different brands of
Taq polymerase) on the
reproducibility
of TRF profiles. In contrast to our results, the
authors found
that replicate profiles from the same sample DNA were
almost identical,
and only one or two irreproducible peaks were
detected. The dramatic
difference in reproducibility between studies
arises largely from
the use of different baseline noise thresholds
(i.e., the value
defining which fluorescence data collected by an ABI
sequencer
will be retained for user analysis and which fluorescence
data
will be discarded as noise). Osborn et al. (
21) used
a threshold
value of 100 fluorescence units, whereas we used 25 fluorescence
units. Applying a threshold of 100 to our data reduces the
number
of irreproducible peaks from 169 (prestandardization) to 13. However,
the higher threshold also eliminated 4 of the 24 reproducible
peaks. Although use of a high threshold value can eliminate much
of the
noise in TRF profiles, the threshold value required to
eliminate a
standard percentage of noise from a set of profiles
cannot be predicted
a priori and can vary from one sample to the
next (data not shown). For
these reasons, we prefer the use of
the lowest possible threshold and
comparison of replicate profiles
to distinguish irreproducible data
from reproducible
data.
Identification of reproducible TRFs between replicates is required to
obtain a reliable and representative TRF profile for
a sample. After
reliable profiles for a set of samples are obtained,
standardization of
sample quantities is necessary to enable comparisons
that are based on
samples of equal size. Even if care is taken
to standardize DNA sample
quantities prior to TRF analysis, the
small errors that can accumulate
during processing of samples
for TRF analysis can result in different
quantities of DNA being
represented in TRF profiles. This problem is
overcome by applying
a procedure that can standardize sample quantities
after data
collection. Figure
4 shows the
effects of standardization on three
theoretical samples. When sample
sizes are unequal, similarity
relationships among samples can be
severely distorted. Samples
1 and 2 in Fig.
4A have identical TRF
profiles except that sample
2 has two additional TRFs because a larger
quantity of DNA was
examined. Standardization of sample size eliminates
spurious comparisons
and can reveal more accurate relationships among
samples (Fig.
4B). Although standardization of sample size may not
always alter
the general topology of similarity dendrograms, it can
routinely
alter the branch lengths of such trees. Of course,
standardization
of sample sizes must be used sensibly. If one sample in
a set
of samples being compared is represented by an extremely low
quantity
of DNA compared to the other samples, standardization of
sample
quantities across the set may result in the loss of a large
amount
of data and distortion of sample relationships. Samples that are
represented by large DNA quantities and that are different from
one
another may appear to be similar if much of the information
in their
profiles is lost during standardization to a low DNA
quantity. In this
circumstance, standardization of sample pairs
during pairwise
comparisons may be more appropriate than standardization
of DNA
quantities across an entire sample set.
View larger version (15K):
[in this window]
[in a new window]
|
FIG. 4.
Dendrograms showing the similarity of three theoretical
samples before and after standardization of the sample profiles. The
aligned TRF profile for each sample is shown in parentheses and lists
the peak heights for seven theoretical TRFs.
|
|
The significance of the TRF method for microbial ecologists is
indicated in part by the integration of TRF profile analysis
programs
into the RDP. With such centralized support, widespread
use of the TRF
method in microbial ecology studies is anticipated.
The limitations and
technical details of the method should be
clearly understood in order
to strengthen the method where possible
and to wisely interpret data
generated by the method. Toward this
end, we identified limitations
underlying the use of TRF profiles
and have presented procedures that
reduce the impact of these
limitations. Application of the analytical
procedures outlined
in this study should strengthen the use of the TRF
method for
inferring the phylogenetic composition of environmental
samples
and for differentiation of microbial communities. The
analytical
techniques presented here are a necessary first step toward
incorporating
peak height as an additional parameter for profile
comparisons.
The data analysis procedures can also be applied to
profiles created
by other methods such as DGGE, rapid amplified
polymorphic DNA,
community restriction fragment polymorphism, or
amplified fragment
length polymorphism
analysis.
| |
ACKNOWLEDGMENTS |
This research was supported by grants from the DOE Chemical and
Biological Nonproliferation program, DOE Program for Ecosystem Research, and the Los Alamos National Laboratory.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: Environmental
Molecular Biology Group, M888, Biosciences Division, Los Alamos
National Laboratory, Los Alamos, NM 87545. Phone: (505) 665-5749. Fax: (505) 665-6894. E-mail: dunbar{at}lanl.gov.
| |
REFERENCES |
| 1.
|
Avaniss-Aghajani, E.,
K. Jones,
D. Chapman, and C. Brunk.
1994.
A molecular technique for identification of bacteria using small subunit ribosomal RNA sequences.
BioTechniques
17:144-149[Medline].
|
| 2.
|
Barns, S. M.,
S. L. Takala, and C. R. Kuske.
1999.
Wide distribution and diversity of members of the bacterial kingdom Acidobacterium in the environment.
Appl. Environ. Microbiol.
65:1731-1737[Abstract/Free Full Text].
|
| 3.
|
Bruce, K. D.
1997.
Analysis of mer gene subclasses within bacterial communities in soils and sediments resolved by fluorescent-PCR-restriction fragment length polymorphism profiling.
Appl. Environ. Microbiol.
63:4914-4919[Abstract].
|
| 4.
|
Chin, K. J.,
T. Lukow, and R. Conrad.
1999.
Effect of temperature on structure and function of the methanogenic archaeal community in an anoxic rice field soil.
Appl. Environ. Microbiol.
65:2341-2349[Abstract/Free Full Text].
|
| 5.
|
Chin, K. J.,
T. Lukow,
S. Stubner, and R. Conrad.
1999.
Structure and function of the methanogenic archaeal community in stable cellulose-degrading enrichment cultures at two different temperatures (15° and 30°C).
FEMS Microbiol. Ecol.
30:313-326[Medline].
|
| 6.
|
Clement, B. G.,
L. E. Kehl,
K. L. DeBord, and C. L. Kitts.
1998.
Terminal restriction fragment patterns (TRFPs), a rapid, PCR-based method for the comparison of complex bacterial communities.
J. Microbiol. Methods
31:135-142.
|
| 7.
|
Dunbar, J.,
L. O. Ticknor, and C. R. Kuske.
2000.
Assessment of microbial diversity in two southwestern U.S. soils by terminal restriction fragment analysis.
Appl. Environ. Microbiol.
66:2943-2950[Abstract/Free Full Text].
|
| 8.
|
Dunbar, J.,
S. Takala,
S. M. Barns,
J. A. Davis, and C. R. Kuske.
1999.
Levels of bacterial community diversity in four arid soils compared by cultivation and 16S rRNA gene cloning.
Appl. Environ. Microbiol.
65:1662-1669[Abstract/Free Full Text].
|
| 9.
|
Edwards, U.,
T. Rogall,
H. Blocker,
M. Emde, and E. C. Bottger.
1989.
Isolation and direct complete determination of entire genes.
Nucleic Acids Res.
17:7843-7853[Abstract/Free Full Text].
|
| 10.
|
Godon, J.,
E. Zumstein,
P. Dabert,
F. Habouzit, and R. Moletta.
1997.
Molecular microbial diversity of an anaerobic digestor as determined by small-subunit rDNA sequence analysis.
Appl. Environ. Microbiol.
63:2802-2813[Abstract].
|
| 11.
|
Hendricks, D. M.
1985.
Arizona soils.
University of Arizona Press, Tucson, Ariz.
|
| 12.
|
Hugenholtz, P.,
C. Pitulle,
K. L. Hershberger, and N. R. Pace.
1998.
Novel division level bacterial diversity in a Yellowstone hot spring.
J. Bacteriol.
180:366-376[Abstract/Free Full Text].
|
| 13.
|
Kaufman, L., and P. J. Rousseeuw.
1990.
Finding groups in data: an introduction to cluster analysis.
John Wiley & Sons, Inc., New York, N.Y.
|
| 14.
|
Knight, V. K.,
L. J. Kerkhof, and M. M. Häggblom.
1999.
Community analyses of sulfidogenic 2-bromophenol dehalogenating and phenol-degrading microbial consortia.
FEMS Microbiol. Ecol.
29:137-147[CrossRef].
|
| 15.
|
Kuske, C. R.,
S. M. Barns, and J. D. Busch.
1997.
Diverse uncultivated bacterial groups from soils of the arid southwestern United States that are present in many geographic regions.
Appl. Environ. Microbiol.
63:3614-3621[Abstract].
|
| 16.
|
Liu, W.,
T. L. Marsh,
H. Cheng, and L. J. Forney.
1997.
Characterization of microbial diversity by determining terminal restriction fragment length polymorphisms of genes encoding 16S rRNA.
Appl. Environ. Microbiol.
63:4516-4522[Abstract].
|
| 17.
|
Maidak, B. L.,
J. R. Cole,
T. G. Lilburn,
C. T. Parker, Jr.,
P. R. Saxman,
J. M. Stredwick,
G. M. Garrity,
B. Li,
G. J. Olsen,
S. Pramanik,
T. M. Schmidt, and J. M. Tiedje.
2000.
The RDP (Ribosomal Database Project) continues.
Nucleic Acids Res.
28:173-174[Abstract/Free Full Text].
|
| 18.
|
Maidak, B. L.,
J. R. Cole,
C. T. Parker,
G. M. Garrity,
N. Larsen,
L. Bing,
T. G. Lilburn,
M. J. McCaughey,
G. J. Olsen,
R. Overbeek,
S. Pramanik,
T. M. Schmidt,
J. M. Tiedje, and C. R. Woese.
1999.
A new version of the RDP (Ribosomal Database Project).
Nucleic Acids Res.
27:171-173[Abstract/Free Full Text].
|
| 19.
|
Marsh, T. L.
1999.
Terminal restriction fragment length polymorphism (T-RFLP): an emerging method for characterizing diversity among homologous populations of amplification products.
Curr. Opin. Microbiol.
2:323-327[CrossRef][Medline].
|
| 20.
|
Moeseneder, M. M.,
J. M. Arrieta,
G. Muyzer,
C. Winter, and G. J. Herndl.
1999.
Optimization of terminal-restriction fragment length polymorphism analysis for complex marine bacterioplankton communities and comparison with denaturing gradient gel electrophoresis.
Appl. Environ. Microbiol.
65:3518-3525[Abstract/Free Full Text].
|
| 21.
|
Osborn, A. M.,
E. R. B. Moore, and K. N. Timmis.
2000.
An evaluation of terminal-restriction fragment length polymorphism (T-RFLP) analysis for the study of microbial community structure and dynamics.
Environ. Microbiol.
2:39-50[CrossRef][Medline].
|
| 22.
|
Overbeek, R.
1996.
Scan for matches.
Argonne National Laboratory, Argonne, Ill.
|
| 23.
|
Tiedje, J. M.,
S. Asuming-Brempong,
K. Nusslein,
T. L. Marsh, and S. J. Flynn.
1999.
Opening the black box of soil microbial diversity.
Appl. Soil Ecol.
13:109-122[CrossRef].
|
| 24.
|
van der Maarel, M. J.,
R. R. Artz,
R. Haanstra, and L. J. Forney.
1998.
Association of marine Archaea with the digestive tracts of two marine fish species.
Appl. Environ. Microbiol.
64:2894-2898[Abstract/Free Full Text].
|
| 25.
|
Wilson, K. H.,
R. B. Blitchington, and R. C. Green.
1990.
Amplification of bacterial 16S ribosomal DNA with polymerase chain reaction.
J. Clin. Microbiol.
28:1942-1946[Abstract/Free Full Text].
|
Applied and Environmental Microbiology, January 2001, p. 190-197, Vol. 67, No. 1
0099-2240/01/$04.00+0 DOI: 10.1128/AEM.67.1.190-197.2001
This article has been cited by other articles:
-
Alvarado, P., Manjon, J. L.
(2009). Selection of Enzymes for Terminal Restriction Fragment Length Polymorphism Analysis of Fungal Internally Transcribed Spacer Sequences. Appl. Environ. Microbiol.
75: 4747-4752
[Abstract]
[Full Text]
-
Brugger, S. D., Hathaway, L. J., Muhlemann, K.
(2009). Detection of Streptococcus pneumoniae Strain Cocolonization in the Nasopharynx. J. Clin. Microbiol.
47: 1750-1756
[Abstract]
[Full Text]
-
Waldron, L. S., Ferrari, B. C., Gillings, M. R., Power, M. L.
(2009). Terminal Restriction Fragment Length Polymorphism for Identification of Cryptosporidium Species in Human Feces. Appl. Environ. Microbiol.
75: 108-112
[Abstract]
[Full Text]
-
Findlay, R. H., Yeates, C., Hullar, M. A. J., Stahl, D. A., Kaplan, L. A.
(2008). Biome-Level Biogeography of Streambed Microbiota. Appl. Environ. Microbiol.
74: 3014-3021
[Abstract]
[Full Text]
-
Torok, V. A., Ophel-Keller, K., Loo, M., Hughes, R. J.
(2008). Application of Methods for Identifying Broiler Chicken Gut Bacterial Species Linked with Increased Energy Metabolism. Appl. Environ. Microbiol.
74: 783-791
[Abstract]
[Full Text]
-
Blackwood, C. B., Hudleston, D., Zak, D. R., Buyer, J. S.
(2007). Interpreting Ecological Diversity Indices Applied to Terminal Restriction Fragment Length Polymorphism Data: Insights from Simulated Microbial Communities. Appl. Environ. Microbiol.
73: 5276-5283
[Abstract]
[Full Text]
-
Thies, F. L., Konig, W., Konig, B.
(2007). Rapid characterization of the normal and disturbed vaginal microbiota by application of 16S rRNA gene terminal RFLP fingerprinting. J Med Microbiol
56: 755-761
[Abstract]
[Full Text]
-
Buckley, D. H., Huangyutitham, V., Hsu, S.-F., Nelson, T. A.
(2007). Stable Isotope Probing with 15N Achieved by Disentangling the Effects of Genome G+C Content and Isotope Enrichment on DNA Density. Appl. Environ. Microbiol.
73: 3189-3195
[Abstract]
[Full Text]
-
Bent, S. J., Pierson, J. D., Forney, L. J., Danovaro, R., Luna, G. M., Dell'Anno, A., Pietrangeli, B.
(2007). Measuring Species Richness Based on Microbial Community Fingerprints: the Emperor Has No Clothes. Appl. Environ. Microbiol.
73: 2399-2401
[Full Text]
-
Bhatia, M., Sharp, M., Foght, J.
(2006). Distinct Bacterial Communities Exist beneath a High Arctic Polythermal Glacier. Appl. Environ. Microbiol.
72: 5838-5845
[Abstract]
[Full Text]
-
Danovaro, R., Luna, G. M., Dell'Anno, A., Pietrangeli, B.
(2006). Comparison of Two Fingerprinting Techniques, Terminal Restriction Fragment Length Polymorphism and Automated Ribosomal Intergenic Spacer Analysis, for Determination of Bacterial Diversity in Aquatic Environments. Appl. Environ. Microbiol.
72: 5982-5989
[Abstract]
[Full Text]
-
Nakano, Y., Takeshita, T., Yamashita, Y.
(2006). TRFMA: a web-based tool for terminal restriction fragment length polymorphism analysis based on molecular weight. Bioinformatics
22: 1788-1789
[Abstract]
[Full Text]
-
Osborne, C. A., Rees, G. N., Bernstein, Y., Janssen, P. H.
(2006). New Threshold and Confidence Estimates for Terminal Restriction Fragment Length Polymorphism Analysis of Complex Bacterial Communities. Appl. Environ. Microbiol.
72: 1270-1278
[Abstract]
[Full Text]
-
Mohanty, S. R., Bodelier, P. L. E., Floris, V., Conrad, R.
(2006). Differential Effects of Nitrogenous Fertilizers on Methane-Consuming Microbes in Rice Field and Forest Soils. Appl. Environ. Microbiol.
72: 1346-1354
[Abstract]
[Full Text]
-
Fierer, N., Jackson, R. B.
(2006). From the Cover: The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. USA
103: 626-631
[Abstract]
[Full Text]
-
Hongoh, Y., Deevong, P., Inoue, T., Moriya, S., Trakulnaleamsai, S., Ohkuma, M., Vongkaluang, C., Noparatnaraporn, N., Kudo, T.
(2005). Intra- and Interspecific Comparisons of Bacterial Diversity and Community Structure Support Coevolution of Gut Microbiota and Termite Host. Appl. Environ. Microbiol.
71: 6590-6599
[Abstract]
[Full Text]
-
Saikaly, P. E., Stroot, P. G., Oerther, D. B.
(2005). Use of 16S rRNA Gene Terminal Restriction Fragment Analysis To Assess the Impact of Solids Retention Time on the Bacterial Diversity of Activated Sludge. Appl. Environ. Microbiol.
71: 5814-5822
[Abstract]
[Full Text]
-
Fogarty, L. R., Voytek, M. A.
(2005). Comparison of Bacteroides-Prevotella 16S rRNA Genetic Markers for Fecal Samples from Different Animal Species. Appl. Environ. Microbiol.
71: 5999-6007
[Abstract]
[Full Text]
-
Castro, H., Newman, S., Reddy, K. R., Ogram, A.
(2005). Distribution and Stability of Sulfate-Reducing Prokaryotic and Hydrogenotrophic Methanogenic Assemblages in Nutrient-Impacted Regions of the Florida Everglades. Appl. Environ. Microbiol.
71: 2695-2704
[Abstract]
[Full Text]
-
Macbeth, T. W., Cummings, D. E., Spring, S., Petzke, L. M., Sorenson, K. S. Jr.
(2004). Molecular Characterization of a Dechlorinating Community Resulting from In Situ Biostimulation in a Trichloroethene-Contaminated Deep, Fractured Basalt Aquifer and Comparison to a Derivative Laboratory Culture. Appl. Environ. Microbiol.
70: 7329-7341
[Abstract]
[Full Text]
-
Hackl, E., Zechmeister-Boltenstern, S., Bodrossy, L., Sessitsch, A.
(2004). Comparison of Diversities and Compositions of Bacterial Populations Inhabiting Natural Forest Soils. Appl. Environ. Microbiol.
70: 5057-5065
[Abstract]
[Full Text]
-
Donovan, S. E., Purdy, K. J., Kane, M. D., Eggleton, P.
(2004). Comparison of Euryarchaea Strains in the Guts and Food-Soil of the Soil-Feeding Termite Cubitermes fungifaber across Different Soil Types. Appl. Environ. Microbiol.
70: 3884-3892
[Abstract]
[Full Text]
-
Idris, R., Trifonova, R., Puschenreiter, M., Wenzel, W. W., Sessitsch, A.
(2004). Bacterial Communities Associated with Flowering Plants of the Ni Hyperaccumulator Thlaspi goesingense. Appl. Environ. Microbiol.
70: 2667-2677
[Abstract]
[Full Text]
-
Broderick, N. A., Raffa, K. F., Goodman, R. M., Handelsman, J.
(2004). Census of the Bacterial Community of the Gypsy Moth Larval Midgut by Using Culturing and Culture-Independent Methods. Appl. Environ. Microbiol.
70: 293-300
[Abstract]
[Full Text]
-
Buchan, A., Newell, S. Y., Butler, M., Biers, E. J., Hollibaugh, J. T., Moran, M. A.
(2003). Dynamics of Bacterial and Fungal Communities on Decaying Salt Marsh Grass. Appl. Environ. Microbiol.
69: 6676-6687
[Abstract]
[Full Text]
-
Kent, A. D., Smith, D. J., Benson, B. J., Triplett, E. W.
(2003). Web-Based Phylogenetic Assignment Tool for Analysis of Terminal Restriction Fragment Length Polymorphism Profiles of Microbial Communities. Appl. Environ. Microbiol.
69: 6768-6776
[Abstract]
[Full Text]
-
Martiny, A. C., Jorgensen, T. M., Albrechtsen, H.-J., Arvin, E., Molin, S.
(2003). Long-Term Succession of Structure and Diversity of a Biofilm Formed in a Model Drinking Water Distribution System. Appl. Environ. Microbiol.
69: 6899-6907
[Abstract]
[Full Text]
-
Engebretson, J. J., Moyer, C. L.
(2003). Fidelity of Select Restriction Endonucleases in Determining Microbial Diversity by Terminal-Restriction Fragment Length Polymorphism. Appl. Environ. Microbiol.
69: 4823-4829
[Abstract]
[Full Text]
-
Merrill, L., Richardson, J., Kuske, C. R., Dunbar, J.
(2003). Fluorescent Heteroduplex Assay for Monitoring Bacillus anthracis and Close Relatives in Environmental Samples. Appl. Environ. Microbiol.
69: 3317-3326
[Abstract]
[Full Text]
-
Sait, L., Galic, M., Strugnell, R. A., Janssen, P. H.
(2003). Secretory Antibodies Do Not Affect the Composition of the Bacterial Microbiota in the Terminal Ileum of 10-Week-Old Mice. Appl. Environ. Microbiol.
69: 2100-2109
[Abstract]
[Full Text]
-
Blackwood, C. B., Marsh, T., Kim, S.-H., Paul, E. A.
(2003). Terminal Restriction Fragment Length Polymorphism Data Analysis for Quantitative Comparison of Microbial Communities. Appl. Environ. Microbiol.
69: 926-932
[Abstract]
[Full Text]
-
Lueders, T., Friedrich, M. W.
(2003). Evaluation of PCR Amplification Bias by Terminal Restriction Fragment Length Polymorphism Analysis of Small-Subunit rRNA and mcrA Genes by Using Defined Template Mixtures of Methanogenic Pure Cultures and Soil DNA Extracts. Appl. Environ. Microbiol.
69: 320-326
[Abstract]
[Full Text]
-
Kuske, C. R., Ticknor, L. O., Miller, M. E., Dunbar, J. M., Davis, J. A., Barns, S. M., Belnap, J.
(2002). Comparison of Soil Bacterial Communities in Rhizospheres of Three Plant Species and the Interspaces in an Arid Grassland. Appl. Environ. Microbiol.
68: 1854-1863
[Abstract]
[Full Text]
-
McSpadden Gardener, B. B., Weller, D. M.
(2001). Changes in Populations of Rhizosphere Bacteria Associated with Take-All Disease of Wheat. Appl. Environ. Microbiol.
67: 4414-4425
[Abstract]
[Full Text]
-
Sessitsch, A., Weilharter, A., Gerzabek, M. H., Kirchmann, H., Kandeler, E.
(2001). Microbial Population Structures in Soil Particle Size Fractions of a Long-Term Fertilizer Field Experiment. Appl. Environ. Microbiol.
67: 4215-4224
[Abstract]
[Full Text]
Top
Abstract
Introduction
