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Applied and Environmental Microbiology, April 2001, p. 1935-1939, Vol. 67, No. 4
0099-2240/01/$04.00+0 DOI: 10.1128/AEM.67.4.1935-1939.2001
Copyright © 2001, American Society for Microbiology. All rights reserved.
16S Ribosomal DNA Terminal Restriction Fragment Pattern Analysis
of Bacterial Communities in Feces of Rats Fed Lactobacillus
acidophilus NCFM
Christopher W.
Kaplan,1
Johanna C.
Astaire,1
Mary Ellen
Sanders,2
Bandaru S.
Reddy,3 and
Christopher L.
Kitts1,*
Environmental Biotechnology
Institute1 and Dairy Products Technology
Center,2 California Polytechnic State
University, San Luis Obispo, California 93407, and American
Health Foundation, Valhalla, New York 105953
Received 16 August 2000/Accepted 6 January 2001
 |
ABSTRACT |
16S ribosomal DNA terminal restriction fragment patterns from rat
fecal samples were analyzed to track the dynamics of
Lactobacillus acidophilus NCFM and discern bacterial
populations that changed during feeding with NCFM. Lactobacillus
johnsonii and Ruminococcus flavefaciens were
tentatively identified as such bacterial populations. The presence of
L. johnsonii was confirmed by isolation from feces.
| |
TEXT |
Many efforts to study microbial
communities in the gastrointestinal tract have focused on the large
bowel and consequently on fecal samples (9, 10, 17, 22, 23,
24). Several methods for following the complex communities in
the large bowel have been employed. However, because of the abundance
and diversity of bacteria in feces, this has proved a difficult task.
One method well suited for studying complex bacterial communities is
the analysis of terminal restriction fragment (TRF) patterns (also known as terminal restriction fragment length polymorphism analysis), which can provide a rapid and reproducible means observing bacterial population dynamics. The goal of this report was to explore a method
for analyzing TRF patterns that can be used to track the dynamics of
specific populations of bacteria in complex communities such as those
in feces.
TRF patterns are created by endonuclease digestion of DNA from a PCR
using one fluorescently labeled primer. Only the terminal restriction
fragments are visualized after electrophoretic separation on a DNA
sequencing machine. When the target of PCR is 16S ribosomal DNA (rDNA),
then the TRF pattern reflects the taxonomic diversity of the bacteria
in the sample (7, 11). This method was used to compare
bacterial communities from different environments with clear success
(7, 10, 11, 12, 13, 15, 20). Comparing TRF patterns taken
at different times can also monitor temporal changes in bacterial
community structure.
While TRF pattern analysis allows rapid monitoring of environments over
time and space, it does have drawbacks. The ability of TRF patterns to
accurately describe complex bacterial communities is complicated by
variations in the conserved 16S rDNA sequences commonly used as PCR
priming sites. Primer selection can dramatically alter the picture that
is presented in a TRF pattern, because only a fraction of the bacterial
16S rDNA sequences in a sample will be amplified. In this study we used
primers shown to hybridize well with 90% (46f) and 99% (536r) of the
~1,500 bacterial 16S rDNA sequences tested in a study by Brunk et al.
(5). In addition to the coverage provided by PCR primers,
there is the concern of TRF length overlap. Phylogenetically distant
bacteria might produce TRFs of different lengths when digested with one
endonuclease but result in TRFs of the same length when digested with a
different enzyme. Thus, a more complete picture of the bacterial
community is provided by an analysis of TRF patterns derived from
multiple-enzyme digests. The use of several enzyme-derived TRF patterns
can also provide data that allow the identification of bacteria
involved in population shifts during a study (2, 3, 16).
The samples for this study came from an experiment reported by Rao et
al. on colon carcinogenesis in rats fed L. acidophilus NCFM
(18). Rao et al. showed that dietary NCFM suppressed the formation of precancerous lesions in the colons of rats injected with
azoxymethane (18). The rats were injected with
azoxymethane at 7 weeks and sacrificed after reaching 16 weeks of age.
We received combined fecal samples collected at 7 and 16 weeks of age
from three groups of rats (one cage per group). Each sample was stored at 
70°C and consisted of pellets collected over a 24-h period from
each cage (three rats per cage). Each group was fed the same diet
except that the amount of NCFM was varied. Table
1 shows the sample names, diets,
and ages of the rats these samples came from. This study used TRF
pattern analysis to follow NCFM content in the feces and monitor
changes in the fecal bacterial communities.
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TABLE 1.
Rat fecal samples with respective diets as received from
the American Health Foundation for use in this study
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Creating TRF patterns for analysis.
Each sample was
homogenized by pulverization under liquid nitrogen. DNA was
extracted from 100 mg of sample using a MoBio (Solano
Beach, Calif.) Ultraclean Soil DNA Kit by the manufacturer's protocol.
Amplification of the template DNA was performed by using a
5'-FAM-labeled primer, 46f (5'-FAM-GCYTAACACATGCAAGTCGA;
Applied Biosystems Inc., Fremont, Calif.), and unlabeled
primer 536r (5'-GTATTACCGCGGCTGCTGG). Reactions were carried
out in triplicate with the following reagents in 50-µl reaction
mixtures: template DNA, 10 ng; 1× buffer (Promega); deoxynucleoside
triphosphates, 0.6 mM; bovine serum albumin, 0.8 µg/liter;
MgCl2, 3.5 mM; 46f, 0.2 µM; 536r, 0.2µM; and
Taq DNA polymerase (Promega), 2 U. Reaction temperatures and
cycling for fecal samples were as follows: 94°C for 2 min; 35 cycles
of 94°C for 2 min, 48.5°C for 1 min, and 72°C for 1 min; and one
cycle of 72°C for 10 min. Primers were removed and amplicons were
concentrated using the MoBio PCR Clean-Up kit according to the
manufacturer's protocol. Fluorescently labeled DNA (200 ng) was cut
with one restriction endonuclease enzyme
MspI,
DpnII, or HaeIII (2.0 to 4.0 U; New England
Biolabs, Beverly, Mass.)in the manufacturer's recommended reaction
buffers. Reaction mixtures were incubated for 5 h at 37°C and
then immersed in a 65°C water bath for 20 min. After ethanol
precipitation the DNA was dissolved in 18 µl of formamide (Bio-Rad,
Benecia, Calif.), with 1 µl each of Genescan Rox 500 (Applied
Biosystems) and Rox 550-700 (BioVentures, Murfreesboro, Tenn.) size
standards. The DNA was denatured at 95°C for 5 min and snap-cooled on
ice. Samples were run on an ABI Prism 310 Genetic Analyzer. Genescan
3.1 software with a 50-U detection threshold, Local Southern size
matching, and heavy smoothing were used to quantify the
electropherogram output.
Sample data consisted of the size (base pairs) and peak area for each
TRF peak in a pattern. Because the amount of DNA loaded
on the
capillary cannot be accurately controlled, the sum of all
TRF peak
areas in a pattern (total peak area) varied between TRF
patterns. To
compensate for this variation, it was necessary to
normalize peak
detection thresholds and peak areas. The peak detection
threshold was
normalized by creating artificial detection thresholds
for each sample.
The new threshold value was created by multiplying
a pattern's
relative DNA ratio (the ratio of total peak area in
the pattern to the
total peak area in the sample with the smallest
total peak area) by 580 area units (the area of a peak at the
50-U detection threshold). TRF
peaks with areas less than the
new threshold value for a sample were
removed from the data set
(Table
2). Peak
areas were then normalized by converting the
value of each remaining
peak area to parts per million of the
new total area.
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TABLE 2.
Example of truncation procedure used to determine
smallest observable peak for each sample in a data set
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|
Monitoring Lactobacillus acidophilus NCFM in
feces.
All the TRF patterns for rats fed NCFM included a large TRF
peak 2 to 3 bp smaller than that predicted for NCFM by rDNA sequence analysis (Table 3). A pure culture of
NCFM produced the same size TRF peak as the one seen in the fecal
patterns. This difference between predicted and observed TRF length has
been described previously, though not fully explained (4, 7, 11,
13).
Visual inspection of the TRF patterns shows a large NCFM-associated
peak in feces from rats fed NCFM, in contrast to a very
small peak in
those not fed NCFM (Fig.
1). Fortunately,
there
appeared to be very few bacteria with the same TRF peaks as NCFM
in the feces of rats not fed NCFM (Table
4). Regardless of the
enzyme used to
create a TRF pattern, TRF peak areas increased
with dietary NCFM
content. In fact, the relative amount of peak
area attributable to NCFM
doubled in rats fed twice as much NCFM
(Table
4). This does not
necessarily imply that the feces harbored
twice as many live cells of
NCFM because PCR was performed on
DNA extracted directly from the feces
and could have been isolated
from nonviable cells.
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FIG. 1.
Close-up of HaeIII-derived TRF patterns from
young rats in the study. The TRF patterns from feces of rats not fed
NCFM (A) and from feces from rats fed 2% NCFM (B) are shown. Peaks of
interest in this study are labeled for ease of discussion (Lba,
L. acidophilus NCFM).
|
|
The ability of TRF patterns to detect the relative amount of NCFM DNA
in the feces suggests that TRF patterns may be used
for the relative
quantitation of some bacteria in complex communities.
In fact, Clement
and Kitts reported a one-to-one correspondence
between the proportion
of NCFM cells added to a fecal sample and
the percentage of the total
TRF peak area present as
L. acidophilus TRF peaks
(
6). This correspondence may be influenced by both
PCR
primer homology and 16S rDNA copy number in
L. acidophilus.
Previous papers have postulated that an average bacterial community
has
3.8 16S rDNA copies per genome (
8) and
L. acidophilus has
four copies (
19).
Monitoring changes in fecal communities.
While the number of
samples collected in the study of Rao et al. was too small to produce
statistically significant data (18), we were interested in
testing methods for analyzing TRF patterns to detect specific organisms
that contribute to differences in bacterial communities. A combination
of principal component analysis (PCA) and database matching was investigated.
Covariance PCA (
21) was performed on normalized data sets
consisting of TRF peak areas combined from all three enzyme-derived
TRF
patterns. Samples from rats with NCFM in their diet (samples
YR
2%, YR 4%, OR 2%, and OR 4% [Table
1]) were analyzed after
removing peaks specific to NCFM to prevent PCA from separating
the
samples based solely on the dominant NCFM peaks. PCA produces
a
collection of loadings for each principal component that describes
the
relative contribution of each variable to the principal component
score
for a sample (
21). This allows an investigator to focus
on
those variables (TRF peaks in this case) that contribute to
differences
in principal component scores between samples. The
TRF peaks with large
loadings can then be matched to database-predicted
TRF lengths for
presumptive identification of significant bacterial
populations. The
first principal component (PC1) appeared to separate
samples based on
the age of rats (sample group YR versus OR [Fig.
2]). The second principal component
(PC2) appeared to separate
samples by NCFM content (0% versus 2 and
4% [Fig.
2]). This suggested
that some bacteria differed in levels
of abundance in the rat
feces depending on age and/or diet.
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FIG. 2.
PCA of three combined TRF patterns for each fecal
sample. Data for each sample consist of TRF peak areas from all
patterns made after digestion with each of the three enzymes. Arrows
represent principal component loadings for the TRF peak sets listed in
Table 3: arrows with solid lines, L. johnsonii set; arrows
with dashed lines, R. flavifaciens set. The TRF lengths
(base pairs) and enzymes (M, MspI; D, DpnII; H,
HaeIII) are indicated. Percent variation covered by each
principal component is indicated in parentheses in the axis titles
below and to the left, along with principal component scores. The right
and top axes show values for principal component loadings.
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|
For each sample, TRF pattern data from the three enzyme digests were
used to look for matches to database-predicted TRF lengths.
16S
rDNA sequences from the Ribosomal Database Project (RDP) 7.1
(
14) were used to create a new database containing
the calculated
TRF peak sizes for 6,358 organisms after PCR with
primers 46f
and 536r. To identify bacteria in a fecal sample, each
organism
from the database was compared to the TRF peaks from three
different
enzyme digests using a Microsoft Excel macro. Differences are
commonly reported between observed TRF lengths and those predicted
by
sequence analysis (
5,
6,
11,
13). To compensate for
this
discrepancy, an observed TRF (from a sample) was allowed
to be within
+1 to
4 of the predicted TRF length (in the
database).
TRF peaks with large, congruent loadings for PC1 were then used to
search through database matches to identify presumptive
bacterial
species whose abundance differed with the age of the
rats. Two sets of
TRF peaks clearly fit these criteria and were
thus chosen for closer
investigation (Fig.
2). The TRF peak sets
were named for bacteria in
the database that matched the set (Table
3). We could now monitor two
TRF peak sets that showed different
dynamics during the study of Rao et
al (
18).
Lactobacillus johnsonii TRF peak set.
Peak areas
from the L. johnsonii set were more abundant (1.8 to
6.7%) in young rats than in old rats (0.3 to 2.2%) as visualized in
the PCA graph (Fig. 2). Peak areas decreased as the rats aged, regardless of dietary NCFM (Table 4). A similar decrease in NCFM peak
areas was seen as the rats aged. Since both species of lactobacillus decreased in relative abundance, perhaps something changed in the older
rat's cecal environment that made it less hospitable to lactobacillus
species in general.
To confirm the presence of
L. johnsonii in the feces, three
different bacteria were isolated at random from two fecal samples
(YR 0% and YR 4%). Rat fecal samples were diluted and plated on
MRS agar (Difco, Sparks, Md.). Colonies were picked and streaked
twice
for purity. DNA from each isolate was then processed for
TRF analysis
as described above. All three bacteria produced TRF
peaks identical to
the
L. johnsonii TRF set. Extracted DNA samples
were
amplified for sequencing by PCR as described above except
that the
forward primer was replaced with unlabeled 8df
(5'-AGAGTTTGTTCMTGGCTCAG).
Sequencing reaction mixtures (10 µl) contained DNA, 4 ng; primer,
1 µM; ABI Big Dye (Perkin-Elmer),
4 µl; and PCR water, 0.4 µl.
Samples were run on an ABI 377 DNA
sequencer, and the resulting
sequences were analyzed in
Autoassembler (Perkin-Elmer). A BLAST
search of the sequences
gave
L. johnsonii as a 99% match. This
indicates not only
that
L. johnsonii was actually present in the
feces but also
that it was numerically abundant enough to be easily
isolated after the
freezing and processing steps the feces were
taken
through.
Ruminococcus flavefaciens TRF peak set.
In
seven-week-old rats the R. flavefaciens set represented an
average of 5% of the total peak area in control rats, while those fed
NCFM showed an average of 1.7%. By age 16 weeks, the average peak area
of the R. flavefaciens set had increased to 6% in the
NCFM-fed rats and 7% in the controls (Table 4). In contrast
with L. johnsonii, this suggests that R. flavefaciens was adversely effected by NCFM at an early stage but
eventually reached levels similar to those of the controls in spite of
the continuing presence of NCFM. Perhaps NCFM altered the cecal
environment in such a way that R. flavefaciens growth was
inhibited in young rats. The increase in R. flavefaciens TRF
peak areas to levels similar to those seen in control rats by 16 weeks
of age suggests that this effect was only temporary. Perhaps the
inhibitory effect against R. flavefaciens in young rats was
diminished as the levels of NCFM dropped in the older rats. Rao et al.
found that feeding NCFM correlated with a significant decrease in fecal
-glucuronidase activity (18). It is not unreasonable,
therefore, to expect to find altered TRF peak areas corresponding to
bacteria known to produce -glucuronidase enzymes. Intriguingly,
Ruminococcus species have been recently reported to express
-glucuronidase (1).
Conclusions.
Although no obvious mechanism behind the
beneficial health effects seen in the Rao et al. study could be
ascertained here, TRF patterns proved to be a useful tool for
monitoring the effects of probiotic dietary supplements on bacterial
community structure. TRF pattern analysis clearly has the ability to
detect changes in bacterial communities due to the introduction of
probiotic supplements. Unfortunately, the statistical significance of
the observed differences in TRF patterns could not be accurately
assessed in this case because the feces of three rats per treatment
were combined during sampling. However, TRF patterns were able to
identify organisms involved in the dynamics of bacterial community
structure, a confirmation of suggestions by researchers using this tool
(2, 3, 10, 16). The isolation of fecal L. johnsonii strains producing the exact TRF peaks seen in fecal TRF
patterns after a database search predicted the presence of this
bacterium confirmed the accuracy of this method. The ability to
associate TRF peaks with organisms allowed a deeper understanding of
bacterial community dynamics with some unforeseen results. The addition
of L. acidophilus NCFM to the young rat's diet appeared to
inhibit the growth of an R. flavifaciens strain and decrease
-glucuronidase activity (18). Why this happened,
whether it is a reproducible effect, and what the significance of this
interaction might be are now topics for investigation in a new study
designed specifically to explore these findings.
| |
ACKNOWLEDGMENTS |
Thanks are due to the Environmental Biotechnology Institute at Cal
Poly San Luis Obispo for funding this study and to the Unocal
Corporation for their support of TRF studies that has provided the
foundation for this study at the EBI.
We also thank the staff at EBI that helped with the research: Raul
Cano, Brian Clement, Tobe Cox, and Alice Hamrick.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: Environmental
Biotechnology Institute, California Polytechnic State University, San Luis Obispo, CA 93407. Phone: (805) 756-2949. Fax: (805) 756-1419. E-mail: ckitts{at}calpoly.edu.
| |
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Applied and Environmental Microbiology, April 2001, p. 1935-1939, Vol. 67, No. 4
0099-2240/01/$04.00+0 DOI: 10.1128/AEM.67.4.1935-1939.2001
Copyright © 2001, American Society for Microbiology. All rights reserved.
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