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Applied and Environmental Microbiology, June 2000, p. 2572-2577, Vol. 66, No. 6
0099-2240/00/$04.00+0
Copyright © 2000, American Society for Microbiology. All rights reserved.
Use of Repetitive DNA Sequences and the PCR To Differentiate
Escherichia coli Isolates from Human and Animal
Sources
Priscilla E.
Dombek,1
LeeAnn K.
Johnson,1
Sara T.
Zimmerley,1 and
Michael J.
Sadowsky1,2,*
Department of Soil, Water, and
Climate,1 and Biological Process
Technology Institute,2 University of
Minnesota, St. Paul, Minnesota 55108
Received 2 February 2000/Accepted 30 March 2000
 |
ABSTRACT |
The rep-PCR DNA fingerprint technique, which uses repetitive
intergenic DNA sequences, was investigated as a way to differentiate between human and animal sources of fecal pollution. BOX and REP primers were used to generate DNA fingerprints from Escherichia coli strains isolated from human and animal sources (geese,
ducks, cows, pigs, chickens, and sheep). Our initial studies revealed that the DNA fingerprints obtained with the BOX primer were more effective for grouping E. coli strains than the DNA
fingerprints obtained with REP primers. The BOX primer DNA fingerprints
of 154 E. coli isolates were analyzed by using the Jaccard
band-matching algorithm. Jackknife analysis of the resulting similarity
coefficients revealed that 100% of the chicken and cow isolates and
between 78 and 90% of the human, goose, duck, pig, and sheep isolates were assigned to the correct source groups. A dendrogram constructed by
using Jaccard similarity coefficients almost completely separated the
human isolates from the nonhuman isolates. Multivariate analysis of
variance, a form of discriminant analysis, successfully differentiated the isolates and placed them in the appropriate source groups. Taken
together, our results indicate that rep-PCR performed with the BOX A1R
primer may be a useful and effective tool for rapidly determining
sources of fecal pollution.
| |
INTRODUCTION |
Despite the fact that elevated
levels of Escherichia coli are correlated with increased
risk of several diseases, fecal contamination of water is a widespread
problem in the United States (30). A 1996 report to Congress
stated that 47% of the river miles assessed in Minnesota could not be
used for swimming due to high levels of fecal coliform bacteria. For
some rivers the problem is pervasive; the fecal coliform counts for
more than 90% of the Minnesota River and its tributaries are
consistently elevated.
Determining the source of fecal pollution is necessary to develop
effective control strategies. The possible sources of fecal contamination include surface runoff from manure-treated agricultural land or farm animal feedlots, failing or inadequate septic systems, sewer overflow, and wildlife. Contamination of water with fecal coliform bacteria of human origin may signal the presence of other potential human pathogens, such as Salmonella spp.,
Shigella spp., hepatitis A virus, and Norwalk group viruses
(8, 17). Farm animals may also harbor human pathogens,
including the potentially fatal organism E. coli O157:H7.
Poultry are a primary reservoir of Salmonella spp, as are
swine, which may also carry Shigella spp. (14).
A number of analytical methods for differentiating between human and
nonhuman sources of fecal pollution have been evaluated. These methods
include determining percentages and identities of fecal streptococci
(3, 4, 23), determining differences in RNA coliphage
distribution (6, 18), pulsed-field gel electrophoresis (11, 26), and determining whether Bacteroides
fragilis HSP40 phages are present (29). However, a
completely satisfactory technique has not been found yet. Historically,
the ratio of fecal coliforms to fecal streptococci has been used
(5, 7), but it has been shown that this method is unreliable
(21).
There have been several reports of the use of antibiotic resistance
profiles to determine sources of E. coli. It has been found
that isolates obtained from humans, chickens, and dairy cows have
higher resistance indices than strains obtained from wild animals
(14). A larger percentage of water isolates from urban areas
compared to isolates from rural areas exhibit resistance to
antibiotics, presumably because human isolates are present (12). More recently, Parveen et al. (19) reported
that human E. coli isolates clustered near isolates obtained
from sewage treatment plant effluents and that isolates from animal
feces were more similar to nonpoint source isolates.
In two recent studies workers have demonstrated that antibiotic
resistance profiles of fecal streptococci can be used to differentiate between human and animal sources of fecal pollution. In one study, more
than 10,000 fecal streptococcal isolates were obtained from 236 samples
of human sewage and septage, cattle feces, poultry feces, and pristine
waters (33). The average rates of correct classification
into one of four possible groups (human, cattle, poultry, and wild)
ranged from 64 to 78%. More recently, Hagedorn et al. (9)
validated this method by using 13 antibiotics and more than 7,000 isolates from 147 samples obtained from humans, dairy cattle, beef
cattle, chickens, deer, and waterfowl. Correct classification into one
of the six groups described above was 87%.
More modern methods have also been evaluated to determine whether they
can be used to differentiate between sources of fecal contamination of
water. Parveen et al. (20) performed a ribotype analysis of
E. coli isolates obtained from a bay in Florida. When their
database was used, 67 and 100% of the isolates from human and animal
feces, respectively, were correctly classified as members of human or
nonhuman source groups. Ribotyping has also been used to determine the
sources of E. coli contaminating Little Soos Creek in
Washington state (25).
In this paper, we describe the use of the rep-PCR DNA fingerprinting
technique to differentiate E. coli strains obtained from known animal and human sources. In rep-PCR DNA fingerprinting, PCR
amplification of the DNA between adjacent repetitive extragenic elements is used to obtain strain-specific DNA fingerprints which can
be easily analyzed with pattern recognition computer software. The
rep-PCR technique was chosen because this technique is simple, can
differentiate between closely related strains of bacteria, and can be
used for high-throughput studies (32). Previously, rep-PCR
has been used successfully to classify and differentiate among strains
of E. coli (15), Rhizobium meliloti
(2), Bradyrhizobium japonicum (10),
Streptomyces spp. (24), Xanthomonas
spp. (1), and several other bacteria (31).
| |
MATERIALS AND METHODS |
E. coli sources and isolation.
Table
1 lists the sources of the isolates used
in this study, the number of isolates obtained from each source, the
numbers of individuals sampled, and locations of the sources. The human isolates were obtained from rectal swabs obtained from students enrolled in a microbiology lab class at the University of Minnesota. Duck fecal samples were obtained during a duck-banding study in northern Minnesota, and rectal swabs of Canada geese were taken from
animals at a wildlife management area. Chicken, pig, sheep, and cow
fecal samples were collected at the 1999 Minnesota State Fair; samples
were collected within 1 day of the arrival of animals at the fair.
Except for the chicken samples, all of which were animals from the same
farm, manure samples were gathered from animals from farms throughout
the state of Minnesota. Rectal swabs and fecal matter were stored on
ice and streaked within 12 h of collection onto mFC agar (Difco,
Detroit, Mich.). After overnight incubation at 44.5°C, blue colonies
were streaked onto the surfaces of MacConkey agar (Difco) plates and
transferred onto ChromAgar ECC (Chromagar Microbiology, Paris, France).
E. coli isolates form blue colonies on ChromAgar ECC, which
differentiates them from other coliform and gram-negative bacteria,
which form red and colorless colonies, respectively. After overnight
incubation at 37°C, pink colonies that were obtained from the
MacConkey plates and were also positive for E. coli on
ChromAgar ECC plates were used to inoculate citrate agar, EC broth
supplemented with 4-methylumbelliferyl-D-glucuronide (Difco) and 1% tryptone (Difco), and methyl red
Voges-Proskauer (Difco) broth. Isolates that did not grow on citrate agar, were positive for gas production and fluorescence on EC broth containing 4-methylumbelliferyl-D-glucuronide, produced indole from
tryptophan, and produced acidic end products when they were grown in
methyl red-Voges-Proskauer broth were designated E. coli
isolates and used for subsequent studies. Approximately 26 isolates
were obtained from each animal source, and 52 isolates were obtained
from humans. Up to three isolates were obtained from a single
individual.
E. coli preparation and PCR conditions.
E.
coli was prepared and PCR were performed essentially as described
by Rademaker and de Bruijn (22), with a few minor
modifications. Briefly, the E. coli isolates were grown for
about 18 h in Luria-Bertani liquid medium (16), washed
in 1 M NaCl, resuspended in sterile water, and frozen at 
80°C until
they were used. rep-PCR fingerprints were obtained by using primer BOX
A1R (5'-CTACGGCAAGGCGACGCTGACG-3') (31) or
primers REP 1R (5'-IIIICGICGICATCIGGC-3') and REP 2I (5'-ICGICTTATCIGGCCTAC-3') (22, 28). PCR mixtures
were prepared as described previously (22) by using 2-µl
portions of whole-cell suspensions of each isolate as the templates. A
control reaction mixture containing 2 µl of water instead of E. coli was also included in each set of PCR. Each PCR was performed
with a model PTC 100 apparatus (MJ Research, Waltham, Mass.) by using
the faster protocol specific for this thermocycler (22). The
PCR performed with primer BOX A1R was initiated by incubating the
reaction mixture at 95°C for 2 min, and this was followed by 30 cycles consisting of 94°C for 3 s, 92°C for 30 s, 50°C
for 1 min, and 65°C for 8 min. The reaction was terminated with an
extension step consisting of 65°C for 8 min. For PCR performed with
primers REP 1R and REP 2I the annealing temperature was 40°C. Five
microliters of 6× loading dye was added to each 25-µl PCR mixture,
and 10 µl of each reaction mixture was separated on a 1.5%
horizontal agarose gel. A 1-kb size ladder (0.5 µg/well; Life
Technologies, Rockville, Md.) was loaded into the two terminal wells
and in the middle of the gel. The gels were electrophoresed at 4°C
for 18 h at 70 V and stained for 20 min with a solution containing
0.5 µg of ethidium bromide per ml. Gel images were captured with a
FOTO/Analyst Archiver electronic documentation system (Fotodyne Inc.,
Hartland, Wis.).
Computer-assisted rep-PCR DNA fingerprint analysis.
Gel
images were normalized, bands were identified and the data were
statistically analyzed by using Bionumerics software (version 1.5;
Applied Maths, Kortrijk, Belgium). Lanes that were blank because the
PCR failed and lanes in which limited numbers of PCR products were
produced were not included in the analysis. The positions of fragments
(bands) on each gel were normalized by using the 1-kb ladder from 298 to 5,090 bp as an external reference standard. Normalization with the
same set of external standards allowed us to compare multiple gels.
Three or four bands that were common to most of the isolates on each
gel were also used as internal reference standards. The external and
internal standards corrected for smiling or other irregularities during
electrophoresis. DNA fragments less than 300 bp long were not used in
analyses because they tended to be indistinct. Fingerprint images were added to a database and compared by performing a statistical analysis.
Similarity coefficients were generated by the band-based method of
Jaccard by using fuzzy logic and area-sensitive options.
The Jaccard
similarity coefficient for each pair of fingerprints
was calculated by
dividing the number of bands that occurred in
both fingerprints by the
total number of bands (common and unique)
in both fingerprints. The
fuzzy logic option allowed band matching
values to gradually decrease
with the distance between bands,
and the area-sensitive option took
into account differences in
area between matching
bands.
Statistical analysis was used to determine the relatedness of DNA
fingerprints and to determine whether the isolates could
be
successfully assigned to the correct source groups. The DNA
fingerprints were compared to each other by calculating Jaccard
similarity coefficients. Similarity coefficients were first determined
by using the BOX- and REP-derived fingerprints individually and
then
were determined by using a combined data set, designated
the
BOX-plus-REP fingerprints. Jackknife analysis was used to
determine how
accurately the similarity coefficients were able
to predict the source
group of each isolate. This was done as
follows. The isolates were
first manually assigned to the correct
source groups, and then each
isolate was individually removed
from the database. The level of
similarity of the removed isolate
to the isolates remaining in each
source group was determined,
and an average group similarity
coefficient was calculated from
the individual similarity values for
each group. The isolate that
was removed was placed in the source group
having the greatest
average group similarity coefficient, and the
percentage of isolates
from each source group correctly assigned was
then
calculated.
A dendrogram was constructed by using Jaccard similarity coefficients.
A binary band-matching character table was generated
by using the
BOX-derived PCR DNA fingerprint data, and this table
was analyzed by
multivariate analysis of variance (MANOVA), a
form of discriminant
analysis. MANOVA was done, accounting for
the covariance
structure.
| |
RESULTS AND DISCUSSION |
Assignment of isolates to source groups.
Figure
1A shows typical fingerprints for
E. coli isolates generated by using rep-PCR performed with
primer BOX A1R. Complex fingerprint patterns were obtained for all of
the isolates studied. In general, the band patterns of isolates from
different animal sources were very similar, and the data indicated that
the isolates were closely related. While the fingerprint patterns for
E. coli isolates obtained from the same animal were similar,
they were not always identical. Approximately one-quarter of the bands
were common to 80% or more of the isolates, and a few bands were
shared by more than 90% of the isolates. Individual lanes generally
contained from 25 to 30 PCR product bands, although almost 40 bands
were obtained for some E. coli isolates. The sizes of the
PCR products ranged from slightly less than 300 bp to about 4,500 bp.
The initial studies were performed with both the BOX and REP primers.
However, 25% fewer PCR products were usually present in the
fingerprints generated with the REP primers than in the fingerprints
obtained with the BOX primer (Fig. 1B). This was largely due to the
scarcity of PCR products less than 750 bp long.
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FIG. 1.
rep-PCR DNA fingerprint patterns of E. coli
strains obtained from beef and dairy cows. (A) PCR DNA fingerprint
patterns generated with primer BOX A1R. Lanes A and L contained an
external standard, a 1-kb molecular weight ladder. (B) PCR DNA
fingerprint patterns generated with primers REP 1R and REP 2I. Lanes A
and L contained an external standard, a 1-kb molecular weight ladder.
The E. coli strains used for the fingerprint analysis shown
in panel B are identical to the strains used for the analysis shown in
panel A, except that the strains in lanes O and T are reversed.
|
|
A total of 208 isolates were used as templates for PCR performed with
the BOX and REP primers. These 208 isolates consisted
of 26 isolates
from each nonhuman animal source and 52 human isolates.
Approximately
74% of the isolates (154 isolates) produced high-quality
DNA
fingerprints when primer BOX A1R was used. These isolates
are listed in
Table
1. Some of the isolates that were successfully
used as templates
when the BOX primer was used did not produce
reliable fingerprints when
the REP primers were used. In our initial
studies, fingerprints were
obtained for only 125 isolates with
both the BOX primer and the REP
primers.
Statistical analysis was used to verify that these 125 isolates were
assigned to the correct source groups. Jaccard similarity
coefficients
were calculated for both the BOX-derived fingerprints
and the
REP-derived fingerprints individually and for a combined
data set, the
BOX-plus-REP fingerprints. The isolates were manually
assigned to the
correct groups, and a Jackknife analysis was performed.
The Jackknife
analysis was used to determine how accurately the
similarity
coefficients predicted the source groups. The results
of this analysis
are shown in Table
2. DNA fingerprints
generated
by using the REP primers were almost as useful as BOX-derived
DNA fingerprints for correctly classifying human and sheep isolates;
about 90% of the isolates belonging to both groups were correctly
classified. However, REP-derived fingerprints lagged considerably
behind BOX-derived fingerprints in the ability to effectively
group the
remaining isolates from the other animal sources (chickens,
cows,
ducks, geese, and pigs). There was no improvement in the
grouping of
strains when BOX-plus-REP DNA fingerprint data were
used compared to
BOX-derived fingerprints alone. Consequently,
only BOX-derived DNA
fingerprints were used in the remainder of
our study. Previously,
Lipman and coworkers (
15) found that
rep-PCR performed with
REP primers was less reliable than PCR
performed with enterobacterial
repetitive intergenic consensus
(ERIC) primers for differentiating
among
E. coli strains from
cows with clinical mastitis.
However, in their study, these authors
generated only a limited number
of PCR fragments with ERIC primers.
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TABLE 2.
Percentages of isolates correctly assigned to source
groups by using BOX-derived, REP-derived, and BOX-plus-REP PCR
DNA fingerprintsa
|
|
The entire BOX-derived DNA fingerprint data set generated with 154 isolates was analyzed by using Jaccard similarity coefficients
and
Jackknife analysis. The percentage of isolates assigned to
each group
was calculated. Table
3 shows that almost
83% of the
isolates obtained from humans were assigned to the human
group.
In some instances, however, human isolates were misidentified
as
members of the goose, pig, and duck groups. This relationship
was not
reciprocal, as goose isolates were most often misidentified
as chicken
isolates (9.5%) and never were classified as members
of the human
group. Overall, our results show that when primer
BOX A1R was used, the
rep-PCR technique very successfully classified
E. coli
isolates in the correct source groups. All of the chicken
and cow
isolates and between 78 and 90% of the human, goose, duck,
pig, and
sheep isolates were correctly identified when this primer
was used.
Based on these results, we concluded that the source
group of an
unknown isolate can most likely be identified by comparison
to the
average similarity coefficients for the source groups.
The percentages
of isolates correctly classified based on maximum-similarity
data were
similar, although usually not identical, to the percentages
of isolates
correctly classified by using average similarity coefficients
(data not
shown). For example, while duck and goose isolates were
classified
slightly better when maximum-similarity values were
used, average
similarity data described groups of pig and cow
isolates better.
Nevertheless, our results indicate that Jaccard
similarity coefficients
may be useful for identifying sources
of unknown environmental
isolates. To do this, the DNA fingerprint
of an unknown isolate can be
directly compared to a library of
BOX-derived DNA fingerprints of
isolates from human and animal
sources. After average group similarity
coefficients are determined
for the source groups, the unknown isolate
can be placed in the
source group with which it exhibits the highest
level of similarity.
Dendrogram construction.
To determine the relatedness of
strains, a dendrogram based on BOX-derived fingerprint data was
constructed by using Jaccard similarity coefficients and the
neighbor-joining clustering method (Fig.
2). As shown in Fig. 2, 26 of the 29 human isolates were grouped into four clusters at the top of the
dendrogram. These clusters also included two waterfowl isolates. Other
types of animal isolates also clustered together when this analysis was performed. All of the chicken isolates fell into a single cluster, as
did the majority of the cow, duck, and sheep isolates. Our results
indicate that while the dendrogram may have been useful for separating
isolates into human and nonhuman source groups, the isolates were
clearly closely related. The average distance between 18 of the 20 clusters, which accounted for more than 96% of the isolates, was less
than 10%. Similarly, although Hagedorn et al. (9) were able
to classify fecal streptococci isolates into groups (humans, dairy
cattle, beef cattle, chickens, deer, and waterfowl) by using antibiotic
resistance patterns, some overlap occurred between the human and
nonhuman (chicken) clusters.
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FIG. 2.
Dendrogram showing the relatedness of E. coli
strains isolated from humans, geese, ducks, sheep, pigs, chickens, and
cows as determined by a PCR DNA fingerprint analysis performed with
primer BOX A1R. Relationships were determined by using Jaccard
similarity coefficients and the neighbor-joining clustering method.
|
|
Clustering of isolates by MANOVA.
MANOVA, a clustering
technique based on discriminant analysis, can be used to determine even
small differentiating features in user-specified groups. In this study,
isolates were first manually assigned to the correct groups, and a
binary band-matching character table was generated by using the
BOX-derived fingerprint data. This table was analyzed by MANOVA by
using an option that accounted for covariance structure. Since seven
groups were specified (humans, cows, pigs, chickens, sheep, ducks, and
geese), a total of six discriminants were determined, and P
values calculated. The P value was the probability that
random subdivision of the groups would yield the same degree of
discrimination. Figure 3 maps isolates based on the first two discriminants. The MANOVA successfully sorted
the E. coli isolates into the correct source groups (the human, cow, sheep, duck, goose, chicken, and pig groups) with no
overlaps (Fig. 3). The first, second, and third discriminants accounted
for 33.0, 24.5, and 18.2%, respectively, of the discrimination. Together, the first three discriminants accounted for 75.7% of the
variation, and the first five discriminants accounted for 93.8% of the
variation. The P values for the first five discriminants were 
0.002, indicating that the specified groups were valid. Together, these results indicate that the MANOVA of the BOX-derived PCR
fingerprint data effectively clustered the human and animal isolates.
Moreover, BOX-derived PCR fingerprint data may be very useful for
determining the sources of unknown environmental E. coli
isolates.
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FIG. 3.
MANOVA of BOX-derived PCR DNA fingerprints from E. coli strains obtained from animal and human sources. Binary
band-matching character tables were analyzed by MANOVA, accounting for
the covariance structure. The E. coli isolates were obtained
from humans ( ), geese ( ), ducks ( ), sheep ( ), pigs ( ),
chickens ( ), and cows ( ). The first discriminant is represented
by the distance along the x axis, and the second
discriminant is represented by the distance along the y
axis.
|
|
In this study, we found that rep-PCR DNA fingerprint analysis was a
useful tool for differentiating between
E. coli isolates
obtained from six species of animals and humans. The animal isolates
included those from two types of waterfowl (geese and ducks) and
common
farm animals (cows, pigs, sheep, and chickens). Since genotypic
analyses are less subject to environmental effects than phenotypic
analyses are, we believe that rep-PCR may be a method of choice
for
differentiating and grouping
E. coli isolates obtained from
animals and humans. Other advantages of rep-PCR are its simplicity,
accuracy, and speed, which are desirable for high-throughput analysis.
While other genotypic analysis methods, such as ribotyping, have
been
examined to determine their ability to differentiate coliform
bacteria
(
25), these methods tend to require extensive manipulation
of DNA and the use of labeled gene probes. Because of this, these
methods are not amenable to high-throughput analyses. In addition,
in
the rep-PCR analyses performed here, DNA fingerprints were
generated by
using whole cell suspensions, which eliminated the
need for DNA
purification.
Previously, multiple antibiotic resistance profiles of
E. coli isolates were used to differentiate between point sources and
nonpoint sources (
19). Parveen et al. showed that isolates
from
point sources were more diverse than isolates from nonpoint
sources
and that
E. coli isolates from human and animal
feces clustered
with isolates from both point and nonpoint sources.
Other researchers
have demonstrated that antibiotic resistance analysis
of fecal
streptococci is useful for differentiating source groups
(
9,
33). However, grouping may be influenced by a strain's
prior
exposure to antibiotics. In addition, fecal streptococci also
persist longer in the environment, which may limit their usefulness
for
determining sources of recent contamination (
13,
27).
We
contend that since fecal coliforms, not fecal streptococci,
are the
most widely used indicators of water quality, it is important
to be
able to group
E. coli isolates.
In conclusion, our results indicate that rep-PCR DNA fingerprinting
performed with the BOX A1R primer is a promising method
for determining
the source groups of
E. coli isolates and may
prove to be
useful for determining the sources of closely related
E. coli strains obtained from environmental
samples.
| |
ACKNOWLEDGMENTS |
This work was supported in part by funding from The Environmental
and Natural Resources Trust Fund through The Legislative Commission on
Minnesota Resources and by funding from the University of Minnesota
Agricultural Experiment Station (to M.J.S.).
| |
FOOTNOTES |
*
Corresponding author. Mailing address: Department of
Soil, Water, and Climate, University of Minnesota, 1991 Upper Buford Circle, 439 Borlaug Hall, St. Paul, MN 55108. Phone: (612) 624-2706. Fax: (612) 625-6725. E-mail: sadowsky{at}soils.umn.edu.
| |
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Abstract
Introduction
