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Applied and Environmental Microbiology, August 1999, p. 3483-3486, Vol. 65, No. 8
0099-2240/99/$04.00+0
Copyright © 1999, American Society for Microbiology. All rights reserved.
Use of Antibiotic Resistance Analysis To Identify
Nonpoint Sources of Fecal Pollution
B. A.
Wiggins,*
R. W.
Andrews,
R. A.
Conway,
C. L.
Corr,
E.
J.
Dobratz,
D. P.
Dougherty,
J. R.
Eppard,
S. R.
Knupp,
M. C.
Limjoco,
J. M.
Mettenburg,
J. M.
Rinehardt,
J.
Sonsino,
R. L.
Torrijos, and
M. E.
Zimmerman
Department of Biology, James Madison
University, Harrisonburg, Virginia 22807
Received 22 March 1999/Accepted 27 May 1999
 |
ABSTRACT |
A study was conducted to determine the reliability and
repeatability of antibiotic resistance analysis as a method of
identifying the sources of fecal pollution in surface water and
groundwater. Four large sets of isolates of fecal streptococci (from
2,635 to 5,990 isolates per set) were obtained from 236 samples of
human sewage and septage, cattle and poultry feces, and pristine
waters. The patterns of resistance of the isolates to each of four
concentrations of up to nine antibiotics were analyzed by discriminant
analysis. When isolates were classified individually, the average rate
of correct classification (ARCC) into four possible types (human, cattle, poultry, and wild) ranged from 64 to 78%. When the resistance patterns of all isolates from each sample were averaged and the resulting sample-level resistance patterns were classified, the ARCCs
were much higher (96 to 100%). These data confirm that there are
measurable and consistent differences in the antibiotic resistance patterns of fecal streptococci isolated from various sources of fecal
pollution and that antibiotic resistance analysis can be used to
classify and identify these sources.
| |
INTRODUCTION |
Contamination of surface water and
groundwater with untreated manure and sewage continues to be a serious
environmental problem (20). Exposure of the public to
pathogens in drinking and recreational water is unacceptable, and high
levels of nitrogen and phosphorus in the receiving waters can cause
eutrophication. In many waters, contamination can occur from several
possible sources, including animal sources in agriculture (e.g., cattle
feedlots and poultry litter piles) as well as human sources (failing
septic systems and leaking sewer lines), and thus the actual source(s)
of the pollution is often unknown.
There have been many attempts to develop a method for source
determination, including measurement of the ratio of fecal coliforms (FC) to fecal streptococci (FS) (1, 3, 16), detection of the
presence of specific types of bacteriophages (4, 14, 17, 21)
or FS (2, 18), molecular techniques such as pulsed-field gel
electrophoresis (6, 19), and antibiotic resistance analysis (7, 9-12, 15). However, few have shown high levels of
specificity and reliability. Among these, one promising approach is
based on the analysis of differences in antibiotic resistance by using the multiple antibiotic resistance (MAR) index (7, 12, 15). In 1996, we reported a new type of antibiotic resistance analysis (ARA), which builds on the power of the MAR analysis (22)
but with FS instead of FC. FS (a multispecies grouping of
gram-positive, catalase-negative cocci that hydrolyze esculin and grow
in 6.5% NaCl and at 45°C) were used instead of FC because FS survive
well in the environment and are found in all potential pollution
sources (e.g. composted poultry litter [23]), in
contrast to FC. By increasing the number of concentrations for each
drug tested from one to four and by using the power of discriminant
analysis (a multivariate statistical technique [5]),
we were able to correctly classify isolates of FS from known and
unknown sources based on their antibiotic resistance profiles. For
example, when isolates from cattle, human, poultry, and wild sources
were analyzed, the average rate of correct classification (ARCC) was
84% (22).
Although our initial study showed good classification success, it was
based on a relatively small number of isolates. In the present study,
we used ARA to classify four new, larger data sets, collected over a
4-year period. By using more and/or different antibiotics, we obtained
classification success rates that, although not as high as the initial
rate, were still well above background (random) classification levels.
Thus, these data support our hypothesis that ARA can be a useful tool
in classifying and identifying potential sources of fecal pollution in
natural waters.
| |
MATERIALS AND METHODS |
Sample collection.
Four sets of samples were collected from
June 1993 through April 1997 (Table 1).
Samples were obtained from the following types of known sources: beef
and dairy cattle feces (cattle), turkey and chicken feces (poultry),
domestic sewage influent and septage (human), and pristine streamwater
(wild). For each agricultural animal source, samples were collected
from fresh feces from local farms located in Rockingham County, Va.
Sewage samples were collected from the influent of local municipal
sewage treatment plants (Fisherville, Stephens City, Middletown, and
Parkers Mill, Va.). These facilities process only human waste, but the
possibility of agricultural input from surface runoff cannot be
excluded. Septage samples were obtained from septic trucks. Wild
isolates were collected from pristine streams (Briery Branch, Upper Dry
River) in the George Washington National Forest, Va. Because these
pristine streams are not significantly impacted by humans
(8), the stream samples were assumed to contain bacteria
from the feces of wild animals only. After collection, all samples were
placed on ice and processed within 6 h.
Isolation of FS.
FS were isolated as described previously
(22). Various amounts of fecal samples (0.1 to 1.0 g)
were suspended in 50 ml of saline buffer (8.5 g of NaCl, 0.3 g of
KH2PO4, and 0.6 g of
Na2HPO4 per liter [pH 7.3]) and were filtered
through 0.45-µm-pore-size filters (type GN-6; Gelman Sciences). The
filters were then transferred to a 50-mm petri dish containing an
absorbent pad soaked with 1.9 ml of Enterococcosel broth (BBL). The
filters were incubated for 48 h at 37°C. After incubation, 48 (or, for some of the earlier samples, 96) colonies from each sample
were randomly picked with sterile toothpicks, transferred to microwell
plates containing 0.2 ml of Enterococcosel broth, and incubated for
another 48 h at 37°C. Some isolates were randomly chosen for
characterization, and 161 of the 190 isolates were classified as FS.
Antibiotics.
Antibiotics were selected because of their
widespread use in animals and/or humans: amoxicillin (AMX) (Sigma);
ampicillin, sodium salt (AMP) (Sigma); chlortetracycline hydrochloride
(CTC) (Sigma); erythromycin (ERY) (Sigma); gentamicin (GEN) (Sigma); oxytetracycline hydrochloride (OTC) (Sigma and ICN); salinomycin (SAL)
(Agri-Bio and Sigma); streptomycin sulfate (STR) (Sigma); tetracycline
(TET) (Sigma); and vancomycin (VAN) (Sigma). All these drugs are used
in both humans and animals except SAL (chickens only) and VAN (humans
only) (13). Stock solutions of each drug were prepared in
water (AMP, OTC, and STR), in 1:1 water-ethanol (CTC, ERY, SAL, TET,
and VAN), or in 1:1 water-methanol (AMX), filter sterilized, and added
to autoclaved Trypticase soy agar (BBL). The following final
concentrations were used: 20, 40, 60, and 80 µg/ml for CTC, OTC, and
STR; 5, 10, 15, and 20 µg/ml for AMX and GEN; 10, 15, 30, and 50 µg/ml for AMP, ERY, and TET; 5, 10, 15, and 30 µg/ml for VAN; and
1, 5, 10, and 15 µg/ml for SAL.
Antibiotic resistance was determined as described previously
(
22). The isolates were transferred with a 48-prong
replica-plater
(Sigma) from the Enterococcosel-containing microwells to
a set
of antibiotic-containing Trypticase soy agar plates. Each set
consisted of one plate of each concentration of each antibiotic
and one
control plate containing no antibiotic. The plates were
incubated for
24 h, and growth of each isolate on each concentration
of each
antibiotic was determined. An isolate was considered to
be resistant to
a given concentration of antibiotic if growth
occurred on that plate.
Esculin-negative isolates and isolates
that did not grow on the control
plates were not used in the
analyses.
Discriminant analysis.
Data for the ability of each of the
known isolates to grow in the presence of each concentration of each
antibiotic were analyzed by SAS software (VAX version 6.08; SAS
Institute Inc.) using the procedure DISCRIM (prior probabilities,
equal; covariance matrix, pooled). The ARCC for each combination of
antibiotics was computed by averaging the percentages of correctly
classified isolates (along the diagonal) (22).
Discriminant analysis was performed on five sets of isolates: one
previously published (
22) set (set 1) and four new sets
(sets 2 to 5). The sets were collected during different times
and
differ in both the number and combination of antibiotics on
which the
isolates were tested (Table
1).
Discriminant analysis was performed in two ways: at the isolate level
and at the sample level. For isolate-level analyses,
each isolate was
classified individually, based on its resistance
to each concentration
of each drug, into one of four types (cattle,
human, poultry, and
wild). For these analyses, the number of isolates
per data set ranged
from 1,435 to 5,990. For sample-level analyses,
the resistance of all
of the isolates from each sample was averaged
for each concentration of
each antibiotic. For example, if one-quarter
of the isolates in a given
sample were resistant to 40 µg of STR/ml,
the average value of 0.25 would be used. Average values for each
concentration of each drug were
then used to classify the sample
into one of the above four groups. The
number of samples per data
set ranged from 17 to
72.
| |
RESULTS |
Isolate-level analyses.
Over a 4-year period, four sets of
isolates were collected and analyzed (by using various numbers and
combinations of antibiotics) to determine if the high rate of
classification success that was obtained previously (22)
could be obtained again. In the previously published set (set 1), the
ARCC of those isolates was 84% when analyzed with four drugs: CTC,
OTC, SAL, and STR (Table 6 in reference 22). Cattle
and wild isolates were classified moderately successfully (79 and 75%,
respectively), and human and poultry isolates were very well classified
(93 and 88%, respectively).
To see if other drugs could provide better classification, a second set
of isolates (set 2) was tested by using an entirely
different set of
drugs: AMP, ERY, GEN, TET, and VAN. These drugs
were chosen because
they are commonly used in humans. Unfortunately,
when set 2 was
analyzed, the ARCC was only 64% (Table
2). Cattle
and poultry isolates were well
classified, but human and wild
isolates were very poorly classified. A
third set (set 3) was
tested by using a subset of the drugs from the
two previous sets:
OTC, SAL, STR, TET, and VAN. When set 3 was
analyzed, the ARCC
was 66% (Table
3). As
with set 2, human isolates were poorly
classified and poultry isolates
were well classified. A fourth
set (set 4) was tested by using six
drugs: AMX, ERY, OTC, STR,
TET, and VAN. AMX was added because of its
high levels of use
in humans. When set 4 was analyzed, the ARCC was
65% (Table
4).
Human isolates were very
poorly classified, but wild isolates
were well classified.
To determine if data from more drugs would improve classification
success, a fifth set (set 5) was tested by using AMP, AMX,
CTC, ERY,
OTC, SAL, STR, TET, and VAN. Additionally, this set
included only
septic sources for the human samples, while the
previous sets contained
samples of primary sewage influent, which
could have been contaminated
with other sources by overland flow.
When set 5 was analyzed, the ARCC
increased to 78% (Table
5).
The
classification success of human isolates increased to 82%.
Poultry and
wild isolates were well classified in this set, but
cattle isolates
were poorly classified.
Sample-level analyses.
In addition to the isolate-level
analyses, we analyzed all five data sets at the sample level. When the
antibiotic resistance patterns of all the isolates in each sample were
averaged, the classification of the samples was very high (Table
6). Three of the five data sets were
perfectly classified at the sample level, and overall only 5 of 236 samples (2%) were incorrectly classified.
| |
DISCUSSION |
Isolate-level analyses.
Although there were lower rates of
classification success for the isolates in the larger datasets than was
seen previously, many more isolates were correctly classified (greater
than 60%) than would occur as a result of random classification into
one of four groups (25%). Even though these subsequent sets of
isolates were not classified as well as the initial set, the ARCCs were consistently more than 60% correct in samples that were collected from
many different sources over a 4-year period.
Sets 2, 3, and 4 were similarly classified (similar ARCCs), but many of
the individual sources showed variability in classification
success.
The reasons for this variability are unclear. The variation
in
classification success may be explained in part by the different
number
and combination of drugs used. In general, the number of
drugs that
were used in a classification was only weakly associated
with
classification success. No particular drug or drugs seem
to be
necessary for good classification in all datasets: removal
of a given
drug from the analysis may reduce the ARCC for one
data set but may
have no appreciable effect for another (analyses
not shown). Thus, it
seems likely that the more drugs that are
used, the better the chances
of getting a combination of drugs
that is successful in discriminating
among that particular set
of
samples.
Another hypothesis for the variation in classification success of
individual sources from set to set could be changes in the
populations
from which the samples were taken. These samples were
collected over a
4-year period from many different locations,
and so this possibility
cannot be excluded. If the resistance
patterns do change over time or
from location to location, the
database that is used to classify
unknown samples should be composed
of recently collected, local
samples.
The classification of human isolates improved markedly in set 5. This
could have been a result of the use of more drugs, but
it could also
have been caused by the use of septage as the source
of the human
samples. Human samples in sets 1 to 4 were from influent
to municipal
sewage treatment plants. Although most of the sewage
was human in
origin, the influent in many of the plants could
have included overland
flow from agricultural land, which could
have introduced contaminating
bacteria from animal sources and
thus reduced the classification
success.
Poultry isolates were consistently very well classified. Because
chickens and turkeys often receive regular exposure to antibiotics
in
their food or water, there is strong selection pressure on
their fecal
bacteria to become resistant to these drugs, and this
is reflected in
the high classification rates. Cattle isolates,
however, were generally
poorly classified, especially in the last
set of isolates. Generally,
cattle are only occasionally given
antibiotics and thus would seem to
be more similar to wild isolates,
which receive no antibiotics. The
high proportion of cattle isolates
that were misclassified as wild in
set 5 (24%) supports the similarity
of cattle and wild
isolates.
Sample-level analyses.
The sample-level analyses strongly
support our hypothesis that patterns of antibiotic resistance can be
used to classify FS isolated from different sources. When the
resistance data from all the isolates from each sample were averaged,
the classification of all samples was excellent (98%). However, a
drawback of sample-level analysis is the necessary assumption that all
of the isolates in a given sample are from the same source (because
they are all averaged together). This assumption is valid for known,
homogeneous samples, such as those analyzed here but would not be valid
for a sample which was contaminated by more than one source. If it is
assumed that a sample has a single major source, the sample-level analysis would be preferred because of the high ARCCs. If this is not
the case, the isolate-level analysis should be used, because it can
assign each of the isolates to a specific source.
In conclusion, the large number of isolates tested so far confirms that
sources of fecal pollution can be differentiated by
using ARA. Although
some isolates were misclassified, the majority
of isolates were
correctly classified in all sources from all
data sets. The information
contained in the resistance patterns
thus seems strong enough to use
for classification of unknown
isolates from polluted waters, which may
contain mixtures of different
sources. However, further research is
needed to determine if antibiotic
resistance analysis can accurately
identify the components of
mixed
samples.
| |
ACKNOWLEDGMENTS |
We thank J. Monroe and C. Hagedorn for critical reading of the
manuscript and R. Domangue, R. Harris, I. Knight, and G. Wyngaard for
discussion and helpful comments.
This work was supported by the James Madison University Department of
Biology and by grants from the Virginia Water Resources Research Center
and the Shenandoah Valley Soil and Water Conservation District of Virginia.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: Department of
Biology, MSC 7801, James Madison University, Harrisonburg, VA 22807. Phone: (540) 568-6196. Fax: (540) 568-3333. E-mail:
wigginba{at}jmu.edu.
| |
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Applied and Environmental Microbiology, August 1999, p. 3483-3486, Vol. 65, No. 8
0099-2240/99/$04.00+0
Copyright © 1999, American Society for Microbiology. All rights reserved.
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Abstract
Introduction
