SEPA
EPA/600/R-07/123 I December 2007 | vwvw.epa.gov/athens
United States
Environmental Protection
Agency
Evaluation of Selected DMA-
based Technology in Impaired
Watersheds Impacted by Fecal
Contamination from Diverse
Sources
Ecosystems Research Division, Athens, GA 30605
National Exposure Research Laboratory
Office of Research and Development
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EPA/600/R-07/123
December 2007
Evaluation of Selected DMA-based Technology in
Impaired Watersheds Impacted by Fecal Contamination
from Diverse Sources
by
Marirosa Molina
Ecosystems Research Division
National Exposure Research Laboratory
Athens, GA 30605
U.S. Environmental Protection Agency
Office of Research and Development
Washinton, DC 20460
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Notice
The information in this document has been funded by the United States
Environmental Protection Agency. It has been subjected to the Agency's peer
and administrative review, and it has been approved for publication as an EPA
document. Mention of trade names of commercial products does not constitute
endorsement or recommendation for use.
Abstract
Fecal pollution of surface waters is a top reason for impairment, as
reported in the U.S. Environmental Protection Agency's report on the quality of
the Nation's waters. To be able to develop and implement TMDLs for impaired
aquatic resources, it is imperative to determine the sources of the contamination.
One tool used to determine the sources of bacterial fecal contamination is to
apply a microbial source tracking approach to the system of interest. Microbial
source tracking (MST) approaches are based on the assumption that specific
strains of bacteria, genetic fingerprints, or DNA-based markers are associated
with specific host species. Because accurate source identification of fecal
contamination is essential in MST, more sensitive, selective and reliable
molecular markers are required. The two types of genotypic methods that have
been applied widely in a variety of environments can be classified as library-
independent (LI) and library-dependent (LD). For both types, the temporal and
spatial stability of selected genotypes are aspects that need to be evaluated, and
these aspects are often times missing when applying MST to environmental
samples. LD-MST methods require the development of large databases
comprised of source-specific isolates. Once a source-specific fingerprint has
been identified, the temporal and spatial variability of that particular genotype still
needs to be validated. LI-MST is based on the application of culture-independent
methods such as amplification of DNAfrom environmental samples using 16S
rDNA markers in combination with polymerase chain reaction (PCR). However,
cross-reactivity of some of the 16S rDNA markers used in field studies has
prompted the development of alternative PCR assays using metagenomic
markers specific for bovine feces. In this study, we report on the comparison of
selected LD and LI methodologies, their usability as rapid and reliable methods
for developing and applying markers to various environmental scenarios, and the
stability of these markers under various spatial and temporal conditions. From
our results, we concluded that library production is highly time and resource
consuming. Its application is probably appropriate in very specific scenarios
where discrimination among a few, selective sources is necessary. In contrast,
application of DNA, PCR-based markers yielded fairly rapid results and has the
capability to screen multiple scenarios in a short period of time. Once stability
and cross-amplification aspects have been addressed, this latter method can be
a highly efficacious approach to determine sources of contamination in a variety
of scenarios.
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Table of Contents
ENVIRONMENTAL ISSUE 6
RESEARCH GOALS 8
DESCRIPTION OF METHODS USED IN THIS RESEARCH 8
AMPLIFIED FRAGMENT LENGTH POLYMORPHISM 8
Box-PCR ANALYSIS 10
HOST SPECIFIC 16S-RDNA MARKERS 11
METAGENOMIC MARKERS 12
GENERAL RESEARCH APPROACH 13
METHODOLOGY 15
SAMPLING LOCATIONS FOR16S-RDNAAND METAGENOMIC MARKERS 15
SAMPLE COLLECTION 18
PHYSICO-CHEMICAL AND MICROBIOLOGICAL METHODS 18
DMA EXTRACTION AND PCR AMPLIFICATION 19
SOURCES FOR AFLP AND BOX-PCR ANALYSIS 21
AFLP AND BOX-PCR PROCEDURES 21
RESULTS AND DISCUSSION 22
EVALUATION OF LIBRARY-INDEPENDENT METHODS 22
Comparison of 16S rDNA-based vs. metagenomic marker performance in farms impacted by cattle
contamination 22
Relationship between enterococci enumeration and the occurrence of molecular
markers 24
Evaluation of human specific 16S-rDNA markers in freshwater streams impacted by rural non-
point sources in Puerto Rico 28
EVALUATION OF LIBRARY-DEPENDENT METHODS 34
Seasonal Distribution of Enterococci Isolates 34
Some methodological considerations developing the phytogeny of Enterococcus strains using
AFLP 35
Phytogeny of E. faecalis, E. hirae ,andE. casseliflavus strains using AFLP. 36
Comparison of AFLP and BOX-PCR analysis 39
CONCLUSIONS 41
SIGNIFICANCE OF RESEARCH 43
FUTURE DIRECTIONS 44
REFERENCES 45
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List of Figures
Figure 1 Main steps of the AFLP Procedure 10
Figure 2 Experimental scheme to perform 16S rDNA and metagenomic marker
analyses 14
Figure 3 General experimental design to isolate and fingerprint enterococcal
species 15
Figure 4 Four sites were sampled in Farm 1, three sites were located along the
stream while site 4 was located in a pond used by the cattle for bathing and
drinking. Aerial photo courtesy of GlobeXplorer.com 16
Figure 5 Sampling sites related to WS2. Sites 1-7 are located within the farm
boundary, 8 and 9 are located in a buffer zone between the farm and a
subdivision, and 10-12 are located within a subdivision. Aerial photo courtesy of
GlobeXplorer. com 17
Figure 6 Relationship between the monthly enterococcal counts and the average
frequency of the DMA markers per month in WS1 27
Figure 7 Relationship between monthly enterococcal counts and the average
frequency of the DMA markers per month in WS2 28
Figure 8 Seasonal distribution of enterococcal species in impacted streams by
cattle contamination 34
Figure 9 Enterococcus hirae phylogenetic tree derived from AFLP fingerprints .38
Figure 10 Typical BOX-PCR gel image produced with E. faecalis isolates 40
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List of Tables
Table 1 Frequency (±95% Cl) of 16S rDNA-based Bacteroides and metagenomic
markers in water samples from two watersheds affected by cattle contamination.
Watershed 1 (WS1) receives direct impact from cattle, while watershed 2 (WS2)
only receives contamination through runoff. Only markers with a frequency
between 0.10 and 0.90 were used for the logistic regression analysis 24
Table 2 Enterococcal abundance {CFU/100ml) in Watershed 1 and Watershed 2.
Sites were divided based on influence by cattle or type of water resource
(streams vs. ponds) 26
Table 3 Description of samples collected in the Rio Anasco Basin, Anasco,
Puerto Rico from August 3-14, 2006 29
Table 4 Bacteroidetes 16S rRNA gene marker hits in water samples collected in
the Rio Anasco Basin, Anasco, Puerto Rico. The numbers indicate the times the
individual primer set was found in each water sample after one amplification
round (1x) 32
Table 5 Comparison of the advantages and disadvantages of the BOX-PCR and
AFLP methodologies 41
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Environmental Issue
The U.S.EPA TMDL 303(d) list fact sheet has indicated that fecal pollution
is the #1 cause of impairment in most states, accounting for up to 13% of all
reported impairments. Cost-effective development and implementation of
TMDLs for impaired aquatic resources requires the rapid and accurate
determination of the sources of contamination. Commonly used microbial water
quality assessment methods measure densities of fecal indicator bacteria, but do
not provide information on the possible sources of contamination producing the
elevated indicator concentration.
One tool used to determine the sources of bacterial fecal contamination is
to apply a microbial source tracking approach to the system of interest. Microbial
source tracking (MST) is based on the assumption that specific strains of
bacteria, genetic fingerprints, or DMA-based markers are associated with specific
host species. Because accurate source identification of fecal contamination is
the objective of MST, more sensitive, selective and reliable molecular markers
are required. The two types of genotypic methods that have been applied widely
in a variety of environments can be classified as library-independent (LI) and
library-dependent (LD). For both types, the temporal and spatial stability of the
selected genotypes are aspects that need to be evaluated, and those aspects
are often times not well characterize when applying MST to environmental
samples. LD-MST methods require the development of large databases
comprised of source-specific isolates (Ritter et a/., 2003; Wiggins et al., 2003).
Once a source-specific fingerprint has been identified, the temporal and spatial
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variability of that particular genotype still needs to be characterized. LI-MST is
based on the application of culture-independent methods such as amplification of
DMA from environmental samples using polymerase chain reaction (PCR). One
of the genes that has been widely used for this application is the gene coding for
the 16S rRNA, that has been demonstrated to have host specificity (Bernhard &
Field, 2000; Layton et al., 2006). However, one drawback of this technique is a
degree of cross-reactivity observed with some of the 16S rDNA markers when
used in field studies because they target highly conserved 16S regions (Shanks
et al., 2006; Shanks et al., 2007). This cross-reactivity prompted the development
of alternative PCR assays using metagenomic markers specific for bovine feces
(Shanks et al., 2006b). These bovine metagenomic markers were successfully
tested in the latter study with little cross reactivity on a large number of bovine
feces collected from a variety of locations across the U.S. However, although
some spatial variability was covered in that study, a more detailed evaluation of
the temporal and spatial variability of the markers was still required to determine
their environmental stability and robustness.
This research supports the second long-term goal (LTG 2) established in
ORD's Water Quality Multiyear Plan for the protection of watersheds and aquatic
communities: "provide the tools to assess and diagnose impairment in aquatic
systems and the sources of the associated stressors".
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Research Goals
The overall objective of this research was to evaluate the temporal and
spatial applicability of DMA-based techniques and markers to identify sources of
fecal contamination in a variety of environmental scenarios.
Description of Methods Used in this Research
The methods evaluated were divided between library-dependent (LD) and
library-independent (LI) approaches. The library dependent methods were used
with a library of enterococci markers isolated from cattle farms (Molina et a/.,
2007). The two LD methods included amplified fragment length polymorphism
(AFLP) and repetitive fragment polymerase chain reaction (PCR) with Box-PCR
primers (Box-PCR). Two LI-PCR methods were also compared: 16S rDNA-
based Bacteroidales markers and metagenomic markers, both specific to cattle.
Amplified fragment length polymorphism. AFLP consists of selective
amplification of restriction fragments resulting from the digestion of total genomic
DMA using PCR. The technique has the capability to inspect an entire genome
for polymorphisms and is highly reproducible. Molecular genetic polymorphisms
are identified by the presence or absence of fragments after restriction and
amplification of genomic DMA. AFLP involves four basic steps after DMA
extraction from pure cultures: DMA digestion with restriction enzymes; ligation
with oligonucleotide adapters; selective amplification with labeled primers; and
gel-based analysis of amplified fragments. See Figure 1 for a representation of
the procedure. Characteristics of the AFLP procedure include: PCR and
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fragment analysis are relatively fast to perform if using automated machines; the
entire genome is inspected for polymorphic fragments; uses small amounts of
genomic DMA and the DMA concentration does not affect the reproducibility of
the assay; provides 50 to 200 fragments per genome assayed allowing for easy
identification of polymorphisms; is highly reproducible; and taxon-specific primer
sets are not required (commercially available primers work with a large variety of
genomes). In addition, the technique can be applied to a large variety of DMA
samples including plants, animals, human, and microbial genomes. Some of the
most common applications have included generating high resolution genetic
maps in plants and animals, analysis of the genetic diversity in plants and
animals, characterization of mammalian genotypes, genotypic analysis and
epidemiological typing of bacteria, genotypic classification of fungi, and the
characterization and classification of pathogens (Blears et a/., 1998).
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of
adapters
Ligated
Gel
Amplified
fragments
Adapted from Pfaller, 2002
Figure 1 Main steps of the AFLP Procedure
Box-PCR Analysis. BOX-PCR is another PCR-based DMA fingerprinting
technique based on amplification of the interspersed repetitive sequences (rep-
PCR) found in the DMA of many bacterial species (Koeuth et a/., 1995). The
BOX element originally described for Streptococcus pneumoniae consists of
three, highly conserved, interspersed, repetitive sequences: boxA, boxB, and
boxC (Martin et a/., 1992) that contain 59, 45 and 50 basepairs in length,
respectively. BOXA1R and BOXA2R primers are based on the boxA sequence,
and have been widely applied for rep-PCR amplification of DMA from a wide
variety of bacterial species (Koeuth et a/., 1995), including Enterococcus. A
comparison of BOX-PCR to pulse field gel electrophoresis (PFGE), identified as
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the gold standard for Enterococcus sp. fingerprinting, indicated that both
techniques yield very similar results at the subspecies level for Enterococcus
faecalis (Malathum etal., 1998).
Host specific 16S-rDNA markers. The majority of molecular tools
currently being applied for microbial source tracking rely on the development of
an extensive library of cultured isolates to which DMA fingerprints from
environmental samples can be compared. The two aforementioned methods fall
into this category. LD methods are labor-intensive and limit the target indicator
bacteria to those that can be readily grown in a laboratory and can also survive
outside the intestine (Simpson et a/., 2002). Combining technological advances
in molecular biology, such as polymerase chain reaction (PCR) and 16S rDNA
gene sequence analysis, has provided powerful tools for characterizing microbial
populations without the need for cultivation of the targeted indicators. These
combined techniques have become very useful for of MST application. For
example, PCR amplification of 16S rRNA gene sequences of the genera
Bacteroides-Prevotella has proven useful for the identification of specific hosts,
such as human, cattle, horses, and pigs (Allsop & Stickler, 1985, Bernhard &
Field, 2000b, Dick et a/., 2005, Kreader, 1995). These anaerobic bacteria are
restricted to the intestinal environment of warm-blooded animals. Unlike some
other fecal coliform bacteria, these Bacteroidetes do not survive long in water,
and make up 30 to 40% of the total fecal bacteria (Harmsen et a/., 1999; Layton
et a/., 2006), which could account for up to 10% of the fecal mass. Therefore,
these anaerobic bacteria could be used as suitable indicators of species-specific
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contamination. Prior to the development of culture-independent molecular
methods, the use of Bacteroides as indicators was limited because of the
difficulty to grow them in culture.
Metagenomic Markersln addition to PCR amplification of specific 16S
rRNA genes, recent development of another culture-independent technique,
genome fragment enrichment, also seems promising for selecting for host-
specific metagenomic markers (Shanks et a/., 2006a). This technique enriches
for genes that are specific in host organisms by subtracting the genes that are
common in other organisms. The metagenomic approach not only targets the
16S rRNA gene, but all genes involved in bacterial-host interactions, such as
surface proteins (Shanks et a/., 2006a). One drawback that the 16S rRNA gene
of Bactero/ctes-like species seems to have is its cross-reactivity with non-target
fecal sources (Lamendella et a/., 2007). This is especially true for the cattle-
specific markers. The metagenomic markers developed for bovine sources are a
good alternative that could possibly reduce the identification of false positives
due to that cross-reactivity. The bovine metagenomic markers developed by
Shanks et al. (2006b) were successfully tested with minimal cross reactivity on
148 different bovine feces collected from a variety of locations across the U.S.
However, although the latter assays were tested against fecal samples obtained
from different regions, more detailed site tests of the temporal and spatial
variability of the markers are still required to determine their environmental
stability and robustness.
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General Research Approach
This research was divided into two general approaches:
• Evaluation and comparison of the presence of 16S-rDNA and
metagenomic markers in both water and sediment samples collected from
two watersheds associated with cattle farms under different management
practices (see Figure 2), and from a rural community serviced by
individual household septic wastewater treatment systems.
• Comparison of amplified fragment length polymorphism (AFLP) and
repetitive polymerase chain reaction with BOX-primer (BOX-PCR)
methodologies to genotype an Enterococcus sp. source library, and
determine the usability of each methodology for host-specific source
identification (see Figure 3).
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water
sediment
feces
filtration enterococci counts
DMA extraction
PCR amplification
Statistical analyses
Figure 2 Experimental scheme to perform 16S rDNA and metagenomic marker
analyses.
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Two cattle
farms
located in
north east
Georgia
First order
streams
with
headwaters,
7 sampling
locations
per stream
Cattle have
unrestricted
access to
stream
5 cow patties/farm
t
Water addition and
membrane filtration
Quantification and isolation
of 35 suspected enterococci
Isolation of 50 suspected
enterococci
Identification of individual species using multiplex PCR
AFLP and BOX-PCR Analysis
Figure 3 General experimental design to isolate and fingerprint enterococcal
species
Methodology
Sampling locations for 16S-rDNA and Metagenomic Markers. The
study sites to compare the metagenomic markers and 16S-rDNA primers
consisted of two watersheds associated with cattle farms. Watershed 1 (WS1)
flows across Farm A located in Madison County, GA. Watershed 2 (WS2) starts
in Farm B located in USDA-owned land in Watkinsville, GA. In WS1, samples
were collected from 4 sites along a creek and a pond (Figure 4).
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Figure 4 Four sites were sampled in WS1 (Farm 1), three sites were located along the
stream while site 4 was located in a pond used by the cattle for bathing and drinking.
Aerial photo courtesy of GlobeXplorer.com.
Cattle had direct access to all sampling sites except for site 1 that was
located upstream from the farm, outside of the property fence approximately 0.13
miles downstream from the origin of the stream. Sites 2 and 3 were located in the
middle and end of the stream crossing the farm, respectively. Site 4 was located
in a pond used by the cattle for drinking and bathing. On average, 60 head of
cattle were present on the farm during the course of this study. Wildlife, such as
geese and deer, also could contribute to the fecal sources impacting both water
bodies in this farm.
There were 12 sampling sites in WS2; seven of the sites were located
along the headwater stream and a pond within Farm B, while five sites were
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located in the same creek downstream, outside the farm (Figure 5). Sites 5 and
11 consisted of agricultural and community ponds, respectively. An average of
140 head of cattle were kept and rotated among 16 fenced pastures in Farm B
during our study. The cattle had no access to the stream or the pond at this farm.
Other possible fecal sources affecting the stream and ponds in this watershed
include wildlife such as deer, geese and raccoons. Neither watershed was
deemed significantly impacted by human fecal pollution.
Figure 5 Sampling sites related to WS2 (Farm B). Sites 1-7 are located within the farm
boundary, 8 and 9 are located in a buffer zone between the farm and a subdivision, and 10-
12 are located within a subdivision. Aerial photo courtesy of GlobeXplorer.com.
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A separate study site was selected to evaluate the human-specific 16S-
rDNA markers. The site consisted of a rural community located in the town of
Anasco, Puerto Rico. Water from five sampling locations was collected over a
two week period. The sampling locations included one site along an intermittent
creek that crossed the community; three sites were located in the Casey River
basin (upstream and downstream from the community); and one site consisted of
a shallow well (30 feet).
Sample collection. For cattle primers, water and fecal samples were
collected on a monthly basis between September 2005 and February 2007.
Water samples were collected in sterilized 1-liter bottles, kept on ice for transport
to the laboratory and processed for enterococci enumerations and nucleic acid
extractions within 6 hours after collection. Two fecal samples per sampling event
were collected aseptically from each farm. Fecal samples were stored at -20°C
until processed.
The samples collected to test the human-specific Bacteroidetes primers
were collected in collaboration with an ongoing study sponsored by the Puerto
Rico Water Resources and Environmental Research Institute in an effort to
provide information for the development of TMDLs for the Rio Anasco. Water
samples (100, 250, and 500 ml) were filtered through polycarbonate filters (0.2
urn). The filters were transferred to microcentrifuge tubes and stored at -20° C,
then shipped on ice overnight to our laboratory.
Physico-chemical and microbiological methods. The temperature and
pH of water samples were measured on-site using a portable pH meter, Orion
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250A plus (Thermo Orion, Beverly, Mass.). Daily precipitation data for WS1 and
WS2 were obtained from station ID 092517 of the National Oceanic and
Atmospheric Administration (httBi/liym^ and the
Georgia Automated Environmental Monitoring Network
respectively. Water sample turbidity was
measured using a 2020 Turbidimeter (LaMotte Co., Chesterfield, MD) according
to the manufacturer's instructions. Enterococcal densities of the water samples
were determined using the membrane filtration technique described in EPA
method 1600. The colonies were counted twice after 24 and 42 hour incubation
at41°C.
DMA Extraction and PCR amplification. In both WS1 and WS2,
approximately 100 ml water samples, and 0.2-0.25 g of cattle feces were used
for DNA extractions using an UltraClean Soil DNA Kit (MoBio Inc., California)
according to the manufacturer's instructions with some modifications.
Specifically, water samples were filtered onto polycarbonate filter membranes
(0.22 urn; Millipore Inc., Bedford, MA). Each filter was then transferred to a 6 ml
sterile tube containing bead solution and solution S1, and vortexed for 10 min.
Inhibitor removal solution (IRS) was added after solution S2, followed by the
steps in the manufacturer's instructions. The nucleic acid fraction was eluted to
65 ul of Tris-EDTA buffer. DNA was quantified photometrically using a NanoDrop
ND-1000 UVA/is spectrophotometer (NanoDrop Technologies, Wilmington, DE),
and the DNA concentration was adjusted approximately to 10 ng/ul.
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The Puerto Rico water samples were also extracted using the MoBio kit.
The primers used included a general Bacteroides-Prevotella marker (32F or Gen-
Bac), two human-Bacteroides markers (HF183 and HF654), and two cattle-
Bacteroides primers (CF128 and CF193). Gels were examined on 1.5%
agarose, mostly for 90 min at 90volts, with one or two exceptions at 100 volts for
60min.
PCR assays were performed using GoTaq Green master mix (Promega,
Madison, Wl) with either 16S rDNA-based Bactero/cfa/es-specific primer sets or
six cattle-specific metagenomic primer sets. The annealing temperature for each
PCR assay was determined using a gradient PCR. The thermal cycling
conditions for the 16S rDNA-based markers were an initial denaturation of 2.5
min at 94°C, followed by 30 cycles of denaturation at 94°C for 30 sec each,
annealing at an optimized temperature for each primer set for 30 sec plus
extension at 72°C for 1 min, and a final extension of 5.5 min at 72°C. The thermal
cycling conditions for the metagenomic markers were 3 min of initial denaturation
at 94°C, followed by 35 cycles of 1 min each of denaturation (94°C), annealing
and extension (72°C), and a final extension step of 5 min at 72°C. Amplification
products were visualized on a 2 % agarose gel stained with 0.2X SYBR Safe
DMA gel stain (Invitrogen). The limit of detection for each molecular marker set
was determined by PCR using serial dilutions of the extracted bovine fecal DMA
as templates, starting at 10 ng/ul. Negative controls included DMA extracts from
sterilized nanopure water and no DMA template reactions, while DMA extracts
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from feces freshly obtained at each sampling event were used as positive
controls.
Sources for AFLP and BOX-PCR Analysis. The library of enterococci
used for the fingerprinting analyses consisted of 1600 isolates collected over a
seasonal cycle at two separate bovine farms where cattle had unrestricted
access to the streams at all times (Molina, 2005). Samples were collected from
pre-farm (non-impacted, upstream-from-the-farm) stream sites, farm stream sites
(impacted), and fecal matter. The library of enterococcal species was developed
by isolating colonies from mEI plates and identifying them at the species level
using a multiplex PCR procedure (Jackson et a/., 2004).
AFLP and BOX-PCR Procedures. Genomic DMA extraction from each
Enterococcus isolate was performed using a Qiagen DNeasy Tissue Kit. The
AFLP procedure was adapted from (Antonishyn et a/., 2000). AFLP restriction
and ligation was performed using /-//ndlll, and Mbo\. Digested genomic DMA was
amplified in parallel reactions using two different selective primer sets, Mbol-AC
and Mibol-CTG. The BOX-PCR procedure was an adaptation from (Malathum et
a/., 1998). BOX-PCR Amplification was performed using Gitschier buffer (pH
8.0) (Kogan et a/., 1987), and a BOXA2R primer. To perform fragment analysis,
the PCR products were electrophoresed through 6% polyacrylamide denaturing
gels with a well-to-read distance of 30 cm for 3 hours on a MJ Research
BaseStation 51 DMA Fragment Analyzer. The parallel reactions were run on
separate gels with a custom size standard in each lane (BioVentures), allowing
accurate sizing of fragments in the 50-600 bp range. For the phylogenetic
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analyses, we analyzed gel images using BioNumerics v3.0. Dendrograms were
created from the Mbol-AC and Mbol-CTG fingerprints using a curve-based
similarity coefficient (Pearson correlation) with the unweighted pair group method
(UPGMA).
Results and Discussion
Evaluation of Library-Independent Methods
Comparison of 16S rDNA-based vs. metagenomic marker
performance in farm waters impacted by cattle fecal contamination. The
general 16S rDNA marker (32F) was detected in all sampling sites at a very high
frequency (81%), except in the sites related to the ponds or their effluents (17%)
(Table 1). This general marker was followed in frequency order by the cattle-
specific 16S rDNA marker (CF128), and then by the metagenomic markers Bac
2,1,5, and 3. The metagenomic markers as a whole were found to be 41 -60%
less frequent than the 16S rDNA cattle marker in stream waters under direct
impact (WS1), and between 3-5% less frequent in stream water under indirect
impact (WS2), depending on the sampling site. These results suggest that the
metagenomic markers are less sensitive than the 16S-rDNA based markers, they
are less stable in the environment, or their presence in cattle is more variable.
The fact that the metagenomic markers were not found in every single cattle
patty sampled at a given time (data not shown) points to the possibility of a
higher variability in cattle manure, but it does not discard the other two
possibilities. Nevertheless, the CF 128 marker was found in relatively low
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frequencies (3-9%) in WS2 versus the directly impacted stream of WS1 (71-94%)
(Table 1). This indicates a rather low impact of cattle fecal contamination
reaching the stream water in WS2 through run-off compared to the direct inputs
in WS1, even though enterococci numbers were rather high in both farm
streams.
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Table 1. Frequency (±95% Cl) of 16S rDNA-based Bacteroides and metagenomic markers
in water samples from two watersheds affected by cattle contamination. Watershed 1
(WS1) receives direct impact from cattle, while watershed 2 (WS2) only receives
contamination through runoff. Only markers with a frequency between 0.10 and 0.90 were
used for the logistic regression analysis
Marker Frequency ±95% Cl
Site Bac32F CF128F Bad Bac2 Bac3 BacS
WS1-1 0.76 ±0.09
WS1 -2, 3 1.00 ±0.00 0.94 ±0.02 0.62 ±0.08 0.65 ±0.08 0.26 ±0.06 0.59 ±0.08
WS1-4 0.88 ±0.05 0.71 ±0.10 0.12 ±0.05 0.18 ±0.07 0 ±0.00 0.12 ±0.05
WS2-1-4 0.72 ±0.05 0.09 ±0.02 0.03 ±0.01 0.06 ±0.01 0.04 ±0.01 0.04 ±0.01
WS2- 5,
0.17 ±0.04 0.03 ±0.01 0 0.03 ±0.01 0 0
6,11,12
WS2- 7-
0.70 ±0.06 0.04 ±0.01 0 0.04 ±0.01 0 0
10
Relationship between enterococci enumeration and the occurrence of
molecular markers. Enterococci counts were performed for all locations where
the markers were tested to establish the relationship with the alternative markers
under the two types of farm management. The geometric mean of the
enterococcal numbers in the areas with the highest probability of cattle impact
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ranged from approximately 24 to 1924 CFU/100ml in WS2 and WS1, respectively
(Table 2). The counts taken at the ponds or pond outflows were the lowest (site
4 in WS1 and sites 5, 6, 11, and 12 in WS2), being 93 and 4 CFU/100 ml in WS1
and WS2, respectively. The upstream locations (site 1 in both watersheds),
exhibited counts of 74 and 17 in WS1 and WS2, respectively. In general, WS2
exhibited much lower counts than WS1, which was expected due to best
management practice implementation in WS2 (fencing cattle out of the stream).
When these results were compared to the observed DMA marker frequencies, no
significant statistical relationships between the monthly enterococcal counts and
the presence of the molecular markers in WS1 (Figure 6) were observed. In
WS2, the enterococcal counts were statistically compared only to the general
marker 32F due to the absence of the other markers from most sites (Figure 7).
In this case also, no significant relationship was identified between the marker
and the enterococcal counts. The only marker that indicated a slightly similar
trend to that observed with the enterococcal counts was CF 128, and this only
during a brief time of the sampling period (Dec 05-Feb 06) in WS1. However,
this relationship didn't persist during the warmer months of the year or the
following winter season.
In accordance with previous reports, enterococcal counts reported here
could not be related to the occurrence of microbial source tracking markers,
suggesting that more information is necessary to understand the dynamics of
DMA source identifiers in a watershed in relation to the densities of traditional
fecal indicators such as E. coli (Shanks et a/., 2006b) and enterococci. One
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possible explanation for the discrepancy could be the differences in the
physiological and biochemical features between the two targeted bacterial
groups. Bacteroides are strict anaerobes and have low environmental
persistence, indicating recent contamination (Fiksdal et a/., 1985, Kreader, 1998,
Oshiro & Fujioka, 1995 1995, Ott etal., 2001). Although enumeration of
enterococci provides information on the level of impairment of a system, it does
not identify the specific source of contamination (Scott, 2005). Therefore, it is
recommended to employ a combination of molecular and traditional methods in
field studies to provide more accurate and reliable results in risk assessment and
prevention or reduction of contamination.
Table 2 Enterococcal abundance {CFU/100ml) in Watershed 1 and Watershed 2. Sites were
divided based on influence by cattle or type of water resource (streams vs. ponds).
Site#
WS1 site 1
WS1 sites 2, 3
WS1 site 4
WS2 sites 1-4
WS2 sites 5, 6,
11, 12
WS2 sites 7-10
Geometric mean
74
1924
94
26*
4*
123*
95% Confidence Interval
Lower bound
9
1130
42
10
2
59
Upper bound
640
3275
207
68
9
257
Zero values in the data were converted to 0.01.
26
-------
100 -,
90 -
80 -
70 -
60 -
50 -
40 -
30 -
20 -
10 -
0 -
Enterococci counts vs. molecular markers in WS1
N
\
J2
o
o
tf
Month
ICF178F
Bac3 ^3 Bac5 -*-Ent. count
Figure 6 Relationship between the monthly enterococcal counts and the average
frequency of the DMA markers per month in WS1
27
-------
Enterococci counts vs. Molecular markers in WS2
1400
Month
Bac32F
CF128F
lBac2
Bac3
iBacS
•Ent. count
Figure 7 Relationship between monthly enterococcal counts and the average frequency of
the DMA markers per month in WS2
Evaluation of human-specific 16S-rDNA markers in freshwater
streams impacted by rural non-point sources in Puerto Rico. In this set of
samples, each primer group was run as follows: Gen-Bac 32F and HF654 -four
times each; primer HF 183 - six times; and primers CF128 and 193 - twice each
with the objective to determine whether the locations sampled were impacted by
either human (HC) or cattle fecal contamination (CC).
28
-------
Table 3 Description of samples collected in the Rio Ahasco Basin, Ahasco, Puerto Rico
from August 3-14, 2006
0 . _ . Site Description, volume of sample filtered, or sampling
bampie uoae
Site A Bridge 406
Site B Bridge 430
Site C Shallow well near Bridge 430
Site D Intermittent stream crossing community
Site E Casey River
wrr* rl win Centrifuged sludge from Athens Water Treatment Plant
Samples 1,4,7,10,13 500 ml of water filtered
Samples 2, 5, 8, 11, 14 250 ml of water filtered
Samples 3,6,9,12,15 100 ml of water filtered
Samples 1 ,2,3 Sampled on 8/3/06
Samples 4,5,6 Sampled on 8/5/07
Samples 7,8,9 Sampled on 8/7/06
Samples 10,11,12 Sampled on 8/9/06
Samples 13,14,15 Sampled on 8/14/07
*When CF primers were tested, WTC was substituted for Cow Fecal DMA as positive control.
When indicated, 2x means that 1 ul of PCR product from a first round (x1 )
was used as template for a second round (x2). Many of the (x2) gels had some
non-specific banding, however, when the correct band size for the primer listed
was present, the gel was scored with a (+); those gels without the correct band
size, but with non-specific bands, were labeled "M" for multiple bands. Results
were scored as Clean, Human, Cow, and Human-Cow, based on the PCR
results.
29
-------
Although 2x amplification assays can increase the signal in those cases
where low initial concentrations of the target DMA are present, our results were
not considered solid enough due to the fact that some gels exhibited a large
number of non-specific bands. Further confirmation, for example through
sequencing of the bands obtained in the 2x amplification, would be necessary to
confirm the presence of the target DMA.
Results for the 1X runs are presented in Table 4. Although the human
primer was not amplified in all samples collected from sites A and B, the results
indicate that human contamination seems to be present in the system at some
level during most sampling dates. The fact that human contamination was not
indicated in every single sample could indicate a low level of contamination or
presence of inhibitors in some samples. The level of contamination is hard to
assess without a real quantification assay. Only once during the sampling period
(8/5/06) did the results indicate that cattle contamination was present in one of
the sampling locations (site B). The presence of the Gen-Bac in the absence of
cattle or human contamination may point to another source of contamination
(neither human nor cattle), or cross-amplification with a natural bacterial
population.
Samples obtained from the well seem to be free from cattle and human
contamination. Only one sample gave a positive human signal, this on the last
day of sampling (out of triplicates), and might not be enough evidence to indicate
an actual human impact. In a situation like this, inadvertent sample
contamination can not be discounted. The stream that crosses the rural
30
-------
community (site D) is clearly impacted by human contamination, since that assay
was positive for every triplicate sample collected throughout the whole sampling
period. This community is served mainly by septic systems. The contamination
observed points to the fact that these septic systems might not be working
properly and are leaking into the intermittent stream traversing the community.
The Casey River also seems to be strongly impacted by human contamination,
but in this case the contamination might be intermittent, since no contamination
was detected in any of the samples collected on the last day of sampling. One
possible explanation for this observation is dilution of the assay signal in the river
or fecal bacterial decay after the initial contamination episode.
There was no relationship observed between the volume of sample filtered
and the presence of a marker, meaning that sometimes a marker was positive in
the 100 mi-sample while it was absent in the 500 mi-sample and vice versa. This
result could be a function of the amount of inhibitors present in a sample at a
given time, or it could just reflect sample randomness. Duplicate and, if possible,
triplicate sample collection is recommended to cover sample variability.
31
-------
Table 4 Bacteroidetes 16S rRNA gene marker hits in water samples collected in the Rio
Ahasco Basin, Ahasco, Puerto Rico. The numbers indicate the times the individual primer
set was found in each water sample after one amplification round (1x)
Number of Primer Set Hits
Sample
Neg Control
WT
C/CowCF*
WTD
A-1
A-2
A-3
A-4
A-5
A-6
A-7
A-8
A-9
A-10
A-11
A-1 2
A-1 3
A-1 4
A-1 5
B-1
B-2
B-3
B-5
B-6
B-7
B-8
B-9
B-10
B-11
B-1 2
B-1 3
B-1 4
B-1 5
C-1
C-2
C-3
C-4
C-5
C-6
C-7
General
Bacteroides
Marker
Gen-Bac
0
4
4
1
1
0
1
2
0
0
0
0
0
0
0
0
0
0
1
1
1
2
1
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
Human Human Cattle Cattle
Bacteroides Bacteroides Bacteroides Bacteroides
Marker Marker Marker Marker
HF-183
0
2
3
1
0
0
0
1
0
0
1
0
0
1
2
1
0
1
0
0
1
1
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
HF-654
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
CF-128
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
CF-193
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Comments:
Control
Control
Control
Human
General
Clean
General
Human
Clean
Clean
Human
Clean
Clean
Human
Human
Human
Clean
Human
General
General
Human
Human and
cow
Clean
General
Clean
General
Human
Human
Clean
Clean
Human
Clean
Clean
Clean
Clean
Clean
Clean
Clean
32
-------
C-8
C-9
C-10
C-11
C-12
C-13
C-14
C-15
D-1
D-2
D-3
D-4
D-5
D-6
D-7
D-8
D-9
D-10
D-11
D-1 2
D-1 3
D-1 4
D-1 5
E-1
E-2
E-3
E-4
E-5
E-6
E-7
E-8
E-9
E-10
E-11
E-1 2
E-1 3
E-1 4
E-1 5
0
0
0
0
0
0
0
0
2
0
0
0
1
2
2
3
0
2
2
0
1
0
0
0
0
0
2
2
0
2
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
2
0
1
2
1
1
2
2
1
2
0
1
2
2
1
1
1
2
2
2
2
1
2
1
0
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Clean
Clean
Clean
Clean
Clean
Clean
Clean
Human
General
Human
Human
Human
Human
Human
Human
Human
Human
General
Human
Human
Human
Human
Human
Human
Human
Human
Human
Human
Human
Human
Human
Clean
Human
Human
Clean
Clean
Clean
Clean
33
-------
Evaluation of Library-Dependent Methods
Seasonal Distribution of Enterococci Isolates. Application of
Pearson's chi-squared statistics to our data indicated that the proportions of the
different bacterial species varied seasonally. However, this variability did not
stand a monthly statistical distribution analysis, which means that the differences
observed were due to sample randomness rather than true seasonal differences.
Nevertheless, some general trends were identified with E. faecalis and E. hirae,
although not with E. casseliflavus. E. faecalis seems to be the only species
showing a trend of higher occurrence frequency during the warmer months of the
sampling period, April through November 2004 (Figure 8). E. hirae was present
more commonly during colder months (spring, fall and winter). E. casseliflavus
indicated no correlation with season, farm sample site or source of sample (water
vs. manure). In addition to the behavior of the three former species, E. faecium
was found in higher abundance only during the winter of 2005. The winter
months also reflected the highest diversity in terms of number of species
identified and the evenness of the different populations.
100%n
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
D other
B flavescens
• mundtii
n hirae
D durans
• faecium
D faecalis
• casseliflavus
Fall 2003 Spring 2004 Summer 2004 Fall 2004 Winter 2005
Figure 8 Seasonal distribution of enterococcal species in impacted streams by cattle
contamination
34
-------
Some researchers have suggested that species such as E. faecalis can be
used as markers for human contamination (Wheeler et al., 2002). However,
results from this research suggest that the large seasonal variability exhibited by
the different enterococcal populations identified make the use of individual
Enterococcus species unreliable due to lack of temporal stability. The observed
variability, combined with the observed presence of the same Enterococcus
species in the cattle farm stream water and the water upstream from the farm,
highlights the fact that enterococci populations are widespread in nature. This
could make their use as markers at the species level undependable. To evaluate
the suitability of Enterococcus at the subspecies level to serve as markers of
bovine contamination, it was necessary to perform fingerprinting analysis of
some of those subspecies that were observed to be present in the cattle farm
streams throughout the year.
Some methodological considerations developing the phytogeny of
Enterococcus strains using AFLP. The two primer sets, Mibo/-CTG and Mbol-
AC, for Hex and Fam, respectively, exhibited congruencies of up to 60%. The
40% incongruence can be explained, in part, by the dynamic phylogeny
produced due to the high species diversity in the library. Detailed analysis using
band matching and maximum parsimony will need to be performed in order to
obtain more detailed information. The phylogenetic trees produced by each
primer set for E. faecalis yielded the greatest incongruence; however, the two
primers produced the same basic groupings for both E. hirae and E.
35
-------
casseliflavus. These results suggest that E. faecalis exhibits the highest species
diversity in the environment among the three species studied.
Phylogeny of E. faecalis, E. hirae ,and E. casseliflavus strains using
AFLP. Using primer set Mbol-CTG, E. faecalis isolates separated into two
distinct clusters depending on the farm from which they were isolated. Other
than the farm differences, isolates were not found to group by source (manure
vs. water), season, or location (stream sites within the farm or stream sites
upstream from the farm). One possible explanation for this division is that cattle
uptake part of their E faecalis fecal population from their drinking water.
Because their drinking water includes the upstream-from-the-farm water, this
possibly explains why the E faecalis isolated from manure could not be
differentiated from that isolated from the upstream water. This observation also
implies that the E. faecalis population present in the wildlife inhabiting each farm
differs from each other, since no similar fingerprints were identified across farms.
E hirae also showed two distinct clusters, one containing isolates mainly
collected during autumn 2003 in Farm 1 from manure, and the other comprised
of isolates from all seasons and sources (Figure 9). The autumn 2003 cluster
was not observed at any of the upstream locations, suggesting that it is
composed of species mostly present in the feces of the cattle on Farm 1. This
cluster is in close phylogenetic relationship to a spring cluster found from both
farms, composed of isolates obtained mostly from the water within the farms, but
absent in the water collected upstream of the farms. Because the fingerprints in
these two clusters are absent in the water upstream from the farms, they could
36
-------
be developed and tested as MST markers for cattle fecal contamination.
However, one drawback observed is the fact that these two clusters only
showed-up during autumn and possibly spring, but not during other times of the
year. This makes them temporally unstable and unreliable. A good indicator
needs to be present throughout all seasons (Simpson et a/., 2002).
E. casseliflavus isolates also grouped into two basic clusters, with one
cluster accounting for 73% of the library. For this species, no seasonal or source
trends were observed, and many fingerprints were found in the water upstream
of the farms. In addition, no difference was observed between farms. These
results suggest that E. casseliflavus fingerprints are widespread in the
environment, making it hard to distinguish contributions of cattle vs. wildlife.
37
-------
Oms:
bpnaf
Spring
Spring
Spring
Spring
Spring
Spring
Sprifli
WinJet
Wisfif
WinJet
SprifU;
WinJet
Wist!
Ssia,g
Wins:
AMtiiBm
Spring
Wieti
»llit!
S:iaarn%3
AXKITCL
Sttrbig
Stria Ji
Wifcto
Sfrriag
Sittrysg
StTOEg
Srsiitf
Sfrriag
Atftan
AKSMK
Atitaii
Aii&ji)
AuJian
Astan
Axton
Aiikmi
WiilB
Axton
Att&un.
F
F
F
I
F
F
F
F
F
1
F
F
F
F
F
F
F
PF
F
F
F
F
F
F
IT
F
I
F
f
F
F
f
F
F
F
I
F
F
F
F
F
F
F
F
F
I
F
F
M
M
M
i
M
I
M
i
M
I
i
i
i
M
1
1
M
M
M
i
i
i
i
i
M
i
i
1
M
M
i
M
M
y
M
y
M
y
M
y
M
M
I
iwaoof
Figure 9 E. hirae phylogenetic tree derived from AFLP fingerprints of isolates obtained
from water and manure samples collected at two cattle farms with impacted streams.
38
-------
Comparison of AFLP and BOX-PCR analysis. The genotyping methods
of BOX-PCR and AFLP each have distinct advantages and disadvantages (Table
5). Our results showed that, in general, AFLP is far superior at discriminating
closely related strains of Enterococcus. AFLP produced a greater number of
bands per PCR reaction, providing greater discriminatory power; had greater
precision in band sizing; and allowed for the use of multiple primer sets.
Additionally, the quality of the AFLP gels was very consistent in terms of both
band reproducibility and overall gel usability for data analysis. Throughput of
samples was also greater with AFLP due to the high sensitivity of the
fluorescence based-detection, thereby allowing the use of much smaller band
lane widths.
The advantages of BOX-PCR are: the simplicity of the method (fewer
steps, technically easier); much lower cost of equipment (only a regular thermal
cycler is required) and reagents (BOXA2R primer, enzymes and buffer); and no
production of hazardous waste. However, the procedure produced highly
variable results in terms of band detection. It also produced lower discriminating
power than the AFLP procedure because for most species we were only able to
obtain between 9 and 18 different bands (Figure 10). In contrast, the AFLP
analysis consistently produced over 100 bands. The BOX-PCR procedure was
also highly sensitive to the buffer pH, which can affect band detection. In
addition, sensitivity and band brightness was highly affected by gel quality.
39
-------
Figure 10 Typical BOX-PCR gel image produced with E. faecalis isolates.
40
-------
Table 5 Comparison of the advantages and disadvantages of the BOX-PCR and AFLP
methodologies
Methodology
BOX-PCR
AFLP
Disadvantages
Advantages
Disadvantages
Advantages
Poor consistency
in gel quality
(affects band
'brightness' or
sensitivity).
Poor
reproducibility (pH
variability affects
band detection;
high PCR assay
variability).
Low band sizing
precision (inability
to discern similarly
sized bands).
Requires certain
gel lane width for
accurate detection
(reduces
throughput).
Assay produces
fewer bands (low
discriminatory
power).
Inexpensive (no
expensive
equipment,
primers, or
standards).
Technically
simple.
No hazardous
waste.
Expensive
(machine, primers,
and standard).
Technically more
challenging (more
steps).
Hazardous Waste.
Much greater
consistency in gel
quality (although
not perfect, very
sensitive).
High
reproducibility.
Very high band
sizing precision.
High throughput.
Many bands (high
discriminatory
power).
Option to use
different selective
primers.
Conclusions
Application of AFLP methodology vs. DMA markers. Studies
examining bacterial strain diversity and temporal variability in aquatic and
terrestrial habitats using the level of genetic specificity undertaken in this study
are uncommon. Our work helps fill this void by providing a genotyping study that
41
-------
involves hundreds of Enterococcus strains from multiple species, seasons, and
two aquatic systems, as well as a detailed temporal screening of 16S and
metagenomic markers. AFLP genotyping of our Enterococcus strain library
provided a large and robust data set, that supplied many unique fingerprints. We
identified a fingerprint of E. hirae that seems to be fairly specific to cattle manure
samples; however, the fingerprint showed-up only during two out of the five
seasons sampled. This makes the fingerprint unsuitable for MST applications
due to the lack of temporal stability and reliability. The fact that E. faecalis
isolates grouped by farm and showed no correlation to source (upstream-of-the-
farms and farm water, or manure) suggests that the cattle in our study may
uptake part of their E faecalis population from their drinking water which then
gets transferred to their manure. Such environmental uptake masks identification
of cattle-specific fingerprints of E. faecalis.
Although the AFLP methodology is very reproducible and has high
discriminating power, its application as a rapid and resource-efficient
methodology is limited because the library production is highly time and resource
consuming. Its application is probably most appropriate in very specific
scenarios where discrimination among few selected sources is necessary. In
contrast, application of DMA, PCR-based markers produced fairly rapid results,
and had the capability to screen multiple scenarios in a short period of time.
Once stability and cross-amplification aspects have been addressed, it can be a
highly efficacious approach to determine sources of contamination in a variety of
scenarios.
42
-------
From our results, we conclude that a combination of the ruminant-specific
marker, CF128F, with the metagenomic markers, Bad, 2 and 5, may provide a
solid application package for tracking bovine fecal contamination sources to
surface waters. Because enterococcal counts did not show a strong correlation
with the occurrence of any of the DMA markers, the dynamics of fecal source
tracking markers in a watershed need to be further investigated to be able to
determine their correlation with the densities of traditional indicators of fecal
contamination.
Significance of Research
This research supports an area of high priority for the Office of Water and
has been listed in the Twenty Needs Report as the highest priority for Regions
and States. This work supports assessment of aquatic systems impairment
under Long Term Goal 2 (LTG 2) of the Office of Research and Development
Water Quality Multiyear Plan. LTG2 provides the tools to assess and diagnose
the causes and pollutant sources of impairment in aquatic systems. Specifically,
the results of this research provide an evaluation of selected LI- and LD-mthods
as to their usability for early and rapid assessment of fecal contamination
sources. Included is a specific application and comparison of some of the
available DMA-based methodologies for discriminating among sources of
contamination in impaired surface waters.
43
-------
Future Directions
During the next phase of this research, we will focus on the application of the
Ll-approaches to quantify the loadings of agricultural, human and other non-
human non-point sources of bacterial contaminants into aquatic resources:
• Determine the loadings, fate and transport of bacterial contaminants from
agricultural non-point sources in surface waters using quantitative PCR
methods that will provide such information in an accurate, fast and
informed way.
• Provide a basis for comparison between traditional fecal indicators, true
pathogenic bacteria and DMA-based fecal indicators.
• Develop and validate a technique by which the recovery of an ecosystem
from bacterial contamination can be measured, and provide information to
watershed managers about the effectiveness of alternative BMP
approaches.
44
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