EPA/600/R-07/123 | December 2007 | www.epa.gov/athens 

Evaluation of Selected DNAbased 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 
EPA/600/R-07/123 

December 2007 

Evaluation of Selected DNA-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 
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 libraryindependent (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 DNA from 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. 

2
 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 FOR 16S-RDNA AND METAGENOMIC MARKERS ............................................. 15

SAMPLE COLLECTION.............................................................................................................................. 18

PHYSICO-CHEMICAL AND MICROBIOLOGICAL METHODS ........................................................................ 18

DNA 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 nonpoint sources in Puerto Rico.............................................................................................................. 28

EVALUATION OF LIBRARY-DEPENDENT METHODS............................................................................... 34

Seasonal Distribution of Enterococci Isolates.................................................................................. 34

Some methodological considerations developing the phylogeny of Enterococcus strains using 

AFLP .................................................................................................................................................. 35

Phylogeny of E . faecalis, E. hirae ,and E. casseliflavus strains using AFLP................................. 36

Comparison of AFLP and BOX-PCR analysis................................................................................. 39

CONCLUSIONS......................................................................................................................................... 41

SIGNIFICANCE OF RESEARCH........................................................................................................... 43

FUTURE DIRECTIONS ........................................................................................................................... 44

REFERENCES ........................................................................................................................................... 45

3
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 DNA markers per month in WS1 ..............................................27 

Figure 7 Relationship between monthly enterococcal counts and the average 

frequency of the DNA 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 

4
List of Tables 

Table 1 Frequency (±95% CI) 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 Añasco Basin, Añasco, 

Puerto Rico from August 3-14, 2006...................................................................29

Table 4 Bacteroidetes 16S rRNA gene marker hits in water samples collected in 

the Rio Añasco Basin, Añasco, 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

5
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 DNA-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 al., 2003; Wiggins et al., 2003). 

Once a source-specific fingerprint has been identified, the temporal and spatial 

6
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 

DNA 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”. 

7
Research Goals 

The overall objective of this research was to evaluate the temporal and 

spatial applicability of DNA-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 al., 

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 rDNAbased 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 

DNA 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 DNA. AFLP involves four basic steps after DNA 

extraction from pure cultures: DNA 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 

8
fragment analysis are relatively fast to perform if using automated machines; the 

entire genome is inspected for polymorphic fragments; uses small amounts of 

genomic DNA and the DNA 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 DNA 

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 al., 1998). 

9
Restriction Genomic DNA fragments 

Digestion with Ligation of 

Restriction enzymes adapters 

Ligated 

fragments

DNA Fingerprint 

PCR 

Gel analysis

Amplified 

fragments 

Adapted from Pfaller, 2002 

Figure 1 Main steps of the AFLP Procedure 

 

Box-PCR Analysis. BOX-PCR is another PCR-based DNA fingerprinting 

technique based on amplification of the interspersed repetitive sequences (repPCR) found in the DNA of many bacterial species (Koeuth et al., 1995). The 

BOX element originally described for Streptococcus pneumoniae consists of 

three, highly conserved, interspersed, repetitive sequences: boxA, boxB, and 

boxC (Martin et al., 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 DNA from a wide 

variety of bacterial species (Koeuth et al., 1995), including Enterococcus. A 

comparison of BOX-PCR to pulse field gel electrophoresis (PFGE), identified as 

10
the gold standard for Enterococcus sp. fingerprinting, indicated that both 

techniques yield very similar results at the subspecies level for Enterococcus 

faecalis (Malathum et al., 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 DNA 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 al., 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 al., 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 al., 1999; Layton

et al., 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 

11
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 MarkersIn 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 hostspecific metagenomic markers (Shanks et al., 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 al., 2006a). One drawback that the 16S rRNA gene 

of Bacteroides-like species seems to have is its cross-reactivity with non-target 

fecal sources (Lamendella et al., 2007). This is especially true for the cattlespecific 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. 

12
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). 

13
Figure 2 Experimental scheme to perform 16S rDNA and metagenomic marker 

analyses. 

filtration enterococci counts 

DNA extraction 

PCR amplification 

Statistical analyses 

water 

sediment 

feces 

14
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). 

2 Cattle farms

7 water 

collection sites/farm

5 cow patties/farm

Membrane filtration Water addition and 

membrane filtration

Quantification and isolation 

of 35 suspected enterococci 

Isolation of 50 suspected 

enterococci 

Identification of individual species using multiplex PCR

• Two cattle 

farms 

located in 

north east 

Georgia 

• First order 

streams 

with 

headwaters, 

7 sampling 

locations 

per stream 

• Cattle have 

unrestricted 

access to 

stream 

AFLP and BOX-PCR Analysis 

15
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 

16
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. 

17
A separate study site was selected to evaluate the human-specific 16SrDNA markers. The site consisted of a rural community located in the town of 

Añasco, 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 -20o

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 Añasco. Water 

samples (100, 250, and 500 ml) were filtered through polycarbonate filters (0.2 

μm). The filters were transferred to microcentrifuge tubes and stored at -20o 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 

18
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 (http://hurricane.ncdc.noaa.gov/dly/DLY) and the 

Georgia Automated Environmental Monitoring Network 

(http://www.georgiaweather.net/), 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 

at 41oC. 

DNA 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 μm; 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 μl of Tris-EDTA buffer. DNA was quantified photometrically using a NanoDrop 

ND-1000 UV/Vis spectrophotometer (NanoDrop Technologies, Wilmington, DE), 

and the DNA concentration was adjusted approximately to 10 ng/μl. 

19
The Puerto Rico water samples were also extracted using the MoBio kit. 

The primers used included a general Bacteroides-Prevotella marker (32F or GenBac), two human-Bacteroides markers (HF183 and HF654), and two cattleBacteroides 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, WI) with either 16S rDNA-based Bacteroidales-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 94o

C, followed by 30 cycles of denaturation at 94oC for 30 sec each, 

annealing at an optimized temperature for each primer set for 30 sec plus 

extension at 72o

C for 1 min, and a final extension of 5.5 min at 72oC. The thermal 

cycling conditions for the metagenomic markers were 3 min of initial denaturation 

at 94o

C, followed by 35 cycles of 1 min each of denaturation (94oC), annealing 

and extension (72o

C), and a final extension step of 5 min at 72oC. Amplification 

products were visualized on a 2 % agarose gel stained with 0.2X SYBR Safe 

DNA gel stain (Invitrogen). The limit of detection for each molecular marker set 

was determined by PCR using serial dilutions of the extracted bovine fecal DNA 

as templates, starting at 10 ng/μl. Negative controls included DNA extracts from 

sterilized nanopure water and no DNA template reactions, while DNA extracts 

20
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 al., 2004). 

AFLP and BOX-PCR Procedures. Genomic DNA extraction from each 

Enterococcus isolate was performed using a Qiagen DNeasy Tissue Kit. The 

AFLP procedure was adapted from (Antonishyn et al., 2000). AFLP restriction 

and ligation was performed using HindIII, and MboI. Digested genomic DNA was 

amplified in parallel reactions using two different selective primer sets, MboI-AC 

and MboI-CTG. The BOX-PCR procedure was an adaptation from (Malathum et 

al., 1998). BOX-PCR Amplification was performed using Gitschier buffer (pH 

8.0) (Kogan et al., 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 DNA 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 

21
analyses, we analyzed gel images using BioNumerics v3.0. Dendrograms were 

created from the MboI-AC and MboI-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 cattlespecific 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 

22
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. 

23
Table 1. Frequency (±95% CI) 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% CI 

Site Bac32F CF128F Bac1 Bac2 Bac3 Bac5 

WS1-1 0.76 ±0.09 0 0 0 0 0 

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, 

6, 11, 12 

0.17 ±0.04 0.03 ±0.01 0 0.03 ±0.01 0 0 

WS2- 7-

10 

0.70 ±0.06 0.04 ±0.01 0 0.04 ±0.01 0 0 

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 

24
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 DNA 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 

DNA source identifiers in a watershed in relation to the densities of traditional 

fecal indicators such as E. coli (Shanks et al., 2006b) and enterococci. One 

25
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 al., 1985, Kreader, 1998, 

Oshiro & Fujioka, 1995 1995, Ott et al., 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). 

95% Confidence Interval 

Site # Geometric mean Lower bound Upper bound 

WS1 site 1 74 9 640 

WS1 sites 2, 3 1924 1130 3275 

WS1 site 4 94 42 207 

WS2 sites 1-4 26* 10 68 

WS2 sites 5, 6, 

11, 12 

4* 2 9 

WS2 sites 7-10 123* 59 257 

* Zero values in the data were converted to 0.01. 

26
Enterococci counts vs. molecular markers in WS1

0

10

20

30

40

50

60

70

80

90

100

Dec. 2005

Jan. 2006

Feb. 2006

Mar. 2006

Apr. 2006

May, 2006

Jun. 2006

Jul. 2006

Aug. 2006

Sep. 2006

Oct. 2006

Nov. 2006

Dec. 2006

Jan. 2007

Feb. 2007

Mar. 2007

Month

% of Hits

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

No. of counts (CFU/100 ml)

CF128F Bac1 Bac2 Bac3 Bac5 Ent. count

Figure 6 Relationship between the monthly enterococcal counts and the average 

frequency of the DNA markers per month in WS1 

. 

27
Enterococci counts vs. Molecular markers in WS2

0

10

20

30

40

50

60

70

80

90

100

Dec. 2005

Jan. 2006

Feb. 2006

Mar. 2006

Apr. 2006

May, 2006

Jun. 2006

Jul. 2006

Aug. 2006

Sep. 2006

Oct. 2006

Nov. 2006

Dec. 2006

Jan. 2007

Feb. 2007

Mar. 2007

Month

% of Hits

0

200

400

600

800

1000

1200

1400

No. of counts (CFU/100 ml)

Bac32F CF128F Bac1 Bac2 Bac3 Bac5 Ent. count

Figure 7 Relationship between monthly enterococcal counts and the average frequency of 

the DNA 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 Añasco Basin, Añasco, Puerto Rico 

from August 3-14, 2006 

Sample Code Site Description, volume of sample filtered, or sampling 

date 

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 

WTC* and WTD 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 DNA as positive control. 

When indicated, 2x means that 1ul 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 DNA 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 DNA. 

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 ml-sample while it was absent in the 500 ml-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 

Añasco Basin, Añasco, 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 

 

General 

Bacteroides 

Marker 

Human 

Bacteroides 

Marker 

Human 

Bacteroides 

Marker 

Cattle 

Bacteroides 

Marker 

Cattle 

Bacteroides 

Marker 

Sample Gen-Bac HF-183 HF-654 CF-128 CF-193 Comments: 

Neg Control 0 0 0 0 0 Control 

WT 

C/CowCF* 4 2 0 1 0 Control 

WT D 4 3 0 0 0 Control 

A-1 1 1 0 0 0 Human 

A-2 1 0 0 0 0 General 

A-3 0 0 0 0 0 Clean 

A-4 1 0 0 0 0 General 

A-5 2 1 0 0 0 Human 

A-6 0 0 0 0 0 Clean 

A-7 0 0 0 0 0 Clean 

A-8 0 1 0 0 0 Human 

A-9 0 0 0 0 0 Clean 

A-10 0 0 0 0 0 Clean 

A-11 0 1 0 0 0 Human 

A-12 0 2 0 0 0 Human 

A-13 0 1 0 0 0 Human 

A-14 0 0 0 0 0 Clean 

A-15 0 1 0 0 0 Human 

B-1 1 0 0 0 0 General 

B-2 1 0 0 0 0 General 

B-3 1 1 0 0 0 Human 

B-5 2 1 0 1 0 

Human and 

cow 

B-6 1 0 0 1 0 Cow 

B-7 0 0 0 0 0 Clean 

B-8 1 0 0 0 0 General 

B-9 0 0 0 0 0 Clean 

B-10 1 0 0 0 0 General 

B-11 0 1 0 0 0 Human 

B-12 0 0 1 0 0 Human 

B-13 0 0 0 0 0 Clean 

B-14 0 0 0 0 0 Clean 

B-15 0 1 0 0 0 Human 

C-1 0 0 0 0 0 Clean 

C-2 0 0 0 0 0 Clean 

C-3 0 0 0 0 0 Clean 

C-4 0 0 0 0 0 Clean 

C-5 0 0 0 0 0 Clean 

C-6 0 0 0 0 0 Clean 

C-7 0 0 0 0 0 Clean 

32
C-8 0 0 0 0 0 Clean 

C-9 0 0 0 0 0 Clean 

C-10 0 0 0 0 0 Clean 

C-11 0 0 0 0 0 Clean 

C-12 0 0 0 0 0 Clean 

C-13 0 0 0 0 0 Clean 

C-14 0 0 0 0 0 Clean 

C-15 0 2 0 0 0 Human 

D-1 2 0 0 0 0 General 

D-2 0 1 0 0 0 Human 

D-3 0 2 0 0 0 Human 

D-4 0 1 1 0 0 Human 

D-5 1 1 0 0 0 Human 

D-6 2 2 0 0 0 Human 

D-7 2 2 0 0 0 Human 

D-8 3 1 0 0 0 Human 

D-9 0 2 0 0 0 Human 

D-10 2 0 0 0 0 General 

D-11 2 1 0 0 0 Human 

D-12 0 2 0 0 0 Human 

D-13 1 2 0 0 0 Human 

D-14 0 1 1 0 0 Human 

D-15 0 1 0 0 0 Human 

E-1 0 1 0 0 0 Human 

E-2 0 2 0 0 0 Human 

E-3 0 2 0 0 0 Human 

E-4 2 2 0 0 0 Human 

E-5 2 2 0 0 0 Human 

E-6 0 1 0 0 0 Human 

E-7 2 2 0 0 0 Human 

E-8 0 1 0 0 0 Human 

E-9 0 0 0 0 0 Clean 

E-10 2 2 0 0 0 Human 

E-11 0 1 0 0 0 Human 

E-12 0 0 0 0 0 Clean 

E-13 0 0 0 0 0 Clean 

E-14 0 0 0 0 0 Clean 

E-15 0 0 0 0 0 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. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Fall 2003 Spring 2004 Summer 2004 Fall 2004 Winter 2005

other

flavescens

mundtii

hirae

durans

faecium

faecalis

casseliflavus

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 phylogeny of 

Enterococcus strains using AFLP. The two primer sets, MboI-CTG and MboIAC, 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 MboI-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 al., 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
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). 

Inexpensive (no 

expensive 

equipment, 

primers, or 

standards). 

Expensive 

(machine, primers, 

and standard). 

Much greater 

consistency in gel 

quality (although 

not perfect, very 

sensitive). 

Poor 

reproducibility (pH 

variability affects 

band detection; 

high PCR assay 

variability). 

Technically 

simple. 

Technically more 

challenging (more 

steps). 

High 

reproducibility. 

Low band sizing 

precision (inability 

to discern similarly 

sized bands). 

No hazardous 

waste. 

Hazardous Waste. Very high band 

sizing precision. 

Requires certain 

gel lane width for 

accurate detection 

(reduces 

throughput). 

High throughput. 

Assay produces 

fewer bands (low 

discriminatory 

power). 

Many bands (high 

discriminatory 

power). 

Option to use 

different selective 

primers. 

Conclusions 

Application of AFLP methodology vs. DNA 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-thefarms 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 DNA, 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, Bac1, 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 DNA 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 DNA-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 

LI-approaches to quantify the loadings of agricultural, human and other nonhuman 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 DNA-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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