HomeMy WebLinkAboutNew Suffolk and West Creek Coliform Enumeration and DNA Bacterial Source Tracking
New Suffolkand West Creek
Coliform Enumeration and DNA Bacterial Source Tracking
Submitted by:
Scott Curatolo-Wagemann
Marine Program
Cornell Cooperative Extension of Suffolk County
423 Griffing Avenue, Suite 100
Riverhead,NY 11901
Prepared for the Town of Southold
2015
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Contents
Background ................................................................................................................................................... 3
Methodology ................................................................................................................................................. 3
Monitoring Stations .................................................................................................................................. 3
Sampling dates and conditions. ................................................................................................................ 5
Fecal Coliform Enumeration ..................................................................................................................... 5
E.coli Source Tracking ............................................................................................................................... 6
Results ........................................................................................................................................................... 7
Fecal Coliform Enumeration Results ......................................................................................................... 7
E. coli DNA source tracking summary ....................................................................................................... 7
Summary ....................................................................................................................................................... 9
Appendix 1: NYSDEC Shellfishing Maps for Cutchogue Harbor and related creeks ................................... 10
Appendix 2: DNA Results by station ........................................................................................................... 12
Appendix 3: DNA Station Graphs ................................................................................................................ 20
Appendix 4: Sample Cultivation .................................................................................................................. 23
Appendix 5:DNA Analysis Methodology ..................................................................................................... 26
Introduction: ........................................................................................................................................... 27
DNA Methodology: ................................................................................................................................. 31
............................................................................................................................................... 33
References
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Background
The creeks which contribute to Cutchogue Harbor are part of a valuable ecosystem, which supports
wildlife, shellfishing, swimming, boating and fishing. Due to their ecological value these creeks are
included in the Town of Southold Local Waterfront Revitalization Program (LWRP). Some areas within
these creeks have been classified as uncertified for shellfishing due to pathogens. Appendix 1 shows the
New York State Department of Environmental Conservation’s (NYSDEC) Shellfishing Maps for these
areas.
An effective way to assess pathogen inputs is through a water quality monitoring program which
measures fecal coliforms, a commonly used pollutant indicator organism found in the digestive tract of
warm blooded animals. A successful water quality monitoring study involves periodically collecting
samples from multiple monitoring stations allowing for the determination of the concentration of fecal
coliforms through standard enumeration techniques. These studies often determine the extent of the
coliform pollutant problem, but they are not able to determine the source of the pollutants. It has been
determined, however, that there are detectable differences in DNA extracted from E. coli (a common
type of fecal coliform) which originate from different hosts. Cornell Cooperative Extension of Suffolk
County (CCE) has created an E. coli DNA library which is specific to Long Island. By running coliform
samples through the library, it can be determined if the sample likely originated from humans or other
potential sources such as domestic animals, birds and other wildlife. In some cases, even the host
species (e.g. raccoon Canada goose) can be identified. Identifying the source of fecal coliforms allow
resource managers to more effectively focus their remediation efforts.
Methodology
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Stations were generally chosen due to historical data that had been collected by the NYSEDEC and from
prior water quality monitoring projects. The stations were selected by the Town of Southold Shellfish
Advisory Committee.
1)New Suffolk (Figure 1-within docking area, off “Galley Ho”). NYSDEC 27-1.0 (NS-1)
2)West Creek (Figure 2). NYSDEC 28-10.2
WEC-1- Kimogenor Point
WEC-2- Georges Road
WEC-3- N/W/C Headwaters
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Figure 1: New Suffolk
Figure 2: West Creek
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DateEventComments
9/1/2014 Wet 1.0” rain
9/2/2014 Wet 1.0”-Rain
9/26/2014 Wet 0.52”-Rain
Samples were collected by trained members from the Town of Southold and the Town’s Shellfish
Advisory Committee. All samples were delivered to Cornell Cooperative Extension of Suffolk County’s
Marine and Environmental Learning Center by designated agents of the Town of Southold and the
Shellfish Advisory Committee. All laboratory procedures were performed according to a previous
Environmental Protection Agency (EPA) approved Quality Assurance Project Plan and the associated
Standard Operating Procedures (SOPs).
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Pathogens in aquatic environments are of great concern because they can significantly impact water
quality and lead to shellfishing bed and bathing beach closures. One of the most commonly used
indicators of pathogens is fecal coliforms.
As a means of comparing our fecal coliform findings, we will reference standards established by the
National Shellfish Sanitation Program (NSSP) which are used by the New York State Department of
Conservation (NYSDEC) in certifying shellfishing waters. The standards require that the geometric mean
MPN (most probable number) of the water sample results shall not exceed fourteen (14) per 100 ml and
the estimated 90th percentile shall not exceed an MPN of 49 per 100 for each station. The samples
should be collected through a Systematic Random Sampling (SRS) protocol which randomly selects the
days that sampling will occur regardless of weather conditions. In addition, a minimum of 30 samples
should be taken in order to calculate the geometric mean. Samples collected were enumerated through
the commonly employed membrane filtration technique using accepted U.S. Environmental Protection
Agency methods (8074 and 8367).
Our study deviated from the NSSP standards in a few important ways: we did not follow an SRS protocol
and we did not collect 30 samples per station. The reason for this is because our goal was to identify
fecal coliform sources. So while it is still justifiable to compare our results with the NSSP standards, the
data in this report should not be used to assess whether or not areas within Cutchogue Harbor should
be certified for shellfishing.
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E.coli 3®´±¢¤ 4± ¢ª¨¦
All samples were cultivated and attempted to be grown on E. coli selective media. Since the coliform
bacteria level is not known when the samples are brought into the lab, all samples were suspected to
have bacteria and thus, cultivation of bacteria colonies is started. When a sample has little or no
bacteria often times there will be no growth on the culture media. Coliform counts can then also verify
that there is little or no bacteria present to cultivate, so there is no growth. Those plates that do grow
bacteria are then cultivated and bacteria colonies are then treated as separate isolates from the same
sample, and then verified to be E. coli organisms. For all samples we cultivated 10 isolates, or individual
bacterial colonies. See Appendix 4 for sample cultivation methodology.
Data Analysis:
CCE has developed a technique for identifying the source of fecal coliform bacteria through DNA
analysis. Bacteria from stormwater samples can be compared against a DNA library specific to Long
Island to determine whether it originated from humans or a particular animal group. This information
provides valuable insight when determining the source of fecal coliforms. When samples are run
through the DNA library, results indicate whether or not a sample originated from human (e.g. failed
septic system) or non-human sources. It may also determine if the sample likely originated from birds or
domestic animals. In some cases, the analysis may even indicate the species of animal (e.g. Canada
goose). See Appendix 5 for detailed discussion on DNA analysis methodology.
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Results
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Samples were collected during storm events. During wet events, stormwater can carry land based
contaminants with bacteria constituting part of that pollution. In many cases in this study, fecal coliform
enumeration results exceeded the 14/100 ml counts used by the NYSDEC for shellfish closures. Only 4 of
the 12 samples collected exhibited counts below the threshold.
Samples were collected on consecutive days on September 1 and September 2. For these sampling days
bacteria at the New Suffolk location, the counts went down. At the West Creek stations, all stations saw
an increase in fecal coliform numbers between the 2 days. Initially these were to be the only samples
collected, but to get a larger sample size, a third day of sampling was conducted during a rain event on
September 26. Table 1 contains all the enumeration data.
DateTimeBottleLab #MPN/100mlDateTimeBottleLab #MPN/100mlDateTimeBottleLab #MPN/100ml
New Suffolk
NS-1NYSDEC 27-1.09/1/201410:302023NSW14-1469/2/201412:00B12NSW14-5129/26/20148:2512NSW14-964
West
WEC-1NYSDEC 28-10.29/1/201411:1514NSW14-2389/2/201412:3038NSW14-6839/26/20148:10 B-83NSW14-1046
WEC-29/1/201410:401AQ6NSW14-319/2/201413:05B146NSW14-769/26/20147:57222NSW14-11106
WEC-39/1/201411:0037AQ4NSW14-42299/2/201413:203001NSW14-88409/26/20147:50B-89NSW14-12124
Table 1: Fecal coliform enumeration data
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Table 2, below shows general predicated sources for each sample. Appendix 5 provides a summary of
DNA source tracking methods and a description regarding how the DNA samples are processed.
Appendix 2 displays the results from each of the samples including the individual isolates. See Appendix
3 for a graphical summary of the source tracking findings.
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Station DescriptionDate NYSDEC Predicted Source
Bird
NS-1 9/1/201427-1.0
New Suffolk 1
DNA Sample Not clear
WEC-1 9/2/201428-10.2
West Creek 1
80% Bird, 20% Human
WEC-3 9/2/201428-10.2
West Creek 3
80% Human, 20% Bird
NS-1 9/26/2014 27-1.0
New Suffolk 1
37.5% Wildlife, 25% Wildlife or Bird,
25% Unknown, 12.5% Bird
WEC-1 9/26/2014 28-10.2
West Creek 1
DNA Sample Not clear
WEC-2 9/26/2014 28-10.2
West Creek 2
29% Human; 29% Bird (probable
herring gull); 14% wildlife; 14%
WEC-3 9/26/2014 28-10.2wildlife or bird; 14% unknown
West Creek 3
Table 2: DNA General Summary
Source Categories
-Humans
-Birds: herring gull; mute swan; greater black back gull; Canada goose; mallard duck; black duck; cormorant.
-Wildlife:deer; raccoon; red fox; muskrat.
-Domestic Animals: horse; dog
New Suffolk: 2 total DNA samples came from one station, sampled approximately 3 weeks apart. Earlier
nd
sample contained only bird source, 2 sample contained 20% bird source in addition to 80% human
source
West Creek: 5 total DNA samples coming from 3 different dates and stations. Station 1 had 2 DNA
samples run; the first did not contain DNA material that was not clear enough for analysis. The second
sample showed evidence of wildlife, bird, and 25% of which that could not be identified. Station 2 had a
single sample analyzed for DNA, but the DNA material was not clear enough for analysis. Station 3 had 2
DNA samples, the first sample contained 80% human and 20% bird. The second sample had a mixture of
29% Human, 29% bird, 14% wildlife, 14% that was not distinguished enough between wild life and bird,
and 14% that was unknown and could not be matched to anything in our source library.
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Summary
stnd
Two rounds of samples were collected on consecutive days in early September; the 1 and the 2. The
station at New Suffolk saw a drop in coliform numbers between the 2 days, while all 3 stations in West
stnd
Creek exhibited increased counts from September 1 to September 2. The New Suffolk station is
within the harbor itself, so it lends itself to get flushed more frequently. For this study the highest
coliform counts were always seen at the West Creek station WEC-3. This station is the furthest sampling
location from the harbor, so it receives the least tidal flushing.
For the DNA analysis, the only major category breakdown that did not appear in any of the samples
analyzed was the category of domestic animals (dog, horse). Human isolates were found at 2 locations:
the station at New Suffolk and WEC3 in West Creek. Human source isolates were found in both samples
that were run at WEC3, at 20% and 29% respectively of each sample. However the larger concentration
of human source came from the single New Suffolk station, at 80% of the total sample makeup.
st
The first sample analyzed from New Suffolk on September 1 contained all bird identified isolates. In
most cases the bird source could be narrowed down to be from cormorants. The samples analyzed from
th
West Creek (WEC1 and WEC3), taken on September 26, seemed to both contain varying sources of
bacteria. These contained varying amounts of wildlife, birds, and humans. Additionally with both
samples, a component that could not be identified was categorized as unknown.
Looking at the DNA makeup of both samples that constituted New Suffolk 1 most of the sources of
bacteria could be traced to bird sources, with humans making up the rest. For West Creek 3, half the
DNA sources were found to be bird, with humans making up 25%, and then small amounts of wildlife
and unknown sources.
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Appendix 1: .93$%# 3§¤««¥¨²§¨¦ - ¯² ¥®± #´³¢§®¦´¤ ( ±¡®± £ ±¤« ³¤£
creeks
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NYSDEC Shellfish Map for Cutchogue Harbor (retrieved from
http://www.dec.ny.gov/regs/4014.html#12835):
(iii) Cutchogue Harbor.
(d) New Suffolk. During the period January 1 through December 31, both dates inclusive, all that area of
the marina, located at the eastern end of New
Suffolk Avenue (Main Street, New Suffolk),
within the confines of the stone breakwater
protecting said marina, and all that area lying
easterly of the stone breakwater within 150
feet of the southernmost point of the
breakwater on the northern side of the marina
basin entrance.
(e) West Creek. During the period of May 1 through
November 30, all that area of West Creek including all
that area of Great Peconic Bay within 750 feet in all
directions of the southernmost point of the jetty on the
east side of the mouth of West Creek.
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CORNELL COOPERATIVE EXTENSION
DNA ANALYSIS RESULTS
PREDICTED SOURCE BY ISOLATE
NYSDEC 27-1.0
NEW SUFFOLK 1
DatePredicted Source by Isolate
9/1/14
Bird-Corm
9/1/14 Bird-Corm
9/1/14
Probable Bird
9/1/14 Probable Bird
9/1/14
Bird-Corm
9/1/14
Bird-Corm
9/1/14 Bird-Corm
9/1/14
Bird-Corm
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CORNELL COOPERATIVE EXTENSION
DNA ANALYSIS RESULTS
PREDICTED SOURCE BY ISOLATE
NYSDEC 28-10.2
WEST CREEK 1
DNA Sample not clear enough for analysis
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CORNELL COOPERATIVE EXTENSION
DNAANALYSIS RESULTS
PREDICTED SOURCE BY ISOLATE
NYSDEC 28-10.2
WEST CREEK 3
DatePredicted Source by Isolate
9/2/14Bird-Herring Gull
9/2/14Bird-Herring Gull
9/2/14Bird-Canada Geese
9/2/14Human
9/2/14Bird
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CORNELL COOPERATIVE EXTENSION
DNAANALYSIS RESULTS
PREDICTED SOURCE BY ISOLATE
NS-1NYSDEC 27-1.0
DatePredicted Source by Isolate
9/26/14Human
9/26/14Human
9/26/14Human
9/26/14Human
9/26/14Probable bird
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CORNELL COOPERATIVE EXTENSION
ANALYSIS RESULTS
PREDICTED SOURCE BY ISOLATE
NYSDEC 28-10.2
WEST CREEK 1
DatePredicted Source by Isolate
9/26/14Wildlife or Bird
9/26/14Wildlife possible Raccoon
9/26/14Wildlife possible Raccoon
9/26/14Wildlife possible Raccoon
9/26/14Unknown
9/26/14Bird possible Black Duck
9/26/14Unknown
9/26/14Wildlife or Bird
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CORNELL COOPERATIVE EXTENSION
DNA ANALYSIS RESULTS
PREDICTED SOURCE BY ISOLATE
NYSDEC 28-10.2
WEST CREEK 2
DNA Sample not clear enough for analysis
.
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CORNELL COOPERATIVE EXTENSION
DNA ANALYSIS RESULTS
PREDICTED SOURCE BY ISOLATE
NYSDEC 28-10.2
WEST CREEK 3
DatePredicted Source by Isolate
9/26/14Unknown
9/26/14Wildlife or Bird
9/26/14Wildlife
9/26/14Bird probable Herring Gull
9/26/14Bird probable Herring Gull
9/26/14Human
9/26/14Human
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20
New Suffolk
21
West Creek
22
Appendix 4: 3 ¬¯«¤ #´«³¨µ ³¨®
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Samples are grown in A1 Broth with MUG and Durham tube. MUG will fluoresce based on enzyme
activity of E. coli. Presumptive E. coli = turbidity + fluorescence ± gas.
Dilute bacterial mixture from the presumptive E. coli tube is then grown on Violet Red Bile Agar. Each
colony that grows is considered a preliminary E. coli isolate.
Isolates are then picked from the VRBA plates and streaked onto sectioned nutrient agar plates and
incubated. Each section will produce a “lawn” of bacteria cloned from a single isolate.
E. coli Identification:
Pure cultures of E. coli are verified using the API system. The API 20E System (BioMerieux Vitek, Inc.
Hazelwood, Missouri) is a standardized miniature version of conventional procedures for the
identification of Enterobacteriaceae and other gram negative bacteria. It is a ready-to -use microtubule
system designed for the performance of 23 standard biochemical tests from isolated colony(ies) of
bacteria on plating medium. Used in conjunction with the API Profile Recognition System it is intended
to accurately and easily identify members of the Enterobacteriaceae family to the species level. Clear-
cut reactions, ease of reading and interpretation, permit valid comparison of results obtained each day
in a laboratory, as well as valid comparisons of results obtained in different laboratories worldwide.
0
Once confirmed, isolates are stored in glycerol stock and frozen at –20 C until PFGE analysis can be
done.
Methodology for PFGE Analysis:
Pulsed Field Gel Electrophoresis (PFGE) has been shown to resolve restriction fragments in the genomes
of bacteria yielding DNA fingerprints of these strains (Arbeit, et al., 1990; Goering R.V., and Winters,
M.A., 1992; Hector, et al., 1992; Linhardt, et al., 1992). These fingerprints can be compared to
determine if the bacteria are genetically similar or clonal. To prevent breakage of large DNA molecules,
intact cells are embedded in agarose, lysed, and deproteinized in situ. The agarose matrix protects the
embedded DNA from shear forces and provides an easy way to manipulate samples. The DNA agarose
plugs can then be loaded into the sample wells of agarose gels for electrophoresis.The CHEFDRIII
instrument is based on the pulsed field method of CHEF(Contoured Clamped Homogeneous Electric
Field). Analysis of the gel will show more than 10 distinct bands that resulted from the restriction
digestion. This band pattern is used for genetic strain identification.
Preparation of isolates:
Inoculate 4 ml. sterile LB (Luria broth) with 50-100 l. glycerol stock culture and incubate.
Isolate DNA from cells.
NotI restriction endonuclease will be used to digest the DNA.
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Utilizing a BIO-RAD CHEFDRIII PFGE system the fragments are separated in a 1% agarose gel. Three
molecular size standards ( ladder) and molecular size control (Saccharomyces cerevisiae chromosomes)
and one E. coli control are run along with the restriction fragments. This allows 10 isolates to be run per
gel with 5 controls.The fragments are stained with ethidium bromide and de-stained in distilled water.
The agarose gel is then placed on a GelDoc 2000. The GelDoc 2000 combines a compact darkroom, UV
transilluminator workstation, high resolution CCD camera and software to digitize, document and
analyze fluorescent gels.
Gel images are captured electronically through the GelDoc system. Once the image has been digitized,
Diversity Database (Bio-Rad) software is used to optimize the image, define lanes, identify bands, define
standards, and add the bands to the database. Diversity Database provides data output in molecular
weight.
For discriminant analysis (DA), the ban patterns are analyzed with JMP statistical software (version 5.1,
SAS Institute Inc.). Variables for the analyses include the number and location of bands produced and
the degree of pooling of sources. Each analysis produces a classification set for every known source
isolate. The average rate of correct classification (ARCC) for each analysis is determined by averaging
the percentages of correctly classified isolates for each source. A database is built for each known
source (e.g. human, raccoon, etc.). The DA procedure compares each set of isolates from an unknown
source (water sample) against the database of known sources and then classifies each isolate into one of
the possible sources.
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!¯¯¤£¨· 5: DNA ! «¸²¨² Methodology
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Introduction:
Various investigations conducted on Long Island, such as the Nationwide Urban Runoff Program
(NURP) (Koppelman and Tannenbaum, 1982), the Long Island 208 Waste Treatment Management Plan
(Koppelman, 1978), the Brown Tide Comprehensive Assessment and Management Plan (Suffolk County,
1992), and the Peconic Estuary Comprehensive Conservation and Management Plan (Suffolk County)
have contributed to a better understanding of the impacts of nonpoint source pollution upon
groundwater and surface water quality on Long Island. One of the major pollutants in Long Island
waters is pathogens such as coliform bacteria. Coliforms are an indicator of the possible presence of
pathogenic organisms and are used by various agencies to determine water quality. The use of
coliforms as a water quality standard has been in use since the late 1800s and has been a good tool in
protecting public health. Monitoring of bacterial counts in estuarine waters following storms shows that
stormwater runoff accounted for at least 93% of the total and fecal coliform loading. The water quality
standards applicable to shellfish growing areas are the highest standards developed for marine waters in
New York State. Fecal coliforms are facultative anaerobic bacilli that ferment lactose with the
0
production of gas within 48 hours at a temperature of 44.5C. A prevalent and well-studied member of
this group is Escherichia coli (E. coli).
Researchers have developed a variety of techniques in an attempt to identify nonpoint sources
of bacteria in surface waters. These techniques are generally called Bacterial Source Tracking (BST) or
Microbial Source Tracking (MST) and are divided between molecular methods (genotype), biochemical
methods (phenotype) and chemical methods. The principal difference between methods is the
subtyping methodologies. Molecular methods include pulsed field gel electrophoresis (PFGE),
ribotyping (r-RNA), and polymerase chain reaction (PCR). Biochemical methods include antibiotic
resistance analysis (ARA), F-specific coliphage analysis, fatty acid analysis, nutritional patterns for carbon
and nitrogen, and fecal bacteria ratios. Chemical methods include optical brightener detection, and
caffeine detection. Molecular methods are all referred to as “DNA fingerprinting” and are based on the
unique genetic makeup of different strains, or subspecies, of fecal bacteria. Biochemical methods are
based on an effect of an organism's genes that actively produce a biochemical substance. The type and
quality of these substances produces what is actually measured. Chemical methods are based on
finding chemical compounds that are associated with human wastewaters, and would be restricted to
determining if sources of pollution were human or not. Molecular and biochemical methods of BST are
dependent on building an initial database of profiles from a range of known sources and then comparing
unknowns isolated from contaminated waters to the database of known sources.
The Cornell Cooperative Extension of Suffolk County Marine Program, with previous funding
from the USEPA and other sources, has developed a molecular methodology to use DNA fingerprinting
27
to identify sources of E. coli bacteria found in surface waters. Using PFGE, we have developed a large
DNA fingerprint library of E. coli bacteria isolated from 14 species of common animals, including
humans. This source library has shown significant differences in DNA patterns to differentiate E. coli
from the various animal hosts. We have developed a technique where we now have the ability to
identify the sources of E. coli bacteria collected in surface waters and stormwater flows. Using PFGE and
discriminant analysis of the DNA banding patterns we can identify the sources of E. coli into broad
categories such as human/non-human; bird/wildlife/domestic animal; as well as to species level for the
14 host species in our library. The EPA funded projects were completed as part of an effort to identify
nonpoint sources of pollution by using MST. An initial source library was established through these
projects. Through the NYS Sea Grant project we were able to add additional source samples from within
the Peconic Estuary as well as areas just outside the estuary, but contained within Suffolk County.
Source Tracking:
Water quality is an important factor in Long Island’s estuaries where extensive commercial and
recreational fisheries exist for both finfish and shellfish. Shellfishing is particularly an important
economic and cultural resource on LI, worth many millions of dollars in most years. Good surface water
quality on Long Island, and the perception of good water quality, is also extremely important to the
area’s large and economically important tourist industry also worth millions of dollars annually. Fecal
coliform contamination from nonpoint sources has been recognized as a major threat to surface water
quality (Geldrich, et al., 1968; Faust, 1976; Kay, et al., 1994, and others) and can lead to closure of
surface waters for purposes of recreation and commercial shellfish harvest. Such closures can have
serious negative impacts on the economy of local communities.
Often the most challenging aspect of mitigating nonpoint source pollution (NPS) is determining
the exact source of pollutants, and then formulating the best techniques of controlling them. One of the
sources generally regarded to be a major cause of shellfish closures has been human wastes coming
from improperly functioning On-Site-Waste-Disposal-Systems (OSWDS) (Kator and Rhodes, 1993).
Human wastes from boats have also been responsible for shellfish closures. While many studies have
indicated that OSWDS (Reneau and Pettry, 1975; Hayes, et al., 1990, and others) and marine heads can
be a source of potential contamination, other potential nonpoint sources of bacteria have been
identified as run-off from agricultural areas (Faust and Goff, 1977), wild animals (Leonard, et al., 1989)
and seagulls (Levesque, et al., 1993), as well as other waterbirds and domestic animals. The most
successful remediation strategy is one that recognizes and mitigates each unique source, in itself, as
each one may require a different type of remediation technique and different Best Management
Practice (BMP). The more dispersed wastes of domestic animals and wildlife are considered nonpoint
sources of pollution because they originate in many locations and are transported to surface waters and
to groundwater at many different points. The magnitude and character of the animal waste pollution
28
problem depends upon several factors (Koppelman and Tannenbaum, 1982); however, the present
study will focus on the species type providing the waste source. NPS problems in coastal communities
are attributable to coliforms from humans as well as many species of waterfowl and local wild and
domestic mammalian sources.
It is presently difficult to determine the exact source of bacteria found in contaminated areas.
Consequently, most coliform mitigation strategies in use today are based on Best Management Practices
directed at controlling the stormwater flows themselves, without regard to the specific animals or
animal groups contributing to high levels of bacteria in those flows. The utility of the indicator organism
concept is limited by its inability to track organisms associated with fecal contamination to their
potential sources. Each year millions of dollars are spent on fecal and total coliform assays to determine
the extent of bacterial and fecal pollution of aquatic environments and to satisfy increasingly rigid
regulatory requirements concerning the microbiological quality of water. Knowing the sources rather
than just monitoring the level of microbial pollution of surface waters would enable water quality
professionals and watershed managers to better design and implement programs to control pollution
and protect source water based on the source animals.
There is evidence now accruing that shows that E. coli bacteria found in the gastrointestinal
systems of different species of animals and animal groups vary in genetic identity, and that these
differences can be measured (Dombek, et al., 2000; Parveen, et al., 1997; Carson, et al., 2001;
Samadpour and Chechowitz, 1995; Simmons and Herbein, 1997; Simmons, et al., 2000). The fecal
bacteria in animals (including humans) are very much genetically the same. There are unique
differences, but the differences are only in a small percentage of an organism’s total DNA. The key to
using molecular methods to differentiate between bacterial sources is finding these differences against
a large background of similarity. It is thought that the distinctions between fecal bacteria from different
animals (including humans) occur because the intestinal environments (selective pressures) are not the
same, and fecal bacteria develop with detectable differences that can be related to sources. These
genetic differences in different strains of E. coli may be able to be used to identify the animal species or
animal group specific for that strain of E. coli. Populations of E. coli, like other bacteria, are composed
essentially of a mixture of strains of clonal descent. Due to the relatively low rates of recombination,
these clones remain more or less independent (Selander, et al. 1987). These clones or strains of bacteria
are uniquely adapted to their own specific environments. As a result, the E. coli strain that inhabits the
intestines of one species should be genetically different from the strain that might inhabit another.
All of these various techniques show promise in helping to identify input sources of bacteria at
some level. Some may be able to provide evidence to differentiate between human and non-human
sources. Some may be able to provide evidence to identify large classes of sources such as human,
livestock or wild animals. Still others may be able to provide evidence to identify for the specific animal
29
host species of the bacteria (e.g. human, dog, horse, raccoon, deer, etc.) Researchers are beginning to
verify, with different techniques used and at various levels, the differences in E. coli (and other fecal
bacteria) isolated from various host animals. Dombek, et al. (2000) found that rep-PCR DNA
fingerprinting is a promising method for determining the source groups of E. coli. Paveen, et al (1997)
found that multiple-antibiotic-resistance profiles could be used to differentiate between point source
(human) and nonpoint source (non-human) sources of pollution. Paveen, et al. (1999) used ribotyping to
differentiate between human and non-human source fecal pollution. Carson, et al. (2001) was able to
distinguish E. coli ribotype patterns from human and seven individual non-human hosts. Samadpour
and Chechowitz (1995) also used ribotyping to differentiate E. coli between humans and several non-
human sources. Hagedorn, et al. (1999) used antibiotic resistance patterns of fecal streptococci to
differentiate between waterfowl, humans, deer and beef cows. Simmons and Herbein (1997) and
Simmons, et al. (2000) have used pulsed field gel electrophoresis to differentiate E. coli isolated from
humans and some wildlife species in Virginia. Preliminary analysis by Hasbrouck (2000) of PFGE profiles
of E. coli isolates from various animals on Eastern LI, is showing banding differences between some of
those animals. Wiggins, et al (1999) using antibiotic-resistance analysis of fecal streptococci found
differences from various source animals. Bernhard and Field (2000) have described a new PCR-based
method for distinguishing human and cow fecal contamination based on Bifidobacterium and the
Bacteroides-Prevotella group.
The field of BST is still developing and no single method has arisen as the “best” method. As the
field of BST has been developing and expanding, it is important for researchers and managers to
determine not only which techniques work best under what conditions (or what questions can be
answered by each technique) and for what suite of problems, but also how the different techniques can
be used in conjunction with each other to solve problems, as well as to compare results between
techniques. As these studies have developed, it is important to identify the usefulness of each BST
technique over a range of applications so that each can be identified as a specific tool to be used as
appropriate and where it best fits. Molecular techniques will generate differences at a finer scale
(specific animal host) whereas biochemical and chemical techniques will yield faster results but for
larger groups of animals (humans, non-human). Comparisons of the different types of MST have been
published, and the USEPA has prepared guidance for the specific use of MST in the development of
TMDLs (USEPA, 2011). Specifically, the use of PFGE was found to result in high percentages of correct
classifications of human source at 88% and all other sources at 81% (Myoda, et al. 2003).
As a means of identifying individual coliform sources and developing a BST technique, a DNA
library, specific to eastern Long Island, has been developed by the Cornell Investigator based on E. coli
isolated from the scat of animals (including humans) which live in association with estuaries of eastern
Long Island. This DNA library consists of “genetic fingerprints” PFGE of E. coli isolates. PFGE has been
used to resolve bacterial genomes ranging from microorganisms responsible for nosocomial infections
(Allardet-Servant, et al., 1989) and Vibrio species colonizing oysters (Buchrieser, et al., 1995) to coliforms
30
isolated from water distribution systems (Edberg, et al., 1994). We are exploring techniques to extend
and develop the use of this method as a BST tool for identifying coliform sources in impacted
embayments within coastal areas. All of the molecular BST methods that are being developed rely on
building a DNA library of source isolates against which to compare unknown samples. Once the known
source library has been developed at a sufficiently large size, and correct source identifications are
sufficiently high for the desired purpose, then the task of comparing fecal isolates from unknown origins
against the library to obtain source identification can be accomplished.
$.! Methodology:
The components of the project were carried out as described in an EPA-approved Detailed Work
Plan. All field and laboratory procedures were performed according to the approved Quality Assurance
Project Plan and the associated Standard Operating Procedures (SOPs).
All lab work for the isolation and verification of E. coli from all water samples were performed in
the Cornell Cooperative Extension of Suffolk County (CCE) laboratory in Southold, New York. Isolates
were prepared for DNA analysis and then run on the PFGE equipment according to SOPs. Gels were
then imaged, quantified, cleaned, standardized, bands and lanes defined and electronically stored using
Bio-Rad’s Diversity Database software.
Discussions with Dr. Charles Hagedorn of Virginia Polytechnic Institute and State University (Virginia
Tech), a leading researcher also involved in bacterial source tracking led us to use JMP, a statistical
analysis software package. JMP is a powerful statistical analysis tool that can be used to analyze a
variety of data. Dr. Hagedorn and other researchers at Virginia Tech have developed a method to adapt
JMP to analyze DNA isolates. We have worked with Virginia Tech to adapt this software to accurately
analyze our DNA data and are very pleased with the results. We are using JMP to perform MANOVA—
Mulitvariate Analysis of Variance—on our DNA isolate data. MANOVA is a useful statistical analysis
method when there is more than one dependent variable, as we have in our analysis. By using JMP
MANOVA models with our water sample unknowns compared against our source isolate knowns we can
now identify the source of E. coli bacteria in our water samples. The result is that we have successfully
developed a procedure and data analysis program that can identify sources of coliform bacteria with a
high degree of accuracy and with a high degree of confidence. We now have a tool—PFGE and JMP—
that will provide DNA technology-based approach to remediation of nonpoint source pollution based on
known sources of bacterial contamination.
31
The process of using DNA technology to identify input sources of coliform bacteria depends on
finding unique banding patterns from E. coli strains that are unique to the species host. This must be
accomplished against a background of strains of E. coli that are similar across various species of hosts as
well as transient clones that may only appear randomly. Also within the E. coli found in the water
samples are isolates of clones that may not be in our source library. Due to the randomness of clone
selection in water samples and clone distribution among animals as well as all the various clonal strains
of E. coli that maybe found across animal groups and in water samples, a direct high probability match
up with the known library for individual species host is not always possible. The JMP model provides us
with a probability matrix of water sample matches to the known sources in the library and then provides
a predicted source based on the highest percentage in the probability matrix. When comparing our
unknown water sample isolates against our known source library, sometimes a very strong correlation is
produced by JMP (e.g. 95% probability that the source is a herring gull) and sometimes because of clonal
issues discussed above, a weak correlation is produced by the JMP model (e.g. 35% probability that the
source is a herring gull). We have chosen a 70% probability as a minimum cut off point. Probabilities
above 70% provide a good match while probabilities below 70% do not provide a good match. These
correlations are the basis for using an average rate of correct classification to determine the
effectiveness of a particular database. Therefore some of the results compared against the library on a
species by species basis may produce a source with a high degree of probability, while other results
provide a low degree of probability. These results are typical of the various methods being used in the
field of bacterial source tracking and are consistent with the results of other researchers.
Because we want to be able to determine something about the source of each water sample
isolate, with a high degree of probability, we have set up 9 JMP models that we run each water sample
isolate against. These 9 models range from species-specific (i.e. each of the 14 source species collected)
to the general division of human or non-human. This will allow us to provide at least some information
on the water sample with a high degree of probability. It is not always possible and may also not always
be necessary to predict the source down to species level. Often just knowing if the source is of human
origin will provide managers with valuable information in developing BMPs. Similarly, identifying
samples into animal group (i.e., bird, wildlife or domestic animal), or being able to specify what the
sample is not, (i.e., not human, not wildlife), will also provide valuable information to address NPS.
Therefore by running each sample isolate through 9 JMP models, we can provide some valuable
information at a high level of probability (> 70%) for each sample. The 9 JMP models are as follows:
1.Human—non-human
2.Bird, wildlife, domestic animal
3.Bird—not bird
4.Wildlife—not wildlife
5.Dog—not dog
6.Horse—not horse
7.All birds by species
32
8.All wildlife by species
9.The total library by species
For this project the following animal groupings with their associated specific animals are as
follows:
Bird: herring gull; mute swan; greater black back gull; Canada goose; mallard
duck; black duck; cormorant.
Wildlife: deer; raccoon; red fox; muskrat.
Domestic Animal: horse; dog
The results of each model in terms of the predicted source and associated percent probability for each
isolate are then combined into a spreadsheet. From the percent probability of each of 9 models, an
overall predicted source is then determined for each isolate. Depending on the results, each isolate is
classified either down to specific species host or more general classification (e.g., wildlife, bird, domestic
animal). Also because of clonal issues discussed above, the results of the MANOVA analysis may not
always tell us what the isolate is, but rather what it is not, e.g., not human, not bird, not wildlife, etc.
These results of what the isolate is not can also provide valuable information on eliminating sources and
developing BMPs. When reading the results for an individual isolate, we can often categorize it
successfully due to how the percent probabilities are designed. But sometimes we cannot make a
determination based on the strength of one single model run, and we list a predictive source as
“possible”. What this is telling us is that a given isolate has been classified to within an animal grouping
above the 70% threshold, but only by a few percentage points. We then look at individual species model
runs and while the predicted source may coincide with the animal grouping, the probability percent is
lower than the 70% cut off.
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