University of Connecticut
DigitalCommons@UConn
Master's Theses
University of Connecticut Graduate School
2-28-2012
Bacterial Source Tracking of E. coli in a
Constructed Wetland
Rose M. Martin
University of Connecticut, [email protected]
Recommended Citation
Martin, Rose M., "Bacterial Source Tracking of E. coli in a Constructed Wetland" (2012). Master's Theses. 224.
http://digitalcommons.uconn.edu/gs_theses/224
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Bacterial Source Tracking of E. coli in a Constructed Wetland
Rose M. Martin
B.S., University of Rhode Island, 2009
A Thesis
Submitted in Partial Fulfillment of the
Requirements for the degree of
Master of Science
at the
University of Connecticut
2012
Acknowledgements
Funding for this study came from a USDA NIFA Hatch grant and the Connecticut
Association of Wetland Scientists (Michael Lefor Grant). The Department of Natural
Resources and the Environment and the Center for Applied Genetics and Technology at
the University of Connecticut provided facilities, materials and supplies for this research.
I would like to thank my advisor, Dr. Jack Clausen, for his contribution to this
research and for the guidance he has provided me over the past two and a half years. I
also sincerely thank Dr. Daniel Gage and Dr. Donald Les, my thesis committee members.
Their input has been crucial to the development of this project.
Special thanks go to Ranyelle Craig, Amanda Dupuy, Keith Kozelka, and Emma
White for their assistance with laboratory methods and to Dr. Linda Strausbaugh and Lu
Li for assistance with and access to sequencing equipment.
iii
TABLE OF CONTENTS
LIST OF TABLES ........................................................................................................... vi LIST OF FIGURES ......................................................................................................... vi ABSTRACT ..................................................................................................................... vii LITERATURE REVIEW ................................................................................................ 1 INTRODUCTION .......................................................................................................... 1 URBANIZATION AND CONTAMINATED RUNOFF ............................................... 2 FECAL CONTAMINATION OF WATER.................................................................... 2 INDICATOR BACTERIA.............................................................................................. 7 CONSTRUCTED WETLANDS .................................................................................... 8 BACTERIAL SOURCE TRACKING............................................................................ 9 Direct Sequence Observation for BST ...................................................................... 14 Primer Selection ........................................................................................................ 16 FATE AND TRANSPORT OF FECAL BACTERIA .................................................. 17 Fate and Transport of Bacteria in Soil ...................................................................... 18 Fate and Transport of Bacteria in Water and Sediments .......................................... 19 Growth and Naturalization ........................................................................................ 20 CONCLUSIONS........................................................................................................... 21 REFERENCES ............................................................................................................. 23 BACTERIAL SOURCE TRACKING OF E. COLI IN A CONSTRUCTED
WETLAND ...................................................................................................................... 32 INTRODUCTION ........................................................................................................ 32 MATERIALS & METHODS ....................................................................................... 34 Study Area ................................................................................................................ 34 Sample Collection ..................................................................................................... 34 E. coli Isolation ......................................................................................................... 35 PCR Amplification.................................................................................................... 35 Dye-terminator sequencing ....................................................................................... 37 Sequence Analysis .................................................................................................... 37 iv
RESULTS & DISCUSSION ........................................................................................ 38 Sample Collection & E. coli Isolation ...................................................................... 38 Sequence Comparison and Analysis ......................................................................... 38 CONCLUSIONS........................................................................................................... 41 REFERENCES ............................................................................................................. 47 APPENDICES ................................................................................................................. 50 APPENDIX I ................................................................................................................ 51 APPENDIX II ............................................................................................................... 54 APPENDIX III .............................................................................................................. 55 APPENDIX IV.............................................................................................................. 56 APPENDIX V ............................................................................................................... 57 APPENDIX VI.............................................................................................................. 58 v
LIST OF TABLES
Literature Review
Table 1. Summary of advantages and disadvantages of various BST methods................ 12
Bacterial source tracking of E. coli in a constructed wetland
Table 1: Pigeon, cattle, inlet water and outlet water isolates corresponding to seven
distinct 593 bp sequences of the E. coli mdh gene. .......................................................... 43
LIST OF FIGURES
Bacterial source tracking of E. coli in a constructed wetland
Figure 1. The sequence of the 940 bp mdh gene. ............................................................ 44
Figure 2: Dendrogram showing 30 pigeon, cattle, inlet water and outlet water isolates. 45
Figure 3: Dendrogram showing all 30 pigeon, cow, inlet water and outlet water isolates
along with GenBank mdh sequences.. .............................................................................. 46
vi
ABSTRACT
Bacterial source tracking was used to identify sources of fecal contamination in a
constructed wetland. Nucleotide sequence differences in the Escherichia coli malate
dehydrogenase (mdh) gene were used to distinguish between strains isolated from pigeon
and cattle feces. Fourteen E. coli isolates were taken from cattle and pigeon fecal
samples and sixteen E. coli isolates were taken from wetland water samples. A region of
the E. coli mdh gene was amplified via PCR and sequenced. Twelve distinct sequences
were obtained. Water samples indicated the presence of both pigeon and cattle fecal
contamination in the wetland. Six sequences distinct from those isolated from pigeon and
cattle feces were also present. Three of these sequences were pigeon-specific and two
were cattle-specific. The presence of host-specific sequences indicates that sequencebased source tracking methods show promise for identifying fecal contamination.
vii
LITERATURE REVIEW
INTRODUCTION
Microbial contamination can result from the introduction of fecal matter to
ground and surface water (46). Such contamination presents a risk to public health, and
is recognized by the U.S. Environmental Protection Agency as a leading cause of surface
water impairment (124). For this reason, water must be monitored routinely for
compliance with government standards in order to protect public health (45, 129).
Traditionally, the presence of commensal fecal coliforms or Escherichia coli has been
used to indicate fecal contamination originating from human and animal waste. Although
these indicator organisms are not pathogenic, they suggest the possible co-occurrence of
pathogenic microbes, which also are part of the normal intestinal flora (4).
The simple presence of indicator bacteria signifies potential contamination but
does not provide information about its source, which is crucial to implementing effective
control strategies (17). To remedy this problem, a variety of bacterial source tracking
(BST) techniques have been developed to associate fecal contamination with specific
human and animal sources (124). Consequently, these techniques have the potential to
aid in the control of contamination at its source.
The objective of this review is to examine the current state of knowledge
pertaining to microbial contamination of water, the use of BST methods to identify
contamination sources, and the fate and transport of fecal bacteria in soil and water. This
review will identify areas requiring further research, with particular emphasis on direct
sequencing BST methods.
1
URBANIZATION AND CONTAMINATED RUNOFF
A well-established correlation exists between urbanization and an increasing
proportion of impervious surfaces such as roads and roofs that prevent water from
infiltrating into soil (5). Impervious surfaces increase the velocity and volume of surface
water and decrease infiltration (5, 81) and stormwater has been shown to mobilize
microbial contaminants (81).
Urban stormwater runoff is understood to contain contaminants hazardous to
public health including raw or poorly treated sewage (42, 81, 121, 127). High levels of
microorganisms in urban stormwater can create health risks for those using the water as a
drinking source (99) or for recreational purposes such as swimming (81). In a study
reporting waterborne disease outbreaks in the United States between 1948 and 1994, a
positive, significant (P = 0.002) association was found between extreme precipitation and
disease outbreaks (30). Extreme precipitation events were defined as storms with an
intensity of more than two in/d. The occurrence and extent of microbial contamination in
stormwater runoff can be quantified using bacterial counts; however, only BST methods
enable the origin, transport, and fate of the microorganisms to be determined.
FECAL CONTAMINATION OF WATER
Fecal contaminants in watersheds originate both from point and nonpoint sources
(43). Nonpoint sources of fecal contamination include livestock operations (27, 37, 112,
119), wildlife (2, 110) and pets (131). Combined sewer overflows (CSO’s) (83), failed
septic systems, leaking sewer lines, and cesspools (80) also can introduce fecal
contamination into runoff.
2
Impacts of Fecal Contamination
The contamination of water by fecal bacteria is a widespread, persistent problem
that adversely impacts public health as well as local and national economies (24, 47, 99).
In 2004, the US Environmental Protection Agency assessed 16% of stream reaches in the
United States and found 44% of those to be impaired, with pathogens cited as the leading
cause of impairment (125). When 29% of estuaries and bays was assessed, 30% of those
were found to be impaired. Pathogens, again, were cited as the cause (125).
The greatest risk from microbial contamination present in water involves the
consumption of drinking water contaminated with fecal matter, which can contain a
variety of pathogenic microbes (47, 80, 132). These pathogens include Norwalk virus,
hepatitus A, hepatitus E, toxin-producing E. coli, and Shigella (80). In the United States,
it is estimated that 96% of rural Americans rely on groundwater sources for their water
supply (99). In developing regions and small communities in particular, where
groundwater is the typical source of drinking water, microbial contamination of
groundwater can have significant negative implications for public health (7, 99).
Swimming in fecally contaminated recreational water is consistently reported to
cause illness as well (130). Settling of microbes associated with particles has been linked
to accumulations of viable bacteria in sediment. When re-mobilized, these bacterial
reservoirs can create health risks for users of recreational waters (63). In order to protect
human health, beaches and shellfish harvesting areas may be shut down when high fecal
coliform indicator bacteria counts are recorded for an area (16, 130). Although indicator
bacteria are used widely to determine the sanitary quality of water and to determine beach
closures, this approach has been criticized. Due to the lack of precision inherent in the
3
enumeration of coliform indicators, the use of fecal coliforms as indicators requires
thoughtful experimental design to indicate the sanitary quality of water adequately (45).
In a California study of one location contaminated predominantly by non-point sources,
Colford et al. (26) found that traditional fecal indicators were not actually associated with
health risks. They concluded that traditional indicators may not adequately assess the
risk of illness when point sources of human contamination are not the predominant
source of fecal contamination.
Human Waste
Human waste in urban areas generally is transported to a treatment plant either by
combined sewer systems or by separate storm and sanitary sewers. Combined sewer
systems are intended to transport stormwater and sanitary sewage from domestic,
commercial, and industrial wastewater sources to a treatment facility via a single pipe.
During periods of rainfall or snowmelt, when increased wastewater flows can exceed the
capacity of the system or treatment facilities, the system is designed to overflow directly
into surface water bodies. These events (known as combined sewer overflows or CSO’s),
can be major contributors to water pollution (121). Because microbes in the water
column tend to adsorb to particles, they fall out of solution and into sediment (21), where
they may persist for longer durations than they would in the water column (14, 29, 68).
One study of river bed sediments around a combined sewer outfall (63) indicated that
bacteria persisted in sediments and could be resuspended, adversely impacting water
quality even when no wastewater discharge was taking place.
4
In rural and suburban areas, on-site wastewater treatment systems such as septic
systems are commonplace. Septic systems are intended to treat domestic wastewater,
preventing microbiological and nutrient pollutants from contaminating surface and
groundwater (3). However, on-site waste disposal systems sometimes fail due to
clogging, overloading, poor separation from the water table, and low soil permeability
(120). Arnade (6) found that wells close to septic tanks showed a greater likelihood than
others of being contaminated with fecal coliforms during Florida’s wet season. Septic
systems located on sand and gravel aquifers are particularly prone to contaminate
groundwater used for human consumption (105).
Agriculture
Livestock waste is a major source of pathogens on agricultural land (43, 111,
117), with 133 million tons of manure on a dry weight basis produced annually in the
United States (13). Fecal waste from confined animal feeding operations (CAFO’s)
typically is stored as semiliquid slurry in lagoons, leading to conditions favoring enteric
pathogen persistence (43). Even when cattle are grazed on pastureland, potential
pathogens in excreta may survive for up to 56 days (111).
Bacteria present in manure can be mobilized by rainfall (53) and animal fecal
waste can enter the environment via leakage from manure lagoons or during major
precipitation events that result in either overflowing of the lagoon or runoff of waste
applied recently to agricultural fields (13, 27). Although the nutrient content and
availability of animal waste makes it valuable as a crop fertilizer, over-application of
wastes or application to saturated soils can lead to the movement of contaminants into
5
receiving waters and aquifers (13). The use of treated wastewater for irrigation also can
introduce fecal microbes into the environment (1).
Wildlife
In developed areas, rodents, pigeons, and waterfowl congregate in large numbers
due to a quantity of available shelters and food (39, 56). The combination of large
numbers of animals and a high proportion of impervious surfaces facilitates the
introduction of fecal contaminants into water systems. Urban bird populations are
implicated as a source of fecal pollution (83). Pigeons live in close proximity to humans
and are common in both urban and rural areas where they harbor abundant levels of
enteric bacteria (0.5 x 106 cells/g feces) (39). Pigeons are a known source of pathogenic
E. coli O157:H7 (107), as well as Clostridium perfringens, Listeria monocytogens,
Salmonella enterica, Yersinia spp., Camphlobacter jejuni, Camphlobacter coli, Coxiella
burnetti, and Chlamydophila psittaci (55). Although attempts to control pigeon
populations by culling and avian birth control historically have been unsuccessful (55,
56), public education programs aimed at reducing pigeon feeding have proven effective
(54).
Domestic Animals
Domestic animals also have been recognized as a source of fecal contamination
(24, 83, 120). Dogs and cats living in watersheds with a high proportion of impervious
surfaces present a risk to public health because they can carry zoonotic pathogens (97).
The USEPA (120) advocates leash laws and regulations mandating pet waste pickup to
6
reduce the contamination of runoff by fecal material. In the United Kingdom, where
animal waste is more seriously recognized as a threat to public health, dog wardens and
feces collection bins have been put in place (39).
Urban Runoff
Runoff is a significant contributor of non-point source pollution in urban areas. In
developed areas, higher densities of impervious surface in the form of roofs, roads, and
other paved areas result in faster movement of water over the landscape and decreased
infiltration (5). An increase in impervious surface leads proportionally to an increase in
runoff (5). As water moves over the landscape, it mobilizes and carries with it microbes
and particles associated with microbes. When stormwater enters receiving water bodies,
microbial cells existing individually in the water tend to remain more mobile, while those
associated with particles tend to settle out (21).
INDICATOR BACTERIA
Indicator bacteria should be nonpathogenic, rapidly detected, easily cultured,
should possess survival characteristics similar to those of the pathogens of concern, and
should be associated strongly with the presence of pathogens (109). Traditionally, the
occurrence of fecal indicators such as E. coli, enterococci, total coliform, fecal coliform
and Clostridium perfringens in receiving waters has been used to signify the potential
presence of pathogenic microorganisms (4, 19) because these microorganisms are
abundant in the guts of warm-blooded animals (70). Problems with using such fecal
bacteria as indicators of pathogens can arise due to weak correlations between the
7
concentration of bacteria and the presence of pathogenic microorganisms in water (61)
and the potential for indicator bacteria to replicate outside the host and become
naturalized in the environment, particularly in tropical areas (15, 18, 64, 65). In addition,
indicator bacteria counts do not specify the source of the contamination, which is needed
to effectively direct remediation efforts.
CONSTRUCTED WETLANDS
Over the past two decades, the use of constructed wetlands to improve water
quality has gained prominence (32, 62, 73) because they provide for low-cost,
environmentally friendly wastewater treatment (34). Two main types of constructed
wetlands typically are used: surface flow wetlands, in which wastewater flows
horizontally over wetland sediments, and subsurface flow wetlands, in which wastewater
flows vertically through permeable sediment and collects in drains (72, 129).
Constructed wetlands have been used to treat roof runoff (60), treatment facility effluent
(87), domestic wastewater (9), and dairy wastewater (62). These wetlands can effectively
remove bacteria from wastewater (35, 51, 60). Reported coliform removal efficiencies
typically exceed 90%, with significantly higher removal rates reported for vegetated
systems (72). One study on the University of Connecticut campus found a greater than
98% reduction in E. coli abundance in roof runoff after constructed wetland treatment
(60). Several studies support the observation that wetlands incorporating vegetation are
better than non-vegetated wetlands at removing bacteria from wastwater (35, 73).
Wetlands planted with vegetative polycultures perform consistently year-round, with
removal rates for fecal coliforms ranging from 98% in the fall to 82% in the winter (73).
8
Emergent vegetation provides resistance to flow and thereby slows surface water flow
(71). Because bacteria usually adsorb to small sediments that take longer to settle out of
the water column (31), an increased retention time induced by emergent vegetation
predictably would facilitate sedimentation and thus bacteria removal. Additionally,
emergent plants reduce wind velocities near the water’s surface, which also reduces resuspension (12). However, Vacca et al. (126) and Hier (60) found that the presence of
plants had no effect on bacteria removal in one treatment wetland. Thus, the role of
wetland vegetation in removal of fecal bacteria in wastewater requires further study.
BACTERIAL SOURCE TRACKING
Although direct pathogen monitoring in water provides information pertaining to
potential health risks, the hundreds of different pathogens that are found in water
contaminated with feces make it infeasible to routinely monitor water for all possible
pathogens (4). Moreover, the indicator bacteria typically used to test for fecal
contamination (4,109) do not indicate the origin of the contamination. As an alternative,
BST approaches use genotypic and phenotypic differences in animal host intestinal
bacteria to determine the source of fecal contamination in water (109). This technology
assumes that members within a bacterial species have become adapted to a specific host
(109). Therefore, if a match to a strain unique to a particular host is found in
contaminated water, the source of fecal contamination may be inferred.
BST protocols (Table 1) involve both phenotypic and genotypic techniques.
Phenotypic BST techniques are based on the observation of expressed physical traits
(such as antibiotic resistance) typical of the strain being tracked. Examples include
9
F+RNA coliphage typing, antibiotic resistance analysis (ARA) and multiple antibiotic
resistance analysis (MAR) (25, 91, 93).
However, most recent BST research employs genotypic techniques. For
genotypic BST, a portion of a microbial genome is amplified via polymerase chain
reaction (PCR) and then characterized genetically to identify patterns of variation
uniquely associated with each strain. Characterization can incorporate various types of
fragment analysis such as repetitive element PCR (rep-PCR), amplified fragment length
polymorphism (AFLP), pulsed-field gel electrophoresis (PFGE), and ribotyping (110).
Methods that rely on direct sequence observation may also be employed (66).
BST techniques can be subdivided further based on whether it is necessary to
culture organisms from samples prior to analysis or if a reference library consisting of
known isolates is required. Both library preparation and culturing can substantially
increase the time and resources needed to complete an assay (44).
Culture independent methods of microbial community analysis examine signature
biochemicals taken directly from environmental samples (10). Culture-independent BST
methods include terminal restriction fragment length polymorphism (T-RFLP),
denaturing gradient gel electrophoresis (DGGE), length heterogeneity PCR (LH-PCR),
and the use of 16SrRNA gene clone libraries (88, 110) (Table 1).
Library independent BST methods (e.g. DGGE, LH-PCR, and host-specific
marker gene PCR) are rapid and easy to perform (17, 110), and have successfully
matched fecal water contamination to host sources (52) (Table 1). However, library
independent BST methods require further method development (110, 114), produce nonquantitative results (4), and sometimes fail to differentiate among multiple host groups
10
(4). Most BST methods (e.g., T-RFLP, 16SrRNA clone libraries, ARA, MAR, rep-PCR,
AFLP, PFGE, ribotyping, and sequencing) require a library (36, 66, 88, 91, 93, 110)
(Table 1). Library dependent BST methods require a comparison of microbes isolated
from water samples to serve as a reference for fecal samples obtained from the host
organisms. These BST methods are more time consuming (44). Regional differences in
intestinal host floras may require the isolation of separate libraries for each watershed
studied (19, 75) and the clonal composition of isolates can differ temporally, e.g., during
the transition from primary habitat (the host) to secondary habitat (the environment) (75).
Although BST techniques have been used successfully to identify sources of fecal
bacteria (8, 58, 89, 90, 96), the need for further study and refinement of currently used
BST techniques is emphasized (20, 88, 109, 110, 114, 115).
11
Table 1. Summary of various BST methods.
Method
T-RFLP
(Terminal
Restriction
Fragment Length
Polymorphism)
Library
Dependent?
Yes
Culture
Dependent?
No
Phenotypic or
Genotypic?
Genotypic
Description
References
uses restriction enzymes coupled with PCR
Marsh, 1999
Simpson, 2002
Blackwood et al., 2003
Meays et al., 2004
Hartmann and
Widmer, 2008
fragments tagged with a fluorphore are
detected.
12
DGGE
(Denaturing
Gradient Gel
Electrophoresis)
No
No
Genotypic
electrophoretic analysis of PCR products
based on melting properties of amplified DNA
sequence; discriminates species
Simpson, 2002
Meays et al., 2004
LH-PCR
(Length
heterogeneity PCR)
No
No
Genotyoic
separates pcr products generated for hostspecific genetic markers by length
Simpson, 2002
Meays et al., 2004
F+RNA Coliphage
Typing
No
Yes
Genotypic
variability of viruses infecting coliform
bacteria fertility factors indicate animal or
human fecal contamination
Cole et al., 2003
Host-specific
marker gene PCR
No
Yes
Genotypic
discriminates E. coli genes from strains
associated with host species
Call et al., 2007
16SrRNA gene
clone libraries
Yes
No
Genotypic
combine LH-PCR and T-RFLP on fecal
anaerobes, discriminates humans and cattle
Meays et al., 2004
McGarvey et al., 2004
12
Table 1. Summary of various BST methods (Continued).
Method
Library
Dependent?
Yes
Culture
Dependent?
Yes
Phenotypic or
Genotypic?
Phenotypic
Description
References
observes variability in resistance to antibiotics
Moore et al., 2005
Olivas et al., 2008
Rep-PCR
(Repetitive Element
PCR)
Yes
Yes
Genotypic
PCR amplification of palindromic DNA
sequences with electrophoretic analysis
Dombek et al., 2000
Simpson, 2002
Mohapatra et al.,
2008a and 2008b
AFLP
(Amplified
Fragment Length
Polymorphism)
Yes
Yes
Genotypic
DNA fingerprinting using rare and frequent
cutting restriction enzymes
Simpson, 2002
PFGE
(Pulsed Field Gel
Electrophoresis)
Yes
Yes
Genotypic
DNA fingerprinting using rare cutting
restriction enzymes paired with
electrophoretic analysis
Scott et al.,2002
Simpson 2002
Ribotyping
Yes
Yes
Genotypic
southern hybridization of genomic DNA cut
with restriction enzymes and probed with
ribosomal sequences
Scott et al., 2002
Simpson, 2002
Sequencing
Yes
Yes
Genotypic
PCR amplification of sample DNA, dyeterminator sequencing to determine order of
nucleotides
Ivanetich et al., 2006
Olive and Bean, 1999
Ram et al., 2004
Ram et al., 2007
ARA,
MAR(Antibiotic
Resistance, Multiple
Antibiotic
Resistance)
13
13
Direct Sequence Observation for BST
In contrast to genotypic BST methods which use fragment analysis, genotypic
BST methods that use DNA sequencing allow for direct sequence observation, which
enables better reproducibility and precision, as well as the opportunity to survey a greater
extent of the genome in order to detect differences (102). Direct sequencing BST
methods involve PCR amplification of a selected DNA region specific to the organism
being studied, followed by sequencing of the PCR products. Suitable primers must first
be developed to suitably target the region of interest. Like other genotypic BST
techniques, direct sequencing methods assume that bacterial strains, after being acted on
by the selective pressure of their host environment, are unique to that particular host
(109). Therefore, if an exact match can be found in a water sample, the source of
contamination can be identified.
Despite the high degree of accuracy and reproducibility in DNA sequencing
achieved as a result of recent technological advances, literature pertaining to sequencebased BST methods is scarce. Olive and Bean (1999) concluded that despite their
potential utility for BST, sequencing methods have some limitations. They pointed out
that few regions selected in previous studies had met criteria essential for strain
differentiation: i.e., they comprise a variable region flanked by highly conserved regions;
the level of variability detected sufficiently discerned different strains of a species; and
the region was not susceptible to horizontal gene transfer. However, subsequent studies
have identified and developed effective BST methods by targeting specific regions of the
E. coli genome. Ram et al. (101) sequenced the ß-glucuronidase (uidA) gene of E. coli at
different sites to quantify genetic differences between populations. They also were able
14
to assign different fecal isolates to possible host sources (including humans, farm
animals, birds and pets) using interpopulational allelic variation in uidA. By excluding
alleles common to all hosts, they achieved a 75% level of correct fecal sample
assignment to host organisms. Ram et al. (102) subsequently used the uidA gene to
evaluate humans, pets and urban wildlife as sources of contamination to storm sewers,
this time achieving a 65% level of correct classification.
Use of the gusA gene in BST methods is complicated by uncertainty regarding the
extent of horizontal gene transfer near the gusR, A, B, C operon. In order for a sequencebased BST method to be effective, genetic diversity at the gene locus being sequenced
must be consistent with the evolutionary history of the organism being studied (66).
Following criteria established by Olive and Bean (94), Ivanetich et al. (66)
evaluated several regions of the E. coli genome for the development of a BST assay.
They selected the malate dehydrogenase gene (mdh), as it satisfies all criteria presented
by Olive and Bean (94) and because there is no evidence that more than a single copy of
the gene exists in the E. coli genome. They also emphasized other advantages of using
the mdh gene for sequence-based BST methods including their observation that no E. coli
mdh alleles were found frequently either in the host species or in most environmental
samples and also because little variation was observed between strains sampled from
hosts in different geographic locations (66). They designed primers to amplify an 825
base pair portion of the 936 base pair mdh gene and focused specifically on a 150 base
pair region of the gene determined to have the highest level of polymorphism. However,
by shortening the target sequence, some polymorphisms were eliminated making
differentiation between some hosts' strains impossible. A total of ten polymorphic sites
15
was identified, and these were used to distinguish between host species, which included
dog, deer, seagull, horse and human. A dendrogram of sequence variants clustered horse,
seagull and dog isolates into identifiable groups, though human isolates were highly
variable. In a blind analysis of environmental fecal isolates, 95% of dog, deer, seagull
and horse isolate sequences were matched successfully with host reference sample
sequences using this approach (66).
Primer Selection
Genomic BST methods require PCR primers for amplification. Primers are short,
single strands of nucleic acid that contain sequences complementary to target DNA
regions and serve as starting points for DNA synthesis. Pairs of primers are designed to
work in the forward and reverse directions on a strand of DNA, allowing for
amplification of a specific region (106). Selecting or designing the correct pair of
primers is crucial to the success of PCR, as poor primer selection can lead to poor PCR
yield (106). Primers must be designed for specificity, and will only work to produce
good PCR results when each primer anneals stably to the target sequence of DNA in the
desired organism (106). In order to develop effective primers, a target region is selected
on the bacterial chromosome and specific sequences are designed to selectively amplify a
region between annealing sites of the two oligonucleotides (105). Requirements for
successful primers include proper base composition, with guanine and cytosine content
between 40% and 60% and an even distribution of adenine, thymine, cytosine and
guanine; an 18 to 25 nucleotide long region of the primer complementary to the template;
the sizes of the two primers not differing by more than 3 bp’s; the absence of inverted
16
repeat or self complimentary sequences to avoid secondary structures; and proper melting
temperature (106). Many web-based primer design tools have been created to assist with
the technical aspects of primer design (23, 78, 100, 128).
Boyd et al. (11) designed primers to amplify an 864 base pair segment of the
malate dehydrogenase (mdh) gene from E. coli. These primers were used initially by
Ivanetich et al. (66) but later were modified to amplify a region of higher polymorphism.
Chen and Griffiths (22) developed primers targeting an 884 bp sequence of E. coli DNA
adjacent to the universal stress protein gene (uspA). This region specifically
differentiated E. coli from other Gram-negative bacteria, and a test assay was highly
specific for E. coli (22). Turner (118) used this primer set in a study employing T-RFLP
to distinguish between strains of E. coli derived from feral pigeons and cattle, but was
unable to isolate unique T-RF’s from cattle-sourced E. coli. Ram et al. (101, 102)
designed primer sets to target the E. coli β-glucuronidase gene for the purpose of
sequence-based source tracking.
FATE AND TRANSPORT OF FECAL BACTERIA
Fecal microbes can survive in soil, water, and sediment for varying durations
depending on the suitability of local conditions including moisture, temperature, pH,
nutrient availability, and competing microorganisms (67). They can even form
naturalized populations (64, 65).
17
Fate and Transport of Bacteria in Soil
Microbial concentrations in soil or percolated water are dependent upon the
survival of organisms in the soil and the soil’s retention ability (49). Fecal bacteria can
survive for long periods in soil, with one study reporting a duration of 68-80 days (28).
Bacteria survive longer in organic soils than in sandy soils (116) because organic matter
increases nutrient retention, provides bacteria with a carbon source, and retains moisture
(43). A review by England (41) indicated that clays may enhance bacteria survival in soil
by providing pore spaces small enough to protect bacterial cells from predation.
Generally, fecal bacteria survive longer in soil at temperatures between 4° and 10°C than
they do at warmer temperatures (28, 74). Shorter survival times associated with warmer
temperatures may be due to a combination of thermal effects and increased activity of
potentially competitive native soil flora (28). Freezing is lethal to bacterial populations,
which can be reduced by up to 95% after several freeze-thaw cycles (74). Enteric
bacteria exhibited lower survival rates in low-pH soils than in those of more moderate pH
(49). Van Donsel et al. (127) showed that the time required to remove 90% of bacteria
introduced into soil increased from as little as 2.7 days in the summer to as long as 20.1
days during the winter. Bacterial species also display different survivorship in different
soil types. In a study of E. coli and Enterococcus spp. derived from pig slurry, E. coli
survived best in a sandy soil, whereas Enterococcus spp. survived best in loamy soil (28).
Gannon et al. (48) observed that transport of different types of soil bacteria was
influenced by the electrostatic charge of their cell surface, hydrophobicity, cell size, and
the presence of capsules and flagella.
18
Microbes that enter the soil move downward with infiltrating water and sediment
(103). Soil provides filtering and adsorption sites for microorganisms, and microbial
movement through a saturated soil depends on soil properties, including sand, silt, clay,
and organic matter content (43). The majority of such filtering seems to occur at the soil
surface, through straining, sedimentation and adsorption (49). McCoy and Hagedorn (85)
found that bacterial populations are reduced rapidly as they enter the soil system.
However, once the organisms reach a highly conductive soil zone, long distances are
required for further population reduction. Microbial organisms are transported more
rapidly when soil is saturated (30, 57). Macropores such as earthworm burrows may
further facilitate the movement of microorganisms through the soil and potentially even
into groundwater (69).
Fate and Transport of Bacteria in Water and Sediments
The availability of pathogens transported in runoff is influenced by the die-off
rate of any enteric bacteria in soil and to the extent of waste applied to it (103). Once in
the water column, microbes associate with particles, clump together to form aggregates,
or are suspended individually (21, 108). Microbes that exist individually in the water
column or are adsorbed to less dense particles will remain more mobile, whereas
microbes that associate with dense inorganic particles generally settle out of the water
column much earlier (21).
Bacteria survive longer in marine and freshwater sediments than in the overlying
water (14, 29, 40, 68, 108). This phenomenon is attributed to the greater availability of
nutrients available in sediments (29). Possible re-suspension of microorganisms may
19
occur during natural turbulence or disturbance by humans (29, 108). Sediment properties
also influence the distribution of bacteria. Bacterial concentrations are higher in
sediments comprised of fine particles than in those comprised of coarse particles (14, 29,
31). Individual bacterial species and strains exhibit different preferences for sediment
size classes (21, 68, 98). Jeng et al (68) found that enterococci attached preferentially to
particles with a diameter of 10--30 μm, but that fecal coliforms and E. coli were not as
selective. Bacterial survival in sediment also increases with increased availability of
organic material (31, 76), which is higher in finer sediments.
Growth and Naturalization
In addition to surviving in soil, water, and sediments, enteric bacteria are capable
of growing and even of forming naturalized populations in these environments (38, 64,
65, 82). Several studies document growth of fecal bacteria in soil and sediment. Fecal
coliforms were able to multiply in storm drain sediments (82) as well as in sewage sludge
applied to a forest clearcut (38). Ishii et al. (64, 65) reported naturalized populations of
E. coli in temperate soils.
Fate of Bacteria in Wastewater Treatment Wetlands
Many treatment wetlands use the process of bacterial adsorption to sand, silt and
clay particles which then undergo sedimentation to remove fecal bacteria from
wastewater (32, 113). Though numerous studies detail removal rates of fecal bacteria
from wastewater using constructed wetlands, less research details the fate of bacteria in
these wetlands.
20
Microorganisms persist in wetland sediments, acting as reservoirs of living
bacteria (32, 113). Because bacteria adsorb preferentially to smaller particles (31),
treatment systems must effectively facilitate the settling out of fine particles (32, 113).
Besides sedimentation, other processes that remove bacteria in constructed wetlands
include filtration through the substrate and associated biofilm, aggregation, oxidation,
exposure to biocides, predation, attacks from lytic bacteria and viruses, antibiosis,
naturally occurring die-off, and competition for limiting nutrients and trace elements (50,
51). Predation by nematodes, rotifers, and protozoa also is thought to be important to the
removal of bacteria from wastewaters treated by constructed wetlands (51).
CONCLUSIONS
Microbial contamination of water threatens public health as well as local and
national economies. Pigeons carry a wide array of pathogenic enteric microorganisms,
but no BST studies have focused on pigeons as potential sources of fecal contamination.
Although bacterial counts can provide information regarding the presence and
concentrations of indicator bacteria, they offer little information about the source of the
microorganisms. Though numerous studies have indicated various promising BST
methods, there is a need to refine currently used techniques for increased reproducibility
and accuracy. Sequencing is a promising BST technique due to inherently high levels of
precision and reproducibility. However, the need for culturing and library development
is a drawback. Culturing increases the amount of time and technical skill needed to
perform these assays. Library development can be problematic due to regional
differences in host intestinal flora and changes in isolate composition during transition
21
between hosts and the environment. Genome regions must meet certain criteria in order
to be suitable for sequence-based BST applications and such regions are uncommon in
bacteria. The E. coli malate dehydrogenase gene (mdh) meets all necessary criteria for
sequencing BST, but only one study has focused on this approach.
22
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31
BACTERIAL SOURCE TRACKING OF E. COLI IN A CONSTRUCTED WETLAND
INTRODUCTION
Water contamination resulting from fecal inputs is a widespread problem, with
pathogen contamination a leading cause of surface water impairment (33) that impacts
public health and local and national economies (3, 5, 11, 26). Fecal contamination can
result from many sources including human waste, agriculture and livestock operations,
indigenous wildlife, domestic animals, and urban runoff (15, 9, 12, 7, 20). Runoff
effectively transports fecal contaminants, and a well-known correlation exists between
urbanization and increased runoff (22). Consequently, greater contamination of water by
pathogens is expected as urbanization progresses, due to the increased volume of runoff
and its load of fecal contaminants.
Because their droppings contain high levels of enteric bacteria (8), pigeons have
emerged as a public health concern due to their abundance in urban and rural areas and
their frequent close proximity to humans. They are a known source of Clostridium
perfringens, Listeria monocytogens, Salmonella enterica, Yersinia spp., Camphlobacter
jejuni, Camphlobacter coli, Coxiella burnetti, and Chlamydophila psittaci (13, 14), and
pathogenic E. coli O157:H7 (30). Despite the fact that pigeons carry such an array of
pathogenic microbes, no studies have focused on them as a potential source of fecal
contamination.
Traditionally, indicators such as E. coli, enterococci, and Clostridium perfringens
have been used to signify the potential presence of pathogenic microorganisms (1, 6).
Because these microorganisms co-occur abundantly in the guts of warm-blooded animals,
32
their presence in receiving waters indicates the existence of fecal contamination and the
presumed presence of pathogenic microorganisms (19). However, simple quantification
of indicator bacteria does not enable the origin of a contaminant to be determined. .
Source identification is necessary for the implementation of management strategies to
curtail contamination and thus lead to improved water quality.
Bacterial source tracking (BST) uses genotypic and phenotypic differences in
intestinal bacterial communities to determine the source of fecal contamination in water.
BST assumes that strains of bacteria become adapted, and thus unique to, particular
animal hosts. Therefore, bacterial isolates from the environment may be matched to
animal sources (6, 24).
Techniques that use gene sequence data to directly analyze specific regions of the
E. coli genome potentially are highly effective in matching environmental samples to
their sources (18, 27, 28). Specifically, the malate dehydrogenase (mdh) gene is a
particularly promising target region for such applications because it satisfies several
essential criteria: it is not vulnerable to horizontal gene transfer, it consists of a highly
variable region flanked by highly conserved regions, it is relatively short in length but
contains sufficient allelic polymorphism to differentiate between strains (25), and occurs
as a single copy in the E. coli genome (18). Only one previous study has tracked sources
of fecal contamination using E. coli mdh gene polymorphisms (18).
The objective of this study was to determine whether occurrences of E. coli
detected in roof runoff and a constructed wetland originated from cattle or pigeon
sources. Nucleotide sequence differences in the E. coli malate dehydrogenase (mdh)
gene were used to genotype strains derived from these two hosts, and these differences
33
were used to screen the microbial composition of water samples derived from the runoff
and wetland sources.
MATERIALS & METHODS
Study Area
The study site was a 416 m2 constructed wetland that received roof runoff from a
602.6 m2 portion of the roof of the Kellog Dairy Center at the University of Connecticut
in Storrs, CT. Until 2004, the wetland received milkhouse waste from the Center. A
flock of over 100 feral pigeons has been observed roosting on the roof. Water is directed
from the roof to a monitoring station and from there to three cells vegetated primarily by
emergent wetland species. The water leaves the wetland through an effluent monitoring
station.
Sample Collection
Fresh fecal reference samples were collected from cattle in the Kellog Dairy
Center. Pigeon reference samples were obtained by placing aluminum foil under groups
of roosting pigeons at a nearby barn silo. Care was taken to procure fecal samples from
individual animals; no mixed samples were taken. Fecal material was collected with
sterile polyester swabs and placed into TWIRL’EM® sterile polystyrene sampling bags
(Thermo Fisher Scientific, Waltham, MA). Samples were placed on ice for transport to
the lab.
34
Water samples were collected from the inlet and outlet pipes of the constructed
wetland using TWIRL’EM® bags during rainstorms when flow into and out of the
wetland occurred. Samples were placed on ice for transport to the lab.
E. coli Isolation
Escherichia coli was isolated from feces and water samples using two culturing
steps. Serial dilutions of fecal material in sterile deionized water were prepared and
plated on MacConkey agar to select for gram-negative, lactose-metabolizing bacteria
(10). Cultures were incubated overnight at 37ºC. Well-isolated suspected E. coli
colonies (10) from each plate were re-streaked onto MacConkey agar for isolation and
again incubated overnight. Isolate colonies were inoculated into 1 mL of Colilert-18
medium (Idexx Laboratories Inc., Westbrook, ME) according to the manufacturer’s
protocol to confirm the presence of E. coli (27). Cultures that tested positive
(fluorescent) for E. coli were stored for downstream use at -20ºC.
PetrifilmTM plates for E. coli and coliforms (3MTM, St. Paul, MN) were used
according to the manufacturer’s protocol to isolate E. coli from water samples.
Suspected E. coli colonies from each plate were streaked onto MacConkey agar and
treated as above. If colonies were too numerous on the PetrifilmTM, a sterile pipette tip
was touched to the plate and streaked onto MacConkey agar as above.
PCR Amplification
Two primers first developed by Ivanetich et al. (16) (5’TGAAAGTCGCAGTCCTCGG-3’; 5’-GGGTAAAAACGGCGTGGA-3’) (Figure 1)
35
were used to amplify an 825 base pair region of the malate dehydrogenase gene (mdh) by
PCR (18). Each 30 μL reaction contained 1 μL of positive Colilert-18 culture, 0.2 μL
Bio-Rad iTaq DNA polymerase, 2 μL 2 mM deoxynucleoside triphosphates (dNTP’s), 1
μL 5 μM forward and reverse primers, 6 μL 5X cresol red, 3 μL 25 mM MgCl2, 3 μL
10X PCR buffer, and 8.8 μL sterile deionized water (32). A master mix containing all
components of the reaction except the template DNA was prepared, vortexed briefly, and
aliquoted into 0.2 mL tubes where template DNA was added. Positive controls contained
E. coli XL1 Blue genomic DNA shown previously to produce a product with this
procedure and negative controls contained sterile water in place of culture.
Reactions were carried out in a MyCycler thermal cycler (Bio-Rad Laboratories
Inc. Hercules, CA). Samples were denatured for 5 min. at 94ºC, and then exposed to 32
amplification cycles consisting of: 2 min at 94ºC (denaturation), 1 min at 60ºC
(annealing), and 1 min at 72ºC (extension). After a final extension step of 25 min. at
72ºC, reactions were held at 10ºC until retrieved.
To confirm successful amplification of the 825 base pair region, PCR products
were electrophoresed for 30 minutes at 140 V alongside 1 Kb+ DNA ladder on a 1%
agarose gel in Tris/Borate/EDTA buffer containing ethidium bromide. Gels were imaged
with a GelDoc XR + UV camera (Bio-Rad Laboratories Inc., Hercules, CA). PCR
products were purified for downstream application using a QiaQuick PCR purification kit
(Qiagen Inc., Valencia, CA) according to the manufacturer’s protocol and were processed
either immediately or stored at -20ºC.
36
Dye-terminator sequencing
An 825 bp region of the mdh gene was sequenced using the same primers used for
PCR. Each 10 uL reaction contained 1 μL QiaQuick-purified PCR product, 0.5 μL
BigDye® Terminator, 1.75 μL BigDye® buffer, 1 μL 5 μM primer, and 5.75 μL sterile
deionized water. Master mixes for each primer were prepared separately and aliquots
were placed into 0.2 mL tubes to which purified PCR product was added. Positive
controls contained purified PCR product previously shown to produce clean sequences.
Negative controls contained sterile water in place of DNA. Reactions were initially
denatured for 1 minute at 96ºC and then exposed to 25 amplification cycles consisting of:
10 seconds at 96ºC (denaturation), 5 seconds at 50ºC (annealing), and 4 minutes at 60ºC
(extension). Reactions were held at 4ºC until retrieved.
Sequencing reaction products were precipitated using a Qiagen Dye-Ex Kit
(Qiagen Inc., Valencia, CA) following the manufacturer’s protocol. Once precipitated,
PCR products were resuspended in highly-deionized formamide (Hi-DiTM, Applied
Biosystems, Carlsbad, CA), vortexed to mix, and loaded into sequencing plates.
Sequencing was performed on an ABI 3130 Genetic Analyzer (Applied Biosystems,
Carlsbad, CA).
Sequence Analysis
Sequences were inspected and reverse compliments of reverse primer sequences
were generated in FinchTV (Perkin Elmer, Waltham, MA). Sequences were exported in
FASTA format and then trimmed and assembled in CLC Main Workbench (CLC bio,
Aarhus, Denkmark). In the event of poor sequence quality, sequencing was repeated.
37
Poor sequence quality included: sequence trace chromatograms < approximately 500 bp;
low signal strength; or “messy” sequence peaks.
Sequences for all 30 pigeon, cattle, inlet water and outlet water samples were
aligned using the CLC Main Workbench multiple alignment function. From this
alignment, a dendrogram was constructed using the neighbor-joining algorithm (29).
Redundant, or identical, sequences were identified. Bootstrapping, a statistical technique
that uses resampling to evaluate dendrogram reliability, was performed with 1000
replicates. The Basic Local Alignment Search Tool (BLAST) algorithm (2) was used to
compare unique sequences identified to GenBank E. coli mdh sequences.
RESULTS & DISCUSSION
Sample Collection & E. coli Isolation
Nine isolates were taken from two cattle fecal samples. Of these, Colilert-18 tests
confirmed that seven of the isolates (78%) were E. coli. Nineteen isolates were obtained
from two pigeon fecal samples, with seven of the isolates (37%) testing positive for E.
coli. All of the eight inlet water sample isolates and the eight outlet water sample isolates
tested positively for E. coli.
Sequence Comparison and Analysis
Trimming to remove poor quality sequence ends for all 30 pigeon, cattle, inlet
water and outlet water samples yielded a 593 bp region when sequences were aligned. A
reverse primer closer to the end of the mdh gene was designed in CLC Workbench in an
attempt to improve gene coverage. However, this primer generated secondary products
38
during PCR and so the original published primer was used (18). Twelve distinct
sequences (Table 1) were identified and aligned (Appendix I). Among these sequences,
21/593 sites (3.5%) were polymorphic, with 17 substitutions (80.9%) representing
transitions. A cluster analysis (Figure 2) grouped isolates with identical sequences.
Bootstrap values, shown on dendrograms, indicate how strongly branching structure is
supported by the data.
Sequence 1 was found most often (20%) among the 30 isolates evaluated (Figure
2, Table 1). Cattle isolates included four of the 12 sequences, as did pigeon isolates.
Two of the sequences were unique to inlet water samples and three were unique to outlet
water samples. Three sequences were obtained from pigeon (and not cow) isolates and
two sequences from cow (but not pigeon) isolates. Two sequences were retrieved from
both cow and pigeon isolates.
In order to be useful for source tracking applications, E. coli sequences must be
host-specific. In this investigation, sequences detected only either in pigeon (and water)
isolates (Sequences 1, 5, and 7) potentially are indicative of pigeon fecal contamination.
Similarly, sequences detected only in cattle (and water) isolates (Sequences 2 and 10) are
potential indicators of cattle fecal contamination. In both cases, a much greater sampling
of host and water samples would be required before the extent of host specificity could be
established with certainty. The sequences found in both pigeon and cow isolates
(Sequences 6 and 9) would not be useful for source tracking due to their lack of host
specificity.
The sequences found exclusively in water (inlet or outlet) samples (Sequences 3,
4, 8, 11 and 12) could represent fecal contamination from different host sources. They
39
also could be sequences of strains that were present in the hosts sampled but not isolated,
or strains from earlier effluent inputs that became naturalized in the constructed wetland
(4, 16, 17). The latter scenario seems unlikely due to the occasional cycles of drying of
the wetland during the summer months and freezing during the winter (21). No
sequences were found in both inlet water and outlet water isolates. Sequences from
strains isolated from inlet samples but not outlet samples suggest that those strains could
have been removed by the constructed wetland. Sequences from strains isolated from
outlet but not inlet samples possibly implicate the wetland as a source of E. coli.
However, it is also possible that the small number of samples and isolates taken did not
fully characterize the extent of diversity for all strains present in both inlet and outlet
water samples.
The presence of sequences isolated from pigeon feces in inlet and outlet water
samples (Sequences 1 and 5) was expected, as large numbers of feral pigeons defecate on
the roof from which runoff is directed into the wetland. Sequence 10 putatively indicates
cattle fecal contamination in roof runoff due to its exclusive presence in cattle and inlet
water isolates, at least among the samples evaluated. Its presence may be due to several
factors. First, the E. coli strain from which the sequence was obtained could have
become naturalized after its introduction during dairy facility effluent inputs prior to
2004, though summer drying and winter freezing cycles make it unlikely that naturalized
E. coli populations would survive. Second, E. coli from cattle feces may have become
airborne (31, 34, 35) and settled on the roof of the dairy facility. From there, rain could
carry E. coli into the constructed wetland. Third, it is possible that this sequence is also
40
found in pigeon strains but was not isolated from pigeon feces among the samples
processed during this study.
A BLAST search of GenBank using the 12 unique sequences identified in this
study mainly matched mdh sequence strains isolated from mammalian sources.
However, this result could be attributable to the presence of fewer mdh sequences in the
GenBank database. The BLAST matches represented a variety of sources from different
locations, suggesting that sequences isolated in this study represented strains that are
widely distributed. A dendrogram, which compares the mdh sequences from GenBank
strains to sequences obtained in this study (Figure 3) illustrates these relationships.
GenBank matches for which a source was not specified are not shown.
The use of more widely separated primer sites could allow for analysis of a larger
region of the mdh gene. Further sampling of pigeon, cattle, inlet water and outlet water
would provide greater assurance that host-specific sequences exist. Expanding host
sampling to include other species could disclose the source of the strains observed in
water but not in pigeon or cow isolates. Dogs, horses, Canada geese, starlings, sparrows,
and groundhogs all have been observed near the study site and could contribute to fecal
contamination in the constructed wetland.
CONCLUSIONS
Sequence variation in a 593 bp region of the mdh gene indicated different E. coli
strains in cattle and pigeon which potentially identify host-specific strains within this
dataset. The distribution of sequences evaluated in this study pointed to pigeon fecal
contamination in wetland inlet and outlet samples. Sequences indicating cattle fecal
41
contamination matched only inlet samples. Wetland inlet and outlet samples indicated
the presence of E. coli strains not isolated from pigeons or cattle in this study. No
sequences were found in both wetland inlet and outlet water samples.
Though preliminary, this study demonstrates the potential for source tracking
methods using nucleotide sequence differences in the mdh gene to identify host-specific
E. coli strain sequences.
42
Table 1. Pigeon, cattle, inlet water and outlet water isolates corresponding to seven distinct 593
bp sequences of the E. coli mdh gene. .
Sequence
1
Isolates
Pigeon 4
Pigeon 5
Outlet Water 5
Outlet Water 6
Outlet Water 7
Outlet Water 8
Sequence
7
Isolates
Pigeon 7
2
Cattle 6
8
Inlet Water 3
3
Outlet Water 2
9
Pigeon 2
Cattle 1
Inlet Water 1
Inlet Water 2
Inlet Water 7
4
Inlet Water 5
Inlet Water 6
10
Cattle 2
Cattle 3
Cattle 5
Cattle 7
Inlet Water 8
5
Pigeon 3
Pigeon 6
Inlet Water 4
11
Outlet Water 1
Outlet Water 3
6
Cattle 4
Pigeon 1
12
Outlet Water 4
43
Figure 1. The sequence of the 940 bp mdh gene. Forward and reverse primers used for PCR amplification and dye-terminator
sequencing are shown.
44
Figure 2. Dendrogram showing 30 pigeon, cattle, inlet water and outlet water isolates. The Neighbor-joining algorithm was used for
dendrogram construction.
45
Figure 3. Dendrogram showing all 30 pigeon, cow, inlet water and outlet water isolates along with
GenBank mdh sequences. The Neighbor-joining algorithm was used for dendrogram construction.
46
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49
APPENDICES
50
APPENDIX I
Alignment of 593 bp region of the mdh gene of unique sequences isolated from pigeon, cow, inlet and outlet water isolates.
Polymorphisms are indicated (1 of 3).
51
Alignment of 593 bp region of the mdh gene of unique sequences isolated from pigeon, cow, inlet and outlet water isolates.
Polymorphisms are indicated (2 of 3).
52
Alignment of 593 bp region of the mdh gene of unique sequences isolated from pigeon, cow, inlet and outlet water isolates.
Polymorphisms are indicated (3 of 3).
53
APPENDIX II
Colilert-18 E. coli Presence / Absence Test
□ Prepare Colilert-18 medium. Dissolve one packet in 100 mL sterile water.
□ Inoculate well-isolated pink colony from MacConkey agar into 1 mL Colilert-18
medium
□ I the sample is not already at 33–38°C, place the sample in a 35°C waterbath for 20
minutes or a 44.5°C waterbath for 7–10 minutes
(This prewarming time is part of (not in addition to) the 18-hour incubation
period)
□ Incubate 18 hours at 35.5 C
□ Fluorescence indicates presence of E. coli
Adapted from kit insert, Colilert-18 medium (Idexx Laboratories Inc., Westbrook, ME)
54
APPENDIX III
PCR of E. coli DNA extracted from Feces
Samples:_____________________________
Extraction method:______________
Extraction Date:_____________ Pages describing extraction:____________
Taq, MgCl2, and PCR buffer used: ABI □/Bio-Rad □
Notes:
_______X Master Mix (______μl):
(Stock Concentrations after dash)
30 μl PCR:
DNA _____ ng/uL
Taq polymerase
dNTPs
up primer
down primer
cresol red
MgCl2
PCR buffer
H20
(______μl)
(______μl)
(______μl)
(______μl)
(______μl)
(______μl)
(______μl)
(______μl)
(______μl)
=_____μl
□
NO DNA in Master Mix
Taq
(______μl)—5 units/μl
dNTPs
(______μl)—2 mM
up primer
(______μl)—5 μM
down primer
(______μl)—5 μM
cresol red
(______μl)—5X
MgCl2
(______μl)—
mM
PCR buffer
(______μl)—10X
H20
(______μl)
=_____μl
□
tube
MM
added?
DNA
added?
Cycling Conditions
94°C for 5 min.
32 cycles of:
94°C for 2 min.
60°C for 1 min.
72°C for 1 min.
72°C for 25 min
10°C for ∞
Start Time: ________________
End Time: _________________
Adapted from Turner (2010)
55
□
□
□
□
□
□
□
□
APPENDIX IV
Cleanup using QiaQuick PCR purification kit
1. □ Add 5x product/digest volume in Buffer PB to product/digest
-PB volume ___________μl
2. □ Transfer to spin column; let sit 1min.
3. □ Spin w/ balance 1 min. @ max speed
4. □ Discard flowthrough.
5. □ Add 750μl Buffer PE
6. □ Spin w/ balance 1 min. @ max speed
7. □ Discard flowthrough.
8. □ Spin w/ balance 1 min. more @ max speed
9. □ Transfer column to a new 1.5ml tube
10. □ Add 60μl dH2O; let sit 1 min.
11. □ Spin w/ balance 1 min. @ max speed
DNA is now ready for downstream application.
Store @ 4°C if not to be used right away.
Adapted from QiaQuick PCR purification kit insert. (Qiagen Inc., Valencia, CA)
56
APPENDIX V
Sequencing Reaction Setup
Samples:_____________________________
*Reactions for forward and reverse primers set up separately*
______ X Primer 1 Master Mix (______μl):
NO DNA in Master Mix
Big Dye
(______μl)
□
Buffer
(______μl)
□
Forward primer (______μl)
□
H20
(______μl)
□
=_____μl
10 μl PCR:
PCR Product
Big Dye Terminator
5x Big Dye Buffer
Up primer
Down primer
H20
=
1 μl
0.5 μl
1.75 μl
1
μl
1
μl
5.75 μl
10 μl
_______X Primer 2 Master Mix (______μl):
NO DNA in Master Mix
Big Dye
(______μl)
Buffer
(______μl)
Reverse primer (______μl)
H20
(______μl)
=_____μl
□
□
□
□
Cycling Conditions
96°C for 1 min.
25 cycles of:
96°C for 10 sec.
50°C for 5 sec.
60°C for 4 min.
10°C for ∞
Adapted from Applied Biosystems BigDye® Terminator v1.1 Cycle Sequencing Kit protocol
57
APPENDIX VI
Precipitation of Sequence PCR Products (Using Qiagen DyeEx Kit)
Sample #:__________________
□
Gently vortex the Dye Ex spin column to resuspend the resin and label
the caps.
□
Loosen the cap of the column a quarter of a turn.
□
Snap off the bottom closure of the spin column and place the spin
column in the 2 ml collection tube.
□
Centrifuge for 3 minutes at the calculated speed (see kit insert).
□
Carefully transfer the spin column to a clean microcentrifuge tube.
□
Apply the contents (10 uL) of the sequencing reaction product to the
gel bead.
□
Centrifuge for 3 minutes at the calculated speed.
□
Remove the spin column and place the collection tube onto the hot
plate for approximately 20 minutes, until all liquit has evaporated.
□
After completely dry, resuspend pellet in 20 uL Hi-Di formamide.
□
Vortex for 20 seconds.
□
Transfer all contents in the collection tube into sequencer plate, load
plate into ABI 3130 sequencer, and sequence.
Adapted from Qiagen Dye-Ex kit insert. (Qiagen Inc., Valencia, CA)
58