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Graduate Theses and Dissertations Graduate School
11-2-2009
Microbial Population Analysis in Leachate From Simulated Solid Microbial Population Analysis in Leachate From Simulated Solid
Waste Bioreactors and Evaluation of Genetic Relationships and Waste Bioreactors and Evaluation of Genetic Relationships and
Prevalence of Vancomycin Resistance Among Environmental Prevalence of Vancomycin Resistance Among Environmental
Enterococci Enterococci
Bina S. Nayak University of South Florida
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Microbial Population Analysis in Leachate From Simulated Solid Waste Bioreactors
and
Evaluation of Genetic Relationships and Prevalence of Vancomycin Resistance Among
Environmental Enterococci
by
Bina S. Nayak
A dissertation submitted in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
Department of Integrative Biology
College of Arts and Sciences
University of South Florida
Major Professor: Valerie J. Harwood, Ph.D.
Daniel V. Lim, Ph.D.
Kathleen Scott, Ph.D.
Diane TeStrake, Ph.D.
Degeng Wang, Ph.D.
Date of Approval:
November 2, 2009
Keywords: community structure, DGGE, methanogens, BOX-PCR, VRE.
© Copyright 2009, Bina S. Nayak
ACKNOWLEDGEMENTS
I would like to express my sincere gratitude to Dr. Valerie J. Harwood and Dr.
Diane TeStrake who believed in me and gave me the opportunity to pursue my studies at
my own pace while having ample time for my daughter. I also thank my committee
members Dr. Daniel Lim, Dr. Kathleen Scott and Dr. Degeng Wang for their guidance,
time and support. I would like to express my gratitude to the members of my lab for
assisting me with parts of my research and supporting me through difficult times. I
would like to acknowledge my two best friends Katrina Gordon and Stefica Depovic for
their love, friendship and encouragement. I would like to extend special thanks to my
parents (Shrinivas and Jayashri Nayak) and my siblings for their support, faith and
blessings, without which I could not have achieved this degree. Thank you also to my
husband Kiran and my sweet daughter, Ruchi, who were extremely co-operative
throughout my study period and encouraged me to continue my studies even though it
took time away from them.
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TABLE OF CONTENTS
LIST OF TABLES .............................................................................................................. v
LIST OF FIGURES ........................................................................................................... vi
ABSTRACT ...................................................................................................................... vii
MICROBIAL POPULATION ANALYSIS IN LEACHATE FROM
SIMULATED SOLID WASTE BIOREACTORS ............................................................. 1
BACKGROUND .................................................................................................... 1
Waste disposal in landfills ............................................................................... 1
Leachate characteristics ................................................................................... 2
Microbial processes in landfills ....................................................................... 3
Methanogenesis ............................................................................................... 5
Microbial characterization of leachate ............................................................. 7
Landfill management practices ........................................................................ 9
Bioreactor studies ............................................................................................ 9
Research goals ............................................................................................... 11
References ...................................................................................................... 13
MICROBIAL POPULATION DYNAMICS IN LABORATORY-SCALE
SOLID WASTE BIOREACTORS IN THE PRESENCE OR ABSENCE
OF BIOSOLIDS.................................................................................................... 23
Abstract .......................................................................................................... 23
Introduction .................................................................................................... 24
Materials and Methods .................................................................................. 26
Bioreactor design ...................................................................................... 26
Sample processing .................................................................................... 27
Total microbial concentrations ................................................................. 27
Polymerase chain reaction (PCR) for community analysis ...................... 27
Denaturing gradient gel electrophoresis (DGGE) ..................................... 28
Cloning and sequence analysis of mcrA gene .......................................... 29
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Statistical analysis ..................................................................................... 30
Results............................................................................................................ 31
Total cell concentrations ........................................................................... 31
pH of leachate ........................................................................................... 32
DGGE analysis of microbial communities ............................................... 32
Sequence analysis of methanogens ........................................................... 34
Discussion ...................................................................................................... 40
Acknowledgements ........................................................................................ 44
References ...................................................................................................... 45
SURVIVAL OF INDICATOR ORGANISMS DURING WASTE
DEGRADATION IN SIMULATED LANDFILL BIOREACTORS ................... 53
Abstract .......................................................................................................... 53
Introduction .................................................................................................... 54
Materials and methods ................................................................................... 56
Results............................................................................................................ 60
Hydrated phase.......................................................................................... 60
Dehydrated phase ...................................................................................... 63
Discussion ...................................................................................................... 65
References ...................................................................................................... 69
RESEARCH SIGNIFICANCE ............................................................................. 74
Microbial community structure ..................................................................... 74
Methanogen populations ................................................................................ 75
Survival of indicator organisms during waste degradation ........................... 76
Waste degradation studies ............................................................................. 76
References ...................................................................................................... 77
EVALUATION OF GENETIC RELATIONSHIPS AND PREVALENCE OF
VANCOMYCIN RESISTANCE IN ENVIRONMENTAL ENTEROCOCCI ................ 79
BACKGROUND .................................................................................................. 79
Identification of enterococci .......................................................................... 80
Genetic “fingerprinting” techniques used to type enterococci ...................... 82
Virulence in enterococci ................................................................................ 85
Antibiotic resistance in enterococci ............................................................... 86
Emergence of VRE ........................................................................................ 88
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VRE in the environment ................................................................................ 91
Nosocomial VRE ........................................................................................... 92
Research goals ............................................................................................... 93
References ...................................................................................................... 94
COMPARISON OF GENOTYPIC AND PHYLOGENETIC
RELATIONSHIPS OF ENVIRONMENTAL ENTEROCOCCUS
ISOLATES BY BOX-PCR TYPING AND 16S rRNA SEQUENCING ........... 117
Introduction .................................................................................................. 117
Materials and methods ................................................................................. 120
Sample collection and processing ........................................................... 120
Sequencing the 16S rRNA gene ............................................................. 122
BOX-PCR genotyping of enterococci .................................................... 123
Results and Discussion ................................................................................ 124
References .................................................................................................... 133
PREVALENCE OF VANCOMYCIN RESISTANT ENTEROCOCCI IN
ENVIRONMENTAL MATRICES AND WASTEWATER .............................. 139
Abstract ........................................................................................................ 139
Introduction .................................................................................................. 140
Materials and methods ................................................................................. 143
Sample collection and processing ........................................................... 143
Vancomycin susceptibility testing .......................................................... 145
Sequencing the 16S rRNA gene and vanA gene .................................... 146
BOX-PCR genotyping of enterococci .................................................... 148
Screening for virulence factors ............................................................... 148
Statistical analysis ................................................................................... 149
Results.......................................................................................................... 149
Discussion .................................................................................................... 154
Acknowledgements ...................................................................................... 157
References .................................................................................................... 158
RESEARCH SIGNIFICANCE ........................................................................... 171
BOX-PCR genotyping of enterococci ......................................................... 172
Vancomycin resistance in enterococci ......................................................... 173
Isolation of VRE from environmental sources ............................................ 174
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References ........................................................................................................... 175
ABOUT THE AUTHOR……………………….……………….…………….....End Page
v
LIST OF TABLES
Table 1. Shannon diversity indices calculated for the DGGE patterns of Archaea
and Bacteria. ............................................................................................................. 35
Table 2. Frequency of methanogen clones observed in the day 50 and day 218
samples obtained from the co-disposal bioreactor. ................................................... 38
Table 3. Isolates used in this study listed according to site (Lake Carroll,
Hillsborough River and Ben T. Davis beach) and environmental matrix
(water, sediment and vegetation). ........................................................................... 121
Table 4. Primers used in this study. ............................................................................... 147
Table 5. VRE genotypes observed from environmental water and wastewater
samples. ................................................................................................................... 150
Table 6. Low-level VRE as a percentage of total enterococci in each matrix
(water, sediment, vegetation) at each site. .............................................................. 151
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LIST OF FIGURES
Figure 1. Dendrograms and corresponding principal components analysis of
archaeal population structure in (a) MSW and (b) co-disposal bioreactors. ............. 36
Figure 2. Dendrograms and corresponding principal components analysis of
bacterial population structure in (a) MSW and (b) co-disposal bioreactors. ............ 37
Figure 3. Diagram of bioreactor configuration. ............................................................... 57
Figure 4. Total cell concentrations (x 109/ml) in leachate during the hydrated
phase as measured by BacLight Live/Dead staining. ............................................... 61
Figure 5. pH values in duplicate bioreactors (B1 and B2) during the hydrated
phase. ........................................................................................................................ 62
Figure 6. Mean concentrations of fecal coliforms (FC) and enterococci (ENC) in
leachate sampled weekly from duplicate bioreactors in the hydrated phase of
the experiment. .......................................................................................................... 62
Figure 7. Total cell concentrations (x 107/ml) in leachate from time zero (T0) to
twenty four (T24) after rehydration of the bioreactor. ............................................... 64
Figure 8. Heterotrophic plate counts on R2A agar (aerobic organisms) and
anaerobic agar (anaerobic organisms) after bioreactor rehydration. ......................... 64
Figure 9. Dendrogram showing the percent similarity of bacterial DGGE patterns
from time zero (T0) to twenty four (T24) after rehydration of the bioreactor. ........... 65
Figure 10. Phylogenetic tree constructed using the neighbor-joining algorithm to
evaluate the distance between 16S rRNA gene sequences of environmental
enterococci. ............................................................................................................. 128
Figure 11. Dendrogram demonstrating the similarity of BOX-PCR patterns of
Enterococcus species isolated from environmental matrices. ................................ 132
Figure 12. Dendrogram demonstrating the similarity of BOX-PCR patterns of
vanA VREF isolated from hospital wastewater, esp- positive isolates are
underlined. .............................................................................................................. 152
vii
MICROBIAL POPULATION ANALYSIS IN LEACHATE FROM SIMULATED
SOLID WASTE BIOREACTORS
AND
EVALUATION OF GENETIC RELATIONSHIPS AND PREVALENCE OF
VANCOMYCIN RESISTANCE AMONG ENVIRONMENTAL ENTEROCOCCI
BINA S. NAYAK
ABSTRACT
Degradation of the several million tons of solid waste produced in the U.S. annually is
microbially mediated, yet little is known about the structure of prokaryotic communities
actively involved in the waste degradation process. In the first study, leachates generated
during degradation of municipal solid waste (MSW) in the presence (co-disposal) or
absence of biosolids were analyzed using laboratory-scale bioreactors over an eight-
month period. Archaeal and bacterial community structures were investigated by
denaturing gradient gel electrophoresis (DGGE) targeting 16S rRNA genes.
Regardless of waste composition, microbial communities in bioreactor leachates
exhibited high diversity and temporal trends. Methanogen sequences from a co-disposal
bioreactor were predominantly affiliated with the orders Methanosarcinales and
Methanomicrobiales. Effect of moisture content on indicator organism (IO) survival
viii
during waste degradation was studied using culture-based methods. Fecal coliform and
Enterococcus concentrations in leachate decreased below detection limits within fifty
days of bioreactor operation during the hydrated phase. IOs could be recovered from the
bioreactor leachate even after a prolonged dry period. This study advances the basic
understanding of changes in the microbial community during solid waste decomposition.
The purpose of the second study was to compare the ability of BOX-PCR to determine
genetic relatedness with that of the “gold standard” method, 16S rRNA gene sequencing.
BOX-PCR typing could clearly differentiate the strains within different Enterococcus
species but closely related genera were not as distinguishable. In contrast, 16S rRNA
gene sequencing clearly differentiates between closely related genera but cannot
distinguish between different strains of Enterococcus species. This study adds to our
knowledge of genetic relationships of enterococci portrayed by two separate molecular
methods.
The incidence of vancomycin resistant enterococci (VRE) in environmental matrices,
residential and hospital wastewater was also investigated. Low-level VRE (vanC
genotype) were isolated from environmental matrices and residential wastewater. VRE
isolates from hospital wastewater were identified as E. faecium and demonstrated
resistance to ampicillin, ciprofloxacin and vancomycin (vanA genotype), but sensitivity to
chloramphenicol and rifampin. Although no high-level VRE were isolated from surface
waters, the high proportion of low-level VRE in environmental matrices is a cause for
concern from the public health perspective.
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MICROBIAL POPULATION ANALYSIS IN LEACHATE FROM SIMULATED
SOLID WASTE BIOREACTORS
BACKGROUND
Waste disposal in landfills
The most common practice for disposal of waste generated in countries around the world
is landfilling. With increasing global populations, the amount of waste generated each
year is growing phenomenally. The world population passed the 6 billion mark at the turn
of the century. In the US alone, 254 tons of municipal solid waste (MSW) was generated
in 2007 (U.S. EPA 2008). The majority of the waste deposited in landfills is composed of
MSW, which includes food waste, yard waste, paper, plastics, metals, glass, rubber,
wood, leather, textiles, etc. This waste is comprised of approximately 40-50% cellulose,
12% hemicellulose, 10-15% lignin, and 4% protein (Barlaz et al. 1989). In addition to
MSW, other types of wastes that are landfilled include biosolids from wastewater
treatment facilities, residues from waste-to-energy (WTE) and other combustion
processes, electronic wastes, construction and demolition wastes.
A consequence of growing populations in urban areas is the increased production of
wastewater, coupled with a decrease in land availability for land application of associated
biosolids. Treated and dewatered biosolids are used as fertilizers and soil amendments.
The unutilized biosolids are disposed of in landfills (Reinhart 2003). Biosolids are rich in
2
moisture and nutrients and contain diverse microbial populations that can provide
supplemental inocula for waste degradation reactions (Rivard et al. 1990; Poggi-Varaldo
1992). Waste degradation in landfills is a slow, ongoing process occurring over decades
and characterized by physicochemical reactions and microbial interactions.
Leachate characteristics
The liquid that percolates through the waste matrix is termed leachate, which solubilizes
and mobilizes minerals associated with the waste (Kjeldsen et al. 2002), and also
promotes microbially-mediated degradation of waste (El-Fadel 1999). The composition
of leachate is related to the moisture content within landfills and biogeochemical
reactions that occur within the waste matrix. In general, the amount of moisture available
to support microbial activity depends on local climatic conditions, e.g. rainfall and
temperature, the specific materials deposited in the landfill, and the frequency of leachate
removal and/or recirculation (El-Fadel 1999). Among the dominant components of
municipal waste are cellulose, hemicellulose, lignin and protein (Barlaz et al. 1989).
Leachates have been analyzed to determine the amount of dissolved organic matter
(recorded as chemical oxygen demand or COD), volatile fatty acids (VFAs), inorganic
components such as calcium (Ca2+
), magnesium (Mg2+
), potassium (K+), ammonium
(NH4+), iron (Fe
2+), sulfate (SO4
2-) and hydrogen carbonate (HCO
3-), heavy metals such
as copper, lead nickel and zinc and trace amounts of arsenate, mercury, lithium, etc
(Kjeldsen et al. 2002). Due to the differences in waste composition, age and landfilling
practices the composition of leachate varies between landfills. The pH of leachate can
vary from 4.5 to 9 depending on the stage of waste decomposition.
3
Landfill leachate can carry toxic metabolic products, heavy metals, pharmaceuticals and
human pathogens (Mose and Reinthaler 1985; Gerba et al. 1995). Leachate
contamination of groundwater due to improper waste management practices poses a
threat to public health (Christensen et al. 1994; Roling et al. 2001). Leachate pooling at
the bottom of the landfill is collected in a network of pipes called the leachate collection
system and pumped to a storage tank. The collected leachate is then transported in
tankers to a neighboring wastewater treatment plant where it is treated and disposed.
Leachate recirculation is practiced in many landfills because it provides an additional
inoculum of established microbial flora that can boost the rate of the degradation process
(Chan 2002). It also reduces the amount of leachate requiring treatment and increases gas
production (Reinhart 1996; Warith 1999). In contrast, excessive leachate recirculation
can result in over-saturation of waste and have a toxic effect on sensitive organisms such
as methanogens (Reinhart 1996). The frequency and amount of leachate recirculation also
plays an important role in the development of certain microbial groups at different time
points during waste degradation(Shen et al. 2001). Leachates collected from landfills
carry a representative sample of the numerous microorganisms involved in degradation of
that waste (Gurijala 1993; Pohland and Kim 2000). Studying the microbial community
profile of this leachate over a period of time could provide interesting insights into the
physical, chemical and biological processes occurring within the landfill.
Microbial processes in landfills
Landfills provide excellent environments for the development of diverse microbial
populations due to the wide variety of substrates available to support the physiological
requirements of microorganisms. The complexity and composition of microbial
4
communities in any particular landfill depends on several parameters such as the types of
wastes deposited, moisture availability, landfill age and operating practices, toxicity of
waste components and their breakdown products, and leachate management practices
(Barlaz et al. 1989; Kjeldsen et al. 2002). These parameters have a direct or indirect
effect on the pH, dissolved organic and inorganic carbon content, oxidation-reduction
potential and temperature of the leachate.
Waste deposited in landfills is at different stages of degradation depending on the age and
depth of the waste. In general, the process of waste degradation is presumed to progress
in four stages from the time of deposition till maturation: aerobic, anaerobic, accelerated
methane production and decelerated methane production (Barlaz et al. 1989). The first
stage is dominated by aerobic heterotrophic bacteria that decompose cellulose and
consume oxygen and nitrate present in the waste (Pourcher et al. 2001). This results in
the production of carbon dioxide and possibly an increase in temperature. After oxygen is
a consumed, anaerobic cellulolytic bacteria, i.e., Clostridium spp. and Eubacterium spp.,
hydrolyze cellulose and hemicellulose into monosaccharides that are further fermented to
produce alcohols and carboxylic acids (Westlake 1995; Van Dyke and McCarthy 2002;
Burrell et al. 2004). Acetogenic bacteria convert these organic acids and alcohols to
acetate, carbon dioxide and hydrogen (Mackie and Bryant 1981). Acetogens can also act
as hydrogen oxidizers by reducing carbon dioxide to produce acetate and other low
molecular weight organic compounds.
Hydrogen has an important role in waste decomposition (Mormile 1996). Hydrogen-
oxidizing methanogenic Archaea oxidize hydrogen and reduce carbon dioxide to
methane(Griffin et al. 1997). Sulfate reducing bacteria (SRB), on the other hand, use
5
hydrogen as the electron donor and sulfate as the terminal electron acceptor (Daly et al.
2000). These two groups of microorganisms compete for available hydrogen in the
landfill waste, which sometimes leads to the inhibition of methanogenesis by the SRBs
(Mormile 1996; Raskin 1996). Carbon mineralization by methanogenesis is preferred for
landfill functioning because sulfate reducers produce hydrogen sulfide, causing the
phenomenon of “souring” (Gurijala 1993). Souring of waste reduces the pH and inhibits
the activity of methanogens.
Methane production and recovery is environmentally and economically desirable due to
its usefulness as a biogas for the production of energy in the form of heat and electricity
Unfortunately, methane recovery from landfills is affected by waste composition,
microbial degradation dynamics and more importantly cost effectiveness. Methane is a
potent greenhouse gas and the altenative to recovering methane, controlling methane
emissions, is equally expensive. Methanotrophic bacteria present in the upper oxic region
of the landfill use methane as their carbon and energy source and convert it to carbon
dioxide (Whalen 1990; Wise et al. 1999). Therefore, in landfills where methane recovery
is not an option, methanotrophs play an important role in controlling the emission of
methane from landfill sites.
Methanogenesis
According to the US Environmental Protection Agency (EPA), in 2007, MSW landfills
were responsible for approximately 23% of methane emissions in the U.S.making them
the second largest anthropogenic source of methane (U.S. EPA 2009). Methanogens are
classified under the domain Archaea, which includes three kingdoms: Euryarchaeota,
Crenarchaeota and Korarchaeota (Winker and Woese 1991; Barns et al. 1996).
6
Methanogens belong to the kingdom Euryarchaeota and are divided into five orders:
Methanosarcinales, Methanomicrobiales, Methanobacteriales, Methanococcales and
Methanopyrales (Bapteste et al. 2005). Methanogens are a strictly anaerobic, mostly
autotrophic group with very diverse 16S rRNA sequences inhabiting varied ecological
habitats ranging from the human digestive system to submarine volcanic vents (Ferry
1993). They utilize a limited range of substrates including hydrogen, acetate, formate,
methanol and other methylated substrates.
Several genome-sequencing projects (Methanobrevibacter smithii, Methanococcus
maripaludis, Methanococcus jannaschii and Methanobacterium thermoautotrophicum)
have added to our knowledge of the complex metabolic pathway of methanogenesis (Bult
et al. 1996; Smith et al. 1997; Hendrickson et al. 2004; Samuel et al. 2007). The most
widely studied enzyme of methanogenesis pathways is methyl coenzyme M reductase
(MCR), which catalyzes the final step of Methanogenesis whereby the methyl group of
methyl-S-coenzyme M is reduced to methane. The MCR enzyme is comprised of ,
and subunits encoded by the mcrA, mcrB and mcrG genes, respectively (Bokranz and
Klein 1987). The mcrA gene is highly conserved and is the gene that is most frequently
used to detect methanogenic activity in varied environments (Lueders et al. 2001; Luton
et al. 2002; Earl et al. 2003; Dhillon et al. 2005).
Methanogens can be detected and analyzed using molecular techniques such as
fluorescence in-situ hybridization (FISH) and construction of clone libraries by targeting
16S rDNA (Mori et al. 2003), or biomarkers such as methanogen-specific DNA
sequences (Hales et al. 1996; Earl et al. 2003; Dhillon et al. 2005). Some studies have
developed methanogen clone libraries using the primers specific for the ubiquitous 16S
7
rRNA gene (Huang et al. 2003; Mori et al. 2003) while others have targeted the mcr gene
encoding methyl coenzyme-M reductase (MCR) (Hales et al. 1996; Nercessian et al.
1999) or both (Springer et al. 1995; Lueders et al. 2001). Functional genes are used as
biomarkers because their higher rates of evolutionary change enhance the ability to
resolve sequences at the species level compared to the 16S rRNA gene (Braker et al.
2000; Junca and Pieper 2004). Currently, limited information is available regarding the
composition of methanogen populations in landfills. The heterogeneous nature of waste
makes it impossible to determine the type of substrates used by methanogens and the
amount of methane generated during different stages of waste decomposition. This
information could be useful in developing and improving methane management practices
to efficiently recover the methane produced in landfills.
Microbial characterization of leachate
In several studies, culture-based methods such as heterotrophic plate counts have been
applied to evaluate microbial numbers in leachates (Barlaz et al. 1989; Boothe et al.
2001). However, the utility of culture-based methods to enumerate and characterize
environmental microorganisms is limited due to the highly specific and stringent growth
requirements or many microorganisms, coupled with the need of many organisms to
function as a consortium (Ward et al. 1992; Amann et al. 1995). Results from culture-
based methods yield a biased subset of the total population and are not effective for
assessing the microbial community structure (Hugenholtz and Pace 1996). Molecular
methods such as fluorescence in-situ hybridization (FISH), denaturing gradient gel
electrophoresis (DGGE), and sequencing of cloned DNA (clone libraries) have proven to
be useful in identifying microbial species and community diversity in landfill waste
8
(Roling et al. 2001; Huang et al. 2002; Burrell et al. 2004; Huang et al. 2005). Complex
community profiles can be statistically analyzed to determine the degree of diversity and
levels of similarity among microbial populations in any given microbial ecosystem
(Fromin et al. 2002).
Microbial ecosystem studies focus on microbial interactions with their environments and
changes occurring in the community structures in response to shifts in environmental
parameters. The extent of microbial diversity can be effectively captured by molecular
biological techniques like genetic fingerprinting, temperature gradient gel electrophoresis
(TGGE) and denaturing gradient gel electrophoresis (DGGE) (Muyzer and Smalla 1998).
Bands from DGGE and TGGE gels can be excised and used for subsequent sequencing
reactions. Complex community profiles obtained by using these techniques can be
analyzed using statistical programs to determine the degree of diversity and levels of
similarity between microbial populations in any given microbial ecosystem (Demba
Diallo 2004). Huang et al used cloning and restriction fragment length polymorphism
(RFLP) analysis to outline the archaeal community structure in landfill leachate (Huang
et al. 2002). The cloning and sequencing approach was also used to study the bacterial
diversity in landfill leachate by Huang et al in another experiment (Huang et al. 2005).
Roling et al performed cloning and DGGE profiling of landfill leachate polluted aquifers
to determine the correlation between the microbial community structure and
hydrochemistry in the aquifers (Roling et al. 2001). To the best of our knowledge, no
previous study has followed changes in archaeal and bacterial DGGE profiles over an
extended period as attempted in this study.
9
Landfill management practices
Typically, leachate that is formed in landfills flows into leachate collection systems that
are installed beneath a drainage layer, and is subsequently either recirculated through the
landfill, treated on-site, or treated at a wastewater treatment facility. In some cases,
clogging of leachate collection systems can occur and prevent drainage of the landfill,
resulting in waste submergence (Fleming et al. 1999). Waste submergence causes
accumulation of toxic compounds, inhibiting microbial activity and slowing waste
degradation. Leachate may also leak through compromised pipes and contaminate the
groundwater (Fleming et al. 1999). A host of factors have been implicated in the clogging
of landfill leachate collection systems including waste characteristics (Cardoso et al.
2006), physical-chemical parameters (Ledakowicz and Kaczorek 2004; VanGulck 2004),
and drainage system design (Rowe 2000b), however, limited information has been
reported on the temporal variations of the microbial community structure associated with
the clogging phenomenon. The deposition of inorganic chemicals such as calcium
carbonate and hydroxyapatite, as well as the presence of biofilms presumably result in the
phenomenon of clogging of leachate collection systems, thereby reducing the life of the
landfill (Rowe 2000a; b; 2002). The “life” of a landfill is directly related to its capacity
(area available for waste disposal) and the amount of waste deposited over a period of
time. Upon “closure”, the landfill must be overlaid with a flexible membrane liner and
earthen material and monitored for the next thirty years.
Bioreactor studies
The heterogeneous nature of the waste deposited in landfills makes it particularly
challenging to design sampling strategies and to conduct analysis of leachate
10
composition. Therefore, several studies have employed the use of laboratory-scale
bioreactors or lysimeters to simulate the conditions in landfills (Griffin et al. 1997;
Pohland and Kim 2000; Burrell et al. 2004; Fleming 2004; Ledakowicz and Kaczorek
2004; Calli et al. 2005). Bioreactors make it possible to control or modify the various
factors that influence waste degradation such as temperature, moisture and accumulation
of toxic compounds. Furthermore, bioreactors facilitate the investigation of the effect of
manipulating one or more of these variables on the overall process of waste
decomposition.
Bioreactors have been employed to study the physical, chemical and microbiological
changes occurring during the degradation of waste deposited in landfills. Effects of
leachate recirculation, seasonal variation, amount of gas generation, development of
microbial populations, attenuation of pollutants, aerobic versus anaerobic waste
degradation and clogging of leachate collection systems have been the focus of several
studies. Stessel and Murphy (1992) studied the effect of moisture and air on the rate of
degradation with the aim of optimizing the quantities of these two variables to achieve
reduction of waste within minimal amount of time (Stessel 1992). A comparison of
laboratory versus in situ field bioreactors conducted in China revealed the feasibility of
the use of bioreactors to mimic refuse decomposition in landfills (Youcai et al. 2002).
Methanotroph community diversity and methane oxidation rates associated with different
plant covers were studied in bioreactors by using methanotroph diagnostic microarrays
(Stralis-Pavese et al. 2004). Burrell et al used methanogenic bioreactors to detect and
identify cellulolytic Clostridium populations involved in anaerobic waste degradation
(Burrell et al. 2004). In spite of the usefulness of bioreactors in reducing and controlling
11
variables associated with refuse decomposition, it is necessary to corroborate data
obtained from bioreactor studies with data from actual landfill operation.
Research goals
Hypothesis 1: Microbial community structure will vary in bioreactors as a function of
waste composition.
Methodology 1: Laboratory scale bioreactors were constructed and packed with only
MSW or MSW combined with biosolids and ash from waste-to-energy processes.
Leachate generated in the bioreactors was sampled once every week for a period of eight
months and analyzed for total cell concentrations and microbial community structure.
Hypothesis 2: Microbial populations in bioreactors will exhibit temporal shifts, reflecting
changes in the community structure with the progression of waste degradation.
Methodology 2: DGGE was used to evaluate the community development in terms of
dominant members of the Archaea and Bacteria, providing a broad snapshot of changes
in the microbial community over an eight-month period.
Hypothesis 3: Microbial concentrations in bioreactors supplemented with biosolids will
be higher than in bioreactors packed with MSW only.
Methodology 3: Total microbial concentrations in the leachate sampled from bioreactors
with different waste composition were assessed by fluorescence microscopy.
Hypothesis 4: Methanogens belonging to many different genera will be identified in the
initial sample due to the complexity of the waste. As the waste “matures”, the sequence
diversity of methanogens will decrease.
12
Methodology 4: Methanogen populations were analyzed from an early (day 50) sample
and a late (day 218) sample of leachate by cloning and sequencing of the mcrA gene
coding for the alpha subunit of the methyl coenzyme-M reductase (MCR) enzyme.
Hypothesis 5: Fecal coliforms and enterococci will be present at high concentrations
during the initial stages of waste degradation. Other microbial community members such
as cellulose degraders, sulfate reducing bacteria and methanogens will outcompete the
indicator organisms as waste degradation progresses.
Methodology 5: Survival of indicator organisms such as fecal coliforms and enterococci
during the process of waste degradation was studied by membrane filtration of leachate
samples onto selective media. One bioreactor was subjected to a year long “dry period”
and indicator organism concentrations in the leachate were evaluated after rehydration of
the bioreactor.
Novel aspects of this study include the temporal profiling of Archaea and Bacteria
populations using DGGE, identifying and comparing methanogen populations during the
initial and later stages of waste decomposition and investigating the survival capability of
indicator organisms through the waste degradation process. Portions of this work were
published as a paper titled “Microbial population dynamics in solid waste bioreactors in
the presence or absence of biosolids” in the Journal of Applied Microbiology (107(4):
1330-1339).
13
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23
MICROBIAL POPULATION DYNAMICS IN LABORATORY-SCALE SOLID
WASTE BIOREACTORS IN THE PRESENCE OR ABSENCE OF BIOSOLIDS
Abstract
Aims: Decomposition of solid waste is microbially mediated, yet little is known about the
associated structure and temporal changes in prokaryotic communities. Bioreactors were
used to simulate landfill conditions and archaeal and bacterial community development in
leachate was examined over eight months.
Methods and Results: Municipal solid waste (MSW) was deposited in laboratory
bioreactors with or without biosolids and combustion residues (ash). The near-neutral pH
fell about half a log by day 25, but recovered to ~7.0 by day 50. Cell concentrations in
bioreactors containing only MSW were significantly higher than those from co-disposal
bioreactors. Archaeal and bacterial community structure was analyzed by denaturing
gradient gel electrophoresis (DGGE) targeting 16S rRNA genes, showing temporal
population shifts for both domains. mcrA sequences retrieved from a co-disposal
bioreactor were predominantly affiliated with the orders Methanosarcinales and
Methanomicrobiales.
Conclusion: Regardless of waste composition, microbial communities in bioreactor
leachates exhibited high diversity and distinct temporal trends. The solid waste filled
bioreactors allowed simulation of solid waste decomposition in landfills while also
reducing the variables.
24
Significance and Impact: This study advances the basic understanding of changes in
microbial community structure during solid waste decomposition, which may ultimately
improve the efficiency of solid waste management.
Introduction
Disposal of municipal solid waste (MSW) in landfills supports the development of
diverse microbial populations (Kjeldsen et al. 2002). The composition of microbial
communities is influenced by many factors such as the types of wastes deposited,
moisture availability, oxidation-reduction states, and temperature (Barlaz et al. 1989a;
Kjeldsen et al. 2002). Co-disposal of waste includes MSW and other types of wastes,
including biosolids from wastewater treatment facilities, ash residues from waste-to-
energy and other combustion processes, electronic wastes, construction and demolition
wastes.
Understanding microbial population development in landfills over a period of time is
challenging due to the complexity of waste materials deposited and the spatial
heterogeneity of landfills. Previous studies have focused on particular aspects of
microbial populations in waste degradation processes. Group-specific primers were
employed to detect cellulolytic clostridia (Van Dyke and McCarthy 2002) and fungi
(Lockhart et al. 2006) in landfill leachate. Quantitative real time PCR was used to study
the development of type I methanotrophic communities during composting of organic
matter (Halet et al. 2006) and to determine the abundance of cellulolytic Fibrobacter
species in landfills (McDonald et al. 2008). Sequencing of cloned DNA (clone libraries)
was used to study archaeal populations in the leachate of a full-scale recirculating landfill
and bacterial populations in the leachate of a closed landfill (Huang et al. 2002; Huang et
25
al. 2005). Several studies have also investigated methanogenic Archaea populations in
landfills (Huang et al. 2003; Uz et al. 2003), yet none of them attempted a temporal
comparison.
Methanogenesis is a process that generates useful methane gas during waste degradation
in landfills (Barlaz et al. 1989a; Senior et al. 1990). However, methane recovery rates are
affected by waste composition, microbial degradation dynamics and economic feasibility.
Methane is also a potent greenhouse gas and landfills account for 34 % of all methane
emissions (U.S. EPA 1999). Methanogens can be detected and analyzed using molecular
techniques such as fluorescence in-situ hybridization (FISH) (Calli et al. 2005) and
construction of 16S rDNA clone libraries (Huang et al. 2003; Mori et al. 2003) or
biomarkers (Hales et al. 1996; Nercessian et al. 1999; Earl et al. 2003; Dhillon et al.
2005). Functional genes are used as biomarkers because their higher evolutionary rates
can enhance the resolution of sequences at the species level compared to the 16S rRNA
gene (Braker et al. 2000; Junca and Pieper 2004).
Conditions characteristic of solid waste degradation in a landfill were mimiced in
bioreactors filled with solid waste and maintained in the laboratory. The chemical data
associated with the study have been published (Cardoso et al. 2006). The microbial data
that were collected simultaneously with the chemical data are presented in this work. We
hypothesized that the microbial community development would vary in bioreactors with
different waste composition. Denaturing gradient gel electrophoresis (DGGE) was used
to evaluate the community development in terms of dominant members of the Archaea
and Bacteria, providing a broad snapshot of changes in the microbial community over an
eight-month period. Methanogen sequences from leachate samples were obtained using
26
primers targeting the mcrA gene coding for the alpha subunit of the methyl coenzyme-M
reductase (MCR) enzyme and compared over time.
Materials and Methods
Bioreactor design
Bioreactors were designed to simulate landfill disposal practices in the U.S. They were
constructed from 1.4 m tall, 30.5 cm diameter PVC pipes. The waste mixtures were
hydrated to field capacity and leachate was recirculated daily to simulate rainfall of 8 cm
d-1
. The leachate collection system was designed to simulate field conditions and
consisted of a perforated 32 mm diameter PVC. The leachate collection pipes were
surrounded by gravel (50.8 mm) with geotextiles above and below the gravel layers. The
drainage system separating the waste from the leachate collection pipe consisted of five
inches of granular material (25.4 mm gravel or Cholee sand). The bioreactors were
operated in duplicate. A diagram of the bioreactor design is presented in our previously
published paper (Cardoso et al. 2006).
Four bioreactors were filled with either MSW alone or MSW co-disposed with biosolids
and combustion residues from waste-to-energy facilities. The waste materials were
obtained from the North County Resource Recovery Facility in Palm Beach County, FL.
Duplicate bioreactors were packed with either 100% MSW or 60% MSW co-disposed
with 30% combustion residues (6% fly ash + 24% bottom ash) and 10% biosolids
(comprised of 50% material from drinking water treatment + 50% material from
wastewater treatment).
27
Sample processing
Leachate samples were collected and three milliliters of each sample was filtered through
0.45 µm filters and the filters were stored at -20°C. Community DNA was extracted from
the filters using the Ultraclean Soil DNA Kit (MoBio Laboratories, Inc.) per
manufacturer’s instructions. The extracted DNA was stored at -20°C until further
processing (1 week maximum).
Total microbial concentrations
Duplicate one ml samples of leachate from each bioreactor were individually centrifuged.
The cells were washed in sterile phosphate buffered saline (PBS), stained with DAPI
(4,6-diamidine-2-phenylindole) (1mg ml-1
DAPI) and filtered through a 0.2 µm
polycarbonate filter (Millipore). The stained cells were observed under a fluorescent
microscope using a UV2B filter. Cells from 5 different fields of view were counted and
the average was used to calculate the cell concentration ml-1
of sample. Measurements in
duplicate samples varied from one another by less than 10%.
Polymerase chain reaction (PCR) for community analysis
Direct amplification of the 16S rRNA genes of Archaea using the primer set 344f and
517r was not consistently successful; therefore a nested approach was used. The first
round of PCR was performed using the primer set 21f and 958r (Delong 1992; Pearson et
al. 2004). Acetamide was added to a final concentration of 2% (vv-1
) to increase the
specificity of the reaction. PCR conditions were as follows: initial denaturation at 94°C
for 3 min, followed by 30 cycles of denaturation at 94°C for 45 s, annealing at 55°C for
45 s, extension at 72°C for 1 min, and a final extension at 72°C for 5 min. This procedure
28
was followed by a second round of PCR using the Archaea-specific forward primer 344f
and a universal reverse primer 517r (Bano et al. 2004; Pearson et al. 2004). A 40 bp GC-
clamp was attached to the 5’-end of the forward primer. Acetamide was excluded from
the reactions. Templates were amplified using a “touchdown” PCR to increase primer
specificity (Ferrari and Hollibaugh 1999). The archaeal primer sets used in this study
amplify both euryarchaeotes and crenarchaeotes and do not amplify non-archaeal
templates (Delong 1992; Raskin et al. 1994). Methanosarcina acetivorans strain C2A
(DSM 2834) was used as the positive control.
Bacterial 16S rRNA genes were amplified using the primer set 1070f and 1392r (Ferris et
al. 1996). A 40 bp GC-clamp was attached to the 5’-end of the reverse primer. PCR
conditions were the same as those used for the first round of archaeal amplification.
Escherichia coli ATCC 9637 was used as the positive control.
Denaturing gradient gel electrophoresis (DGGE)
DGGE was carried out using the Bio-Rad DCode Universal Mutation Detection System.
A 1mm thick 7% (w v-1
) polyacrylamide gel containing a 40%- 65% linear denaturing
gradient of formamide and urea (100% denaturant = 7 M urea and 40% (v v-1
)
formamide) was prepared for archaeal community analysis whereas a 45%-60% gradient
was used for bacterial community analysis.
DGGE standards were created by loading GC-clamped PCR products (approximately 150
to 300 ng total DNA) of the small subunit rRNA gene from Aiptasia pallida (brown sea
anemone) (18S rRNA), Gallus domesticus (chicken) (18S rRNA), Methanosarcina
acetivorans strain C2A (DSM 2834) (16S rRNA), Clostridium perfringens (Sigma
D5139) (16S rRNA), Escherichia coli ATCC 9637 (16S rRNA) and Streptomyces fradiae
29
ATCC 10745 (16S rRNA) mixed with 10 µl of loading dye. Two standard lanes were
loaded per gel. Approximately 650 to 800 ng (total) of PCR product amplified from
leachate samples was loaded in individual lanes of the gel. Gels were electrophoresed at
47V, 60°C for 16 h, stained with SYBR Green I and images were obtained using a Foto/
Analyst Imaging System (Fotodyne Inc).
Cloning and sequence analysis of mcrA gene
Since co-disposal (MSW + ash + biosolids) is a widespread method of waste disposal,
one of the two co-disposal bioreactors was selected for the study of methanogen
populations. An early sample of leachate (day 50) and a late sample (day 218) were
selected for methanogen population analysis. DNA extracted from the two samples was
amplified using the ME1 and ME2 primers (Hales et al. 1996; Nercessian et al. 1999)
that target the mcrA gene. PCR conditions were as described previously (Hales et al.
1996). Methanosarcina acetivorans strain C2A (DSM 2834) was used as the positive
control.
The day 50 and day 218 amplicon bands (760 bp) were excised from the gel using a
QIAQuick Gel Extraction Kit (Qiagen, CA), as per manufacturer’s instructions. The
TOPO TA Cloning Kit for Sequencing (Invitrogen, CA) was used for both cloning and
transformation (as per manufacturer’s instructions). The vectors (plasmids) were
subsequently transformed into One Shot TOP10 chemically competent Escherichia coli
cells (Invitrogen, CA). Cells were gently plated onto Luria broth (LB) agar plates
amended with 100 μg ml-1
ampicillin and individual colonies were re-streaked on new
plates. Plasmids were extracted using the FastPlasmid Mini kit (Eppendorf, Hamburg,
Germany) per manufacturer’s instructions. Insert DNA from the extracted plasmids was
30
amplified using the ME primer set and confirmed by agarose gel electrophoresis. PCR
reactions were purified using QIAQuick PCR Purification Kit (Qiagen, Valencia, CA).
The Genome Lab DTCS-Quick Start Kit (Beckman Coulter, Fullerton, CA) was used for
the final sequencing PCR reaction. The amplified DNA was purified, concentrated by
ethanol precipitation and sequenced using a Beckman CEQ™ 8000 Genetic Analysis
System (Beckman Coulter). Sequences were analyzed using BLAST
(http://www.ncbi.nlm.nih. gov/BLAST/) to confirm their identities as methanogens.
Possible methanogen sequences were designated JME for clones from the day 50 sample,
and DME for clones from the day 218 sample.
Statistical analysis
Gel images were imported into Bionumerics (Version 3.0, Applied Maths, Belgium) and
analyzed using the Dice similarity coefficient by constructing unweighted pair group
method with arithmetic mean (UPGMA) dendrograms (optimization 1.0%, tolerance
0.5%). Principal components analysis (PCA) was performed using SPSS (SPSS Inc.,
Chicago, IL) to obtain two-dimensional plots showing relatedness of populations. PCA is
a data reduction technique which takes into account all the variables in a given set,
determines the patterns of similarities and differences between the variables and
expresses the results as a two or three-dimensional plot. For fingerprint patterns such as
those obtained by DGGE or RFLP, bands are classified as present or absent (binary) and
compared by constructing a band-matching table (Boon et al. 2002; Caddick et al. 2006).
Microbial concentrations (direct microscopic cell counts) were compared by paired t-tests
(GraphPad Instat). Shannon diversity index is a measure of the richness (number of
31
species present in a community) and abundance of any ecological habitat. Shannon
diversity index (H) was calculated by using the formula:
H = - ∑ pi log pi
calculated as pi = ni/ N where ni is the height of a peak and N is the sum of the peak
heights of all bands in the densitometric curve (Eichner et al. 1999; Ogino et al. 2001;
Haack et al. 2004).
Shannon indices were calculated for the community profile of each given time point of
individual bioreactors and an average of these was reported.
Results
Total cell concentrations
Total cell concentrations (measured by epifluorescence microscopy) in the first month of
bioreactor operation increased about an order of magnitude, from 3 x 108 cells ml
-1 to 3 x
109 cells ml
-1 in MSW bioreactors and co-disposal bioreactors. Cell concentrations
dropped after 50 days, then stabilized in all bioreactors except MSW1. Cell
concentrations varied only about four-fold from bioreactor start-up to the termination of
the experiment. There was a significant difference between the mean cell concentrations
of the two MSW bioreactors calculated over the course of the study (paired t-test; P <
0.0001). The difference was attributable largely to the increased cell concentrations in the
second half of the experiment in MSW1. In contrast, the difference in cell concentrations
for the two co-disposal bioreactors was not significant (P > 0.95). Cell concentrations in
leachate from MSW bioreactors were significantly greater than those from co-disposal
bioreactors (P = 0.048).
32
pH of leachate
The initial increase in microbial numbers in all bioreactors corresponded with an initial
decrease in pH from neutral to 5.5 by day 25. The pH returned to neutral by day 50 and
remained neutral till the conclusion of the experiment.
DGGE analysis of microbial communities
An initial experiment was performed to test the reproducibility of DGGE in the complex
matrix of the leachate. Triplicate leachate samples taken within several minutes of one
another were analyzed from a selected bioreactor (Co-disposal 2). The similarity of the
DGGE patterns for triplicate analyses of both archaeal and bacterial community structure
based on 16S rRNA genes were greater than 95%, showing high reproducibility of the
method (data not shown).
Leachate samples collected on 12 to 15 dates over a period of eight months were
subjected to DGGE. The first incidence of detection of Archaea by PCR using 16S rRNA
genes corresponded to the occurrence of negative oxidation-reduction potentials and the
presence of volatile acids, indicating anaerobic conditions (Cardoso et al. 2006), on day
25. Inhibition of the PCR was not responsible for the absence of archaeal PCR products
in the early leachate samples, as positive control DNA spiked into these leachates was
amplified.
Archaeal community structure
Analysis of archaeal DGGE patterns indicates that in all the bioreactors, the initial
population changed substantially between start-up (day 25) and maturation (day 50)
(Figure 1). The data from a representative bioreactor for each treatment (MSW or co-
33
disposal) are shown in Figure 1a and 1b, respectively. In the MSW bioreactors archaeal
community fingerprints over a two-month period (day 50 to day 78, designated cluster I)
clustered together followed by a substantial change in the community at day 99 (Figure
1a). Cluster II, which includes patterns from day 120 to day 218, denotes a cluster of
comparatively similar patterns towards the end of the study. In the co-disposal
bioreactors, archaeal DGGE patterns after bioreactor start-up indicated a more gradual
shift in the community structure with less well-defined clusters (Figure 1b). Principal
components analysis (PCA) of the DGGE patterns also demonstrated temporal shifts in
communities (Figure 1a and 1b).
Bacterial community structure
Analysis of bacterial DGGE patterns revealed that the patterns in MSW bioreactors were
clustered in discrete groups of high (>75%) similarity. Cluster I included day 1 to day 25,
cluster II day 50 to 99 and cluster III day 169 to 218 (Figure 2a). In contrast, the patterns
in co-disposal bioreactors shifted in a more gradual manner (Figure 2b). The data from
one bioreactor per treatment are shown in Figure 2; relationships among the patterns of
duplicate bioreactors were similar for both MSW and co-disposal treatments. PCA results
for bacterial community structure also corresponded to the results obtained from the
UPGMA dendrograms (Figure 2a and 2b).
Microbial community structure was more similar within bioreactors than between
bioreactors of the same treatment (data not shown). This grouping was consistently
observed for Archaea and Bacteria in MSW and co-disposal bioreactors. Comparison of
communities in MSW vs. co-disposal bioreactors did not reveal grouping by treatment
34
(waste type); thus, factors other than the waste composition were most instrumental in
determining community structure.
Calculation of the Shannon diversity indices revealed a higher apparent diversity of
observed archaeal populations (average H = 1.37) in the bioreactors as compared to the
bacterial populations (average H = 1.24) (Table 1).
Sequence analysis of methanogens
Seventeen unique mcrA gene sequences were found out of the thirty-seven clones
analyzed for the day 50 leachate sample of the co-disposal bioreactor (Table 2). The most
numerically dominant clone was closely related to the uncultured methanogen clone RS-
ME43 isolated from rice field soil (Lueders et al. 2001). Methanogen clones from the day
50 leachate sample were closely related to the members of Methanosarcinales,
Methanobacteriales and Methanomicrobiales. Twelve unique mcrA gene sequences were
found out of the thirty-two clones analyzed for the day 218 leachate sample (Table 2).
The most dominant clone was closely related to the uncultured methanogen clone
MidMcrA114 isolated from the sediment of the Pearl River Estuary (Jiang et al. 2008,
unpublished material). All the methanogen clone sequences from the day 218 leachate
sample were related to members of the Order Methanomicrobiales.
35
Table 1. Shannon diversity indices calculated for the DGGE patterns of Archaea and
Bacteria.
Treatment Archaea Bacteria
Shannon Diversity
index (H)
MSW1 1.374 1.195
MSW2 1.384 1.218
Co-disposal1 1.361 1.241
Co-disposal2 1.367 1.296
36
1a.
1b.
Figure 1. Dendrograms and corresponding principal components analysis of archaeal
population structure in (a) MSW and (b) co-disposal bioreactors. I, II, III and IV denote
clusters of similar patterns(>75% similarity). Note that Archaea were not detected in the
leachate before day 25. (Clustering patterns of duplicate bioreactors for both MSW and
co-disposal were similar). Arrow indicates increasing direction of denaturant and
acrylamide gradient from lower to higher concentration.
II
100 80 60
Day 50
Day 92
Day64
Day 78
Day 99
Day 25
Day 136
Day 169
Day 120
Day 184
Day 204
Day 218
I
II
I
III
100 80 60
Day 50
Day 78
Day 64
Day 92
Day 99
Day 120
Day 136
Day 169
Day 184
Day 25
Day 204
Day 218 IV
37
2a.
2b.
Figure 2. Dendrograms and corresponding principal components analysis of bacterial
population structure in (a) MSW and (b) co-disposal bioreactors. I, II, III, IV and V
denote clusters of similar patterns (>75% similarity). (Clustering patterns of duplicate
bioreactors for both MSW and co-disposal were similar). Arrow indicates increasing
direction of denaturant and acrylamide gradient from lower to higher concentration.
III
II
I
100 80
Day 4
Day 11
Day 1
Day 25
Day 64
Day 78
Day 50
Day 92
Day 99
Day 169
Day 184
Day 204
Day 218
100 80 60
IV II
I
V
Day 92
Day 99
Day 120
Day 78
Day 50
Day 64
Day 11
Day 25
Day 1
Day 4
Day 184
Day 204
Day 136
Day 169
Day 218
III
II
38
Table 2. Frequency of methanogen clones observed in the day 50 and day 218 samples
obtained from the co-disposal bioreactor.
Clonea, b Putative Group
(Order) Closest Relatives
% of
clone
library
JME1 (FJ435818) Methanomicrobiales Methanobacterium sp.
MB4 (DQ677519) 3
JME2, 5-7
(FJ435819,
FJ435822, FJ435823,
FJ435824)
Methanomicrobiales Methanocorpusculum
parvum (AY260445) 11
JME3 (FJ435820) Methanosarcinales Rice field soil clone
RS-ME28 (AF313863) 3
JME4,8 (FJ435821,
FJ435825) Methanomicrobiales
Biogas plant clone ATB-
EN-5737-M022
(FJ226633) 5
JME11-13,15,36
(FJ435826,
FJ435827, FJ435828,
FJ435830, FJ435849)
Methanosarcinales
Methanosarcina mazei
strain TMA
(AB300778) 14
JME14,22-
27,30,37,43
(FJ435829,
FJ435836, FJ435837,
FJ435838, FJ435839,
FJ435840, FJ435841,
FJ435844, FJ435850,
FJ435854)
Methanosarcinales Rice field soil clone
RS-ME43 (AF313876) 27
JME16,18,19
(FJ435831,
FJ435833, FJ435834) Methanosarcinales
Methanosarcina sp.
HB-1 Subsurface
groundwater clone
(AB288266)
8
JME17,32 (FJ435832,
FJ435846) Methanosarcinales
Methanosarcina
barkeri mcrBCDGA
(Y00158) 5
JME20 (FJ435835) Methanosarcinales
Nankai trough marine
sediment core clone
NANK-ME73121
(AY436550)
3
JME28 (FJ435842) Methanobacteriales Anaerobic digester 3
39
Clonea, b Putative Group
(Order) Closest Relatives
% of
clone
library
clone ME_dig80_2_15
(DQ680456)
JME29 (FJ435843) Methanobacteriales
Bovine rumen clone
unfaunated-mcrA-13
(AB244709) 3
JME31 (FJ435845) Methanosarcinales
UASB bioreactor clone
GranMCR7M10
(AY937278) 3
JME34 (FJ435847) Methanomicrobiales
Cattle manure clone
G4INMC365
(DQ262403) 3
JME35 (FJ435848) Methanobacteriales
Human fecal sample
clone DC_clone-mcrA-
2 (AM921682) 3
JME38 (FJ435851) Methanosarcinales
Upland pasture soil
clone SI_18
(DQ994847) 3
JME40 (FJ435852) Methanosarcinales
Methanosarcina mazei
strain LYC
(AB300782) 3
JME41 (FJ435853) Methanomicrobiales
Cattle manure clone
G4INMC365
(DQ274999) 3
DME1,35 (FJ435855,
FJ435885) Methanomicrobiales
Biogas plant clone
ATB-EN-9759-M148
(FJ226741) 6
DME2,3,8,31
(FJ435856,
FJ435857, FJ435862,
FJ435881)
Methanomicrobiales Methanocorpusculum
parvum (AY260445) 13
DME4,5,7
(FJ435858,
FJ435859, FJ435861) Methanomicrobiales
Biogas plant clone
ATB-EN-5737-M022
(FJ226633) 9
DME6,19,30
(FJ435860,
FJ435871, FJ435880) Methanomicrobiales
Biogas plant clone
MARMC548
(DQ260615) 9
DME9 (FJ435863) Methanomicrobiales Methanocorpusculum 3
40
Clonea, b Putative Group
(Order) Closest Relatives
% of
clone
library
labreanum Z
(CP000559)
DME10,20-22
(FJ435864,
FJ435872, FJ435873,
FJ435874)
Methanomicrobiales
Lake sediment clone
Beu4ME-34
(AY625600) 13
DME11,16-
18,25,33,34,38
(FJ435865,
FJ435868, FJ435869,
FJ435870, FJ435877,
FJ435883, FJ435884,
FJ435886)
Methanomicrobiales
Estuary sediment clone
MidMcrA114
(EU681946) 25
DME13 (FJ435866) Methanomicrobiales Methanocorpusculum
sp. MSP (AY260446) 3
DME15,27
(FJ435867,
FJ435879) Methanomicrobiales
Biogas plant clone
F10RTCR23
(DQ261495) 6
DME23 (FJ435875) Methanomicrobiales Sewer clone
(EF628141) 3
DME24,26
(FJ435876,
FJ435878) Methanomicrobiales
Gas condensate-
contaminated aquifer
clone L44B
(EU364876)
6
DME32 (FJ435882) Methanomicrobiales
Biogas plant clone
G8RTCR50
(DQ260503) 3
aAll isolates labeled JME denote clones obtained from day 50 sample
bAll isolates labeled DME denote clones obtained from day 218 sample
Discussion
The various cells of a landfill generally contain waste that is at different stages of
decomposition depending upon waste composition, residence time, moisture, etc., making
it difficult to study the progression of microbial population development in the highly
41
heterogeneous landfill environment. Compared to landfills, bioreactors are simpler
systems that allow better control over the variables that play a role in waste degradation.
As a simplified system, bioreactors have been employed in other studies to study
microbial community dynamics (Barlaz et al. 1989a), cellulose degrading Clostridium
populations (Burrell et al. 2004), methanogen communities (Griffin et al. 1997) and
chemical composition of leachate (Pohland and Kim 2000; Ledakowicz and Kaczorek
2004).
In spite of the attempt to construct parallel (duplicate) bioreactors in this study, cell
concentrations in duplicate MSW bioreactors varied significantly over the course of the
experiment, while these values in co-disposal bioreactors, which included biosolids, were
similar. The variable cell concentrations between duplicate MSW bioreactors may be
attributable to the heterogeneous nature of the waste, which was shredded municipal
waste obtained from a landfill in Palm Beach County, FL. This waste contained paper,
plastic, food, and other common components of garbage, and was not standardized. This
heterogeneity probably also contributed to the dissimilarity of community profiles in
duplicate bioreactors. These results underscore the complexity of determining the factors
that influence processes such as clogging, which can lead to landfill failures and
management problems (Rohde and Gribb 1990; Fleming et al. 1999).
A common trend seen in all bioreactors was a decrease in pH, which was consistently
accompanied by a rise in cell concentrations over the first 25-30 days of the study. This
trend reflects the successive processes that typically occur during solid waste
degradation. During the early stages, aerobic and facultative anaerobic heterotrophs
decompose organic substrates and quickly exhaust oxygen and nitrate (Barlaz et al.
42
1989a). Cellulose and hemicellulose comprise a high percentage (45% – 60%) of
landfilled material and are easily biodegradable (Barlaz et al. 1989b). Cellulose
degradation is performed by hydrolytic and fermentative bacteria and fungi in the
primarily anaerobic conditions of the landfill (Pourcher et al. 2001; Van Dyke and
McCarthy 2002; Burrell et al. 2004; Lockhart et al. 2006; McDonald et al. 2008).
Fermentative bacteria use monosaccharides and amino acids to produce alcohols, organic
acids, carbon dioxide and hydrogen (Barlaz et al. 1989a), which causes more acidic
conditions (lower pH). Methanogens utilize carbon dioxide, hydrogen, acetate, and
formate resulting in an increase in pH (Mormile et al. 1996). Removal of acetate by iron
reducing bacteria (Frenzel et al. 1999; Lin et al. 2007) and bicarbonates produced by
sulfate-reducing bacteria (SRBs) (Elliott et al. 1998) could also cause increase in pH of
leachates After the initial decrease in pH at day 25, pH values increased to near neutral
and remained at that level through the conclusion of the study.
Another common trend among bioreactors was that, irrespective of waste composition
(MSW vs. co-disposal), archaeal populations exhibited higher apparent diversity as
assessed by DGGE than the bacterial populations. To the best of our knowledge, no
previous study has compared the diversity in 16S rRNA sequences of archaeal vs.
bacterial populations in decomposing solid waste. The emergence of a diverse population
of Archaea about 25 days after inoculation and its maintenance throughout the study
reflects the process of succession and ultimately maturation of the microbial community
in the bioreactors. Although DGGE reflects a broad community structure, like any other
molecular technique, it is subject to biases and errors such as selective amplification,
heteroduplex formation and co-migration of DNA fragments (Muyzer and Smalla 1998).
43
However, DGGE is widely employed in ecological studies because it enables quick and
convenient comparison of temporal and spatial distributions of the predominant members
in a population as compared to other molecular methods such as cloning and sequencing.
Analysis of microbial community structure in the laboratory bioreactors revealed
temporal shifts of archaeal and bacterial populations in bioreactors regardless of the
waste content, suggesting a succession process from an immature to a mature community.
These results concur with other studies that demonstrated succession during solid waste
decomposition. For example, a study on composting of MSW using phospholipid fatty
acid analysis (PLFA) to identify operational taxonomic units suggested four stages of
waste degradation (Herrmann and Shann 1997). Two studies using culture-based
methods of microbial identification also found population shifts that suggested succession
processes (Barlaz et al. 1989a; Boothe et al. 2001) . Interestingly, microbial populations
in MSW bioreactors were clustered into groups with high similairty, whereas a gradual
change was detected in microbial populations from co-disposal bioreactors. The diverse
inoculum provided by the biosolids may have contributed to the gradual change in
community structure for the co-disposal bioreactors.
Methanogens belong to the archaean kingdom Euryarchaeota (Winker and Woese 1991;
Barns et al. 1996), and are divided into five orders: Methanosarcinales,
Methanomicrobiales, Methanobacteriales, Methanococcales and Methanopyrales
(Bapteste et al. 2005). Our study found representatives from the Methanosarcinales,
Methanobacteriales and Methanomicrobiales in the initial stages of waste degradation
(day 50 sample). Interestingly, the later stages (day 218 sample) were exclusively
dominated by members of the Methanomicrobiales, which include genera such as
44
Methanocorpusculum and Methanoculleus. Several of the sequences obtained in this
study were most similar to cultured methanogens, e.g. Methanobacterium sp. MB4,
Methanosarcina mazei and Methanocorpusculum parvum while others were most similar
to sequences obtained from uncultured organisms found in a variety of environments,
including biogas plants, rice field soil, subsurface sediments and the bovine rumen.
Previous studies of methanogens in leachate from bioreactors or landfills identified a
number of phylogenetic groups including Methanosaeta and Methanobacteriaceae (Calli
et al. 2003), Methanomicrobiales (Methanoculleus and Methanofollis) and
Methanosarcinales (Methanosaeta and Methanosarcina) (Uz et al. 2003).
Methanosarcina sp., Methanobacterium sp. and Methanocorpusculum sp. were isolated
from anaerobic sewage sludge digestors (Bryant and Boone 1987; Raskin et al. 1994;
Griffin et al. 1997; Whitehead and Cotta 1999).
Despite the great variability in leachate microbial community profiles, the temporal trend
in microbial community structure was consistently observed for archaeal and bacterial
populations in the simulated solid waste bioreactors. Gaining an understanding of the
environmental factors that influence these high-diversity communities will require
extensive research, but this effort is justified by the potential for using this knowledge to
improving landfill management practices.
Acknowledgements
This study was funded by the Hinkley Center for Solid and Hazardous Waste
Management, Gainesville, FL.
45
We thank Lisa Rhea and Robert Ulrich for help in constructing, maintaining and
sampling the bioreactors, and Ee Goh for DNA sequencing.
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53
SURVIVAL OF INDICATOR ORGANISMS DURING WASTE DEGRADATION IN
SIMULATED LANDFILL BIOREACTORS
Abstract
Landfill waste contains microbial pathogens and fecal indicator organisms (IOs). Solid
waste degradation is expected to reduce their populations, yet little is known about the
factors that influence their persistence in landfills. This study was designed to follow
changes in IO concentrations during the process of waste degradation (“hydrated” phase)
and to determine the fate of IOs in the waste after an extended period of moisture
deficiency (“dehydrated” phase). Landfill conditions were simulated by constructing
bioreactors packed with municipal solid waste (MSW), combustion residues and
biosolids from wastewater treatment facilities. Leachate generated by moistening MSW
was sampled weekly for seven months and analyzed for total cells by fluorescence
microscopy and for culturable IO concentrations. Total live cell concentrations in
leachate were measured by BacLight staining. They decreased from 1.9 x 109 cells/ml to
4.9 x 108 cells/ml after seven months. Fecal coliform concentrations decreased from 6.3 x
104 CFU/ml to below detection limits while Enterococcus concentrations decreased from
1 x 106 CFU/ml to below detection limits by the end of the second month of bioreactor
operation and remained undetectable for the rest of the hydrated phase. Decay rates for
fecal coliforms were not significantly different from the decay rates for enterococci in
both bioreactors. A one-year drying period was followed by rehydration and a 24-h
sampling regimen. Total cell concentrations increased from ~107 cells ml
-1 immediately
after rehydration to ~108 cells ml
-1 after 24 hours. Hourly changes in the bacterial
community structure analyzed by denaturing gradient gel electrophoresis (DGGE)
54
analysis following rehydration revealed a fairly stable, low-diversity community. Fecal
coliforms and enterococci were detected at low concentrations (≤20 or 30 CFU/100 ml,
respectively) in the leachate even after the prolonged dry period. The recovery of IOs
after prolonged dehydration was unexpected, and raises questions about the fate of
pathogens during the process of waste degradation.
Introduction
The survival and composition of microbial communities in landfills depends on several
factors such as waste composition, pH, temperature, toxic metal levels and moisture
availability (Boothe et al. 2001). The amount of moisture available to support microbial
activity depends on local climatic conditions, i.e. rainfall and temperature, the
composition of waste deposited in the landfill, and the frequency of leachate removal
and/or recirculation (El-Fadel 1999). The majority of the waste deposited in landfills
comprises municipal solid waste (MSW) which includes household waste, newspapers,
glass, and metals (Kjeldsen et al. 2002). Co-disposal of ash from waste-to-energy (WTE)
processes and biosolids from wastewater treatment is also practiced in Class I landfills
(Reinhart 2003).
Biosolids are nutrient and moisture rich and add to the microbial load of the waste. One
major concern associated with deposition of biosolids in landfills is the possible presence
of pathogens that have survived the treatment process. Pathogens can also be introduced
in landfills from a variety of other sources such as soiled diapers, biomedical waste and
pet feces (Peterson 1974; Mose and Reinthaler 1985; Trost and Filip 1985; Gerba et al.
1995). Dissemination of human pathogens into the community can occur due to
groundwater contamination by landfill leachate. This potential risk is evaluated by
55
enumerating indicator organisms (IOs) such as fecal coliforms and enterococci in
groundwater that may be contaminated by leachate (Rose 2004).
Landfills undergo periods of moisture abundance during rainy periods and moisture
deprivation in most climates. Compaction of refuse reduces infiltration of water through
the waste layers, thereby pooling less leachate in the collection systems (Tatsi and
Zouboulis 2002) and minimizing the probability of leachate contaminating the
groundwater. In contrast, heavy rainfall dilutes toxic compounds in leachate and
accelerates refuse decomposition (Wreford et al. 2000) but increases the potential for
groundwater contamination. This study aims to evaluate the changes in concentrations of
the overall microbial population and IOs during moisture-rich and moisture-deprived
conditions. We hypothesize that indicator organisms will persist well in the moisture-rich
(“hydrated”) phase while being unrecoverable in the moisture-deprived (“dehydrated”)
phase of waste degradation.
For the hydrated phase of the study, bioreactors were constructed and operated in
duplicate and the total cell concentrations and IO concentrations in leachate were
enumerated for a period of seven months. Due to practical reasons and space restrictions,
only one bioreactor was operated for the dehydrated phase of the study. This bioreactor
was subjected to a dry period of one year, after which it was rehydrated and the leachate
was tested over a 24-hour period to determine total microbial concentrations,
heterotrophic plate counts and IO concentrations. Changes in the bacterial community
structure post-rehydration were also investigated by denaturing gradient gel
electrophoresis (DGGE).
56
Materials and methods
Landfill conditions were simulated in duplicate laboratory scale bioreactors built from
150 cm tall; 20 cm diameter PVC pipes (Figure 3). They were packed with 60%
municipal solid waste (MSW), 20% combustion residues (ash) from waste-to-energy
processes, 10% biosolids from wastewater treatment and 10% sand (inert material) (all
measurements by mass). Combustion residues were obtained from the Hillsborough
County Resource Recovery Facility (Covanta Hillsborough Inc.) at Falkenburg Road,
Tampa, FL. Biosolid material (dewatered sludge) was obtained from the Falkenburg
Road Advanced Wastewater Treatment Plant in Tampa, FL. The waste mixtures were
placed in the bioreactors and hydrated to field capacity with distilled water, which was
held for 24 hours before draining. The bioreactors were rehydrated with a mixture of 2.5
liters of drained leachate and 1 liter of distilled water.
Hydrated conditions were maintained by recirculating a mixture of leachate: distilled
water (2.5:1) three times a week for seven months. Leachate samples were collected and
analyzed weekly during the entire hydrated phase (see below), but were collected more
frequently after rehydration of the dehydrated bioreactor (see below). One bioreactor was
randomly chosen for the dehydration study. After a year-long dry period, the bioreactor
was rehydrated by adding distilled water (20 L) and leachate was sampled every hour for
the first eight hours (time zero to time seven) and once at the 24-hour mark.
57
Figure 3. Diagram of bioreactor configuration.
Total microbial concentrations were determined by direct fluorescence microscopy using
two staining methods. The more commonly used DAPI (4,6-diamidine-2-phenylindole)
stains the DNA of all cells and does not differentiate between live and dead cells. The
Live/Dead BacLight Bacterial Viability kit (Molecular Probes, Invitrogen) includes
SYTO 9 that stains both live and dead cells (green fluorescence) and propidium iodide
that stains cells with compromised cell membranes (red fluorescence). The advantage of
BacLight in differentiating live from membrane-damaged cells is counterbalanced by the
CAP
BASE
Leachate Distributor
Gas collection fitting
Molded PVC Cap
8-holed Vanstone
Flanges
8” dia. PVC Pipe
HDPE Lining
Supporting Gravel Base
¾” X 12” Drainage Pipe
3” of Geotextile-
wrapped gravel
8-holed Blind Flange
8-holed Vanstone
Flanges
8” Gravel Cover
Bi-planar Geogrid
Separator
MID-SECTION
Waste
Mixture
Liquid Flow
58
fact that samples must be observed immediately after staining due to the potential for
sample drying and loss of fluorescence, but this is not the case with DAPI-stained slides,
which can be stored for later counting. Both staining methods were employed during the
hydrated phase of the experiment to compare cell concentrations. DAPI staining (1mg ml-
1 DAPI) was performed as described previously (Nayak et al. 2009). Live versus
membrane-damaged cells were estimated by staining duplicate leachate samples with the
Live/Dead BacLight Bacterial Viability kit for microscopy as per manufacturer’s
instructions.
IO concentrations were determined (in duplicate) by standard membrane filtration
methods. The volume of leachate filtered was increased from 1 ml in the initial stages of
the experiment to 100 ml (25 ml on each of four separate filteres) as the IO
concentrations decreased towards the end of the experiment. Filters were placed on mFC
agar for fecal coliforms and mEI agar for enterococci (American Public Health
Association (APHA) 1998). After incubation for 18-22 hours (U. S. Environmental
Protection Agency 1997), blue colonies (fecal coliforms) from mFC agar and colonies
with blue halo (presumptive enterococci) from mEI agar were enumerated.
For the dehydrated phase, it was not feasible to perform BacLight staining due to time
restrictions (samples were collected and processed every hour). Total cell concentrations
were estimated by DAPI staining and IO concentrations were determined by membrane
filtration of 100 ml (50 ml each filtered two times) as described above. To determine if
culturable bacteria persisted in the bioreactor after prolonged dehydration, heterotrophic
plate counts were obtained by spread plating 100 μl of leachate on R2A agar (Becton
Dickinson, MD) for aerobic organisms and anaerobic agar for anaerobic organisms
59
(Becton Dickinson, MD). R2A agar plates were incubated at room temperature under
ambient conditions for 3 days whereas anaerobic agar plates were incubated in Gas-Pak
chambers (H2 + CO2) (Becton Dickinson, MD) for 5 days. Total community DNA was
extracted from leachate samples using the Ultraclean Soil DNA Kit (MoBio Laboratories,
Inc.) per manufacturer’s instructions. Bacterial 16S rRNA genes were amplified using the
primer set 1070f and 1392r (Ferris et al. 1996). PCR conditions and the DGGE procedure
details are as described previously (Nayak et al. 2009).
Statistical analysis: Log10 values of the microbial concentrations (direct microscopic cell
counts) and IO concentrations were compared by paired t-tests (GraphPad Instat). The
decrease in IO concentrations was biphasic; i.e. there was a steep decline in
concentrations from day 1 to day 21 followed by a more gradual decline. Hence the initial
decay rate was calculated from day 1 to day 21 and the overall decay rate was calculated
from day 1 to the last day of IO detection.
Decay rates for IOs were calculated using the formula:
Decay rate (k) = (log10N – log10N0)/ ( t + 1)
where N = concentration of organisms on day 21 (for initial decay rate) or last day of
detection (for overall decay rate)
N0 = concentration of organisms on the first day of the experiment
t = day 21 (for initial decay rate) or last day of detection (for overall decay rate)
Paired t tests were performed to compare IO concentrations in duplicate bioreactors by
normalizing each concentration to the starting density. Fecal coliform concentrations
were normalized to 105 CFU ml
-1 whereas Enterococcus concentrations were normalized
to 106 CFU ml
-1.
60
Shannon diversity index (H) was calculated by using the formula:
H = - ∑ pi log pi
calculated as pi = ni/ N where ni is the height of a peak and N is the sum of the peak
heights of all bands in the densitometric curve (Eichner et al. 1999; Ogino et al. 2001;
Haack et al. 2004).Shannon indices were calculated for the community profile of each
given time point and an average of these was reported.
Results
Hydrated phase
In the first, hydrated phase of the experiment, total (live + dead) cell concentrations in
leachate as calculated by BacLight staining increased from 2.8 x 108 cells ml
-1 to 2.3 x
109 cells ml
-1 after the first month and dropped down to 7.0 x 10
8 cells ml
-1 by the end of
the experiment (Figure 4). BacLight concentrations were slightly but not significantly
lower than DAPI concentrations and followed the same trends (data not shown). Live cell
concentrations in bioreactors increased from 2.3 x 108 cells ml
-1 to 1.9 x 10
9 cells ml
-1
after the first month and dropped down to 4.9 x 108 cells ml
-1 at the end of the seventh
month (Figure 4). There was no significant difference between the mean cell
concentrations (BacLight staining) of the two bioreactors for the total counts (P = 0.63)
or live counts (P = 0.58). On an average, 82.3% of total cells (red + green) appeared live
throughout the hydrated phase. No increasing or decreasing trend was observed in the
ratio of live vs. dead cells.
pH values in leachate samples from bioreactors dropped from neutral to 5.8 by day 14 of
the experiment (Figure 5). The pH returned to neutral by day 28 and remained neutral till
the end of the experiment. Fecal coliform concentrations decreased from 6.3 x 104 CFU
61
ml-1
to below detection limits (<1 CFU ml-1
), while Enterococcus concentrations
decreased from 1 x 106 CFU ml
-1 to below detection limits (<1 CFU ml
-1) by day 56 of
the experiment (Figure 6). There was no significant difference in the fecal coliform
concentrations (P = 0.76) or Enterococcus concentrations (P = 0.10) in duplicate
bioreactors. Initial decay rates (day 1 to 21) for fecal coliforms in each bioreactor were –
0.23 and –0.20 and the total decay rates were –0.08 and –0.09. Initial decay rates (day 1
to 21) for enterococci in each bioreactor were –0.23 and –0.21 and the total decay rates
were –0.11 and –0.10. Paired t-tests were performed on normalized fecal coliform and
Enterococcus concentrations and no significant differences were found in the
concentrations of the two IOs (P = 0.94).
0
0.5
1
1.5
2
2.5
3
1
14
28
42
56
70
84
98
11
2
12
6
14
0
15
4
16
8
Time (days)
B1-Total
B1-Live
B2-Total
B2-Live
Figure 4. Total cell concentrations (x 109/ml) in leachate during the hydrated phase as
measured by BacLight Live/Dead staining. Results of duplicate bioreactors (B1 and B2)
are presented.
Cel
l co
nce
ntr
atio
n x
10
9/
ml
62
5.3
5.7
6.1
6.5
6.9
7.3
1 14 28 42 56 70 84 98 112 126 140 154 168
Time (days)
pH
B1 B2
Figure 5. pH values in duplicate bioreactors (B1 and B2) during the hydrated phase.
0
1
2
3
4
5
6
7
1 7 14 21 28 35 42 49 56
Time (days)
FC
ENC
Figure 6. Mean concentrations of fecal coliforms (FC) and enterococci (ENC) in leachate
sampled weekly from duplicate bioreactors in the hydrated phase of the experiment. Data
shown only until culturable cells were no longer detectable in the hydrated phase.
log
CF
U/
ml
63
Dehydrated phase
In the second, dehydrated phase of the experiment, total cell concentrations in the
leachate increased from 1.5 x 107 cells ml
-1 at time zero to 2.4 x 10
8 cells ml
-1 at 5 hours.
Total cell concentrations subsequently stabilized at approximately 1.1 x 108 cells ml
-1
(Figure 7). Heterotrophic plate counts for culturable aerobic organisms increased from
5.9 x 105 CFU ml
-1 at time zero to 2 x 10
6 CFU ml
-1 at 24 hours (Figure 8). A similar
increase was noted for culturable anaerobic organisms, which increased from 6 x 103
CFU ml-1
at time zero to 4 x 104 CFU ml
-1 at 24 hours (Figure 8). Since anaerobic
chambers were not used while plating the leachate, the concentrations do not include any
extremely oxygen-sensitive anaerobes that may have been present. Fecal coliforms were
detected in the leachate five hours after the bioreactor was rehydrated (8 CFU 100 ml-1
),
but not before that time. Twenty-four hours after rehydration, the fecal coliform
concentrations increased to 20 CFU 100 ml-1
. Enterococci were detected in the leachate
three hours after rehydration of the bioreactor (20 CFU 100 ml-1
). Twenty-four hours
after rehydration, Enterococcus concentrations increased to 30 CFU 100 ml-1
.
DGGE was performed to observe the bacterial community structure in leachate after this
prolonged dry period and to determine changes in the community during the next twenty-
four hours. A shift was observed in the bacterial community structure (assessed by
DGGE) between time zero and the first hour following rehydration, after which the
populations were approximately 90% similar (Figure 9). There was a considerable change
in the population structure again at the last measurement (24 hour). The average Shannon
diversity index (H) for the twenty-four period was calculated at 1.04.
64
0
5
10
15
20
25
30
0 1 2 3 4 5 6 7 24
Time (hours)
Figure 7. Total cell concentrations (x 107/ml) in leachate from time zero (T0) to twenty
four (T24) after rehydration of the bioreactor. The line between T7 and T24 denotes a time
break.
0
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7 24
Time (hours)
Aerobic Anaerobic
Figure 8. Heterotrophic plate counts on R2A agar (aerobic organisms) and anaerobic
agar (anaerobic organisms) after bioreactor rehydration. The line between T7 and T24
denotes a time break.
Cel
l co
nce
ntr
atio
n x
10
7/
ml
log
CF
U/
ml
65
Figure 9. Dendrogram showing the percent similarity of bacterial DGGE patterns from
time zero (T0) to twenty four (T24) after rehydration of the bioreactor. The circle denotes
a previously undetectable, relatively low GC content band in the 24th
hour sample. Arrow
indicates increasing direction of denaturant and acrylamide gradient from lower to higher
concentration.
Discussion
The effect of moisture on the fate of IOs was studied by determining culturable fecal
coliform and Enterococcus spp. concentrations in the leachate from laboratory-scale
bioreactors subjected to moisture-rich and moisture-deprived conditions. Fecal coliforms
and enterococci remained detectable for the first 50 days of the hydrated phase, after
which they were undetectable till the end of the hydrated phase. The transition of fecal
coliforms and enterococci to a viable but non-culturable (VBNC) state could explain the
drop in the concentrations below detection level after the first 50 days. During the initial
stages of waste degradation cellulose degrading bacteria such as Clostridium spp. and
Eubacterium spp. hydrolyze cellulose and hemicellulose into monosaccharides that are
further fermented to produce alcohols and carboxylic acids (Van Dyke and McCarthy
2002; Burrell et al. 2004). Accumulation of these metabolic products results in a drop in
pH, which could be the stressors driving IOs into the VBNC state (Oliver 1993; Higgins
100 90 80 70
T2
T3
T4
T5
T6
T7
T1
T24
T0
66
et al. 2007). The decline in the pH of the leachate from neutral to acidic by day 14
corresponds with the initial linear decline in IO concentrations.
Fecal coliform decay rates were not significantly different from Enterococcus decay rates
and a similar decreasing trend in IO concentrations was observed over a period of time.
The decay rates of both IOs demonstrated a biphasic trend wherein a linear decay in
concentrations was observed initially and subsequently leveled off until culturable cells
were no longer detectable. The IO decay rates observed in our study are comparable to
those observed by other studies, although they were measured in different environments
(Anderson et al. 2005; Badgley 2009). Enterococcus populations in freshwater
mesocosms demonstrated an initial decay rate of –0.63 and an overall decay rate of –0.02
over a period of two weeks (Badgley 2009). This is in agreement with the biphasic trend
of the Enterococcus decay rates observed in our study. In another study, Anderson et al
documented decay rates of –0.27 and –0.31 for fecal coliforms and enterococci
respectively in freshwater mesocosms inoculated with wastewater (Anderson et al. 2005).
The IOs persisted for two weeks and four weeks in the water and sediment columns of
the mesocosms, respectively. Both these studies used freshwater as the matrix, which is
low in nutrients, and hence the IOs did not persist for long. In comparison, waste is high
in nutrients and hence IOs were detected in the leachate samples for about seven weeks.
The ratio of live to dead cells remained constant throughout the hydrated phase of the
study. We hypothesize that the consistent survival of cells over time in the mesocosms is
due in part to the high nutrient concentration in the leachate, where one microbial
population is constantly replaced by another (succession) resulting in no net increase or
decrease in the concentration of live or dead cells.
67
Few studies have investigated the survival of indicator organisms during the process of
waste decomposition. Cameron and McDonald reported the early die-off of total and
fecal coliforms when raw wastewater was mixed in a 9:1 and 1:1 ratio with leachate
produced in bioreactors (Cameron and Mcdonald 1977). Since IO survival was
determined by mixing wastewater in leachate and not within the bioreactors as part of the
waste degradation process, a direct comparison cannot be made with the current study.
Deportes et al documented an initial increase in fecal coliform concentrations in raw
waste followed by a steady decline (two log decrease per week) within twenty days in an
MSW composting plant (Deportes et al. 1998). The decrease in pathogen concentrations
(Salmonella, Shigella and Ascaris eggs) correlated with the decrease in fecal coliform
concentrations but not with the concentrations of total and fecal streptococci. They
concluded that the combined monitoring of indicators and pathogens provides a better
measure of the sanitization of waste. In comparison, we did not see an initial increase in
fecal coliform concentrations but the decrease in concentrations was comparable with the
initial decline (day 1 to day 21) observed in our study.
The detection of fecal coliforms and enterococci after one year of dehydration was
remarkable and completely unexpected. The recovery of these cells in a culturable state
after a prolonged moisture-deprived phase indicates that they can survive, possibly within
localized “pockets” of moisture within the waste (Lleo et al. 1998). Zaleski et al reported
a decrease in fecal coliform and E. coli concentrations in biosolids even when abundant
moisture conditions were maintained (Zaleski 2005). They documented culturable fecal
coliforms in biosolids that were moistened (rainfall) after a desiccation period and
attributed it to regrowth due to fecal contamination from birds. The decrease in fecal
68
coliform concentrations during moisture-abundant conditions is in agreement with that
observed in our study. In our study, the detection of fecal coliforms after bioreactor
rehydration in our study cannot be attributed to any external influence since the
bioreactor was sealed and maintained in the laboratory.
Even after a prolonged dry period, the total cell concentrations in the leachate were lower
by just one log as compared to total cell concentrations measured during the hydrated
phase. The recovery of culturable aerobic and anaerobic heterotrophs immediately after
rehydration is also noteworthy. Changes occurring in the bacterial population structure in
leachate were followed every hour for the first eight hours after rehydration using DGGE
to observe the dominant and transient communities and to determine the extent of
diversity in the bioreactor community after an extended dry period. Bacterial populations
in the leachate in the twenty-four hour time period after rehydration were highly similar.
The emergence of a previously undetectable, lower GC content band in the 24th
hour
sample (circled in Figure 9) suggests a reviving population in a succession process that
will probably continue over time (Nayak et al. 2009).
In our previous study, we used DGGE to track the bimonthly changes in microbial
populations in leachate sampled from frequently hydrated bioreactors (Nayak et al.
2009). Bacterial populations exhibited higher diversity (1.37) than those in the current
study (1.04) and considerable shifts were observed in the dominant species in the two-
week period between samples. Here, dehydration of the bioreactor resulted in a reduction
of microbial concentrations, which may be coupled with a reduction in population
diversity. However, since we did not measure population diversity in the hydrated phase,
the change in diversity between the two phases cannot be compared.
69
Other population studies have employed the Shannon diversity index to calculate the
diversity of bacterial communities. For example, sulfate-reducing bacteria (SRBs) in oxic
sediment layers of an oligotrophic lake exhibited lower diversity (H = 1.83) as compared
to anoxic layers (H = 2.59) (Sass et al. 1998). In another study, high diversity of
microbial communities was observed in mercury-contaminated soil (H = 3.48) and was
comparable to the uncontaminated control (H = 3.83) (Muller et al. 2002). In comparison,
we observed lower bacterial community diversity in our previous and current bioreactor
studies. Landfill waste is high in nutrients but also carries concentrated amounts of toxic
metabolic products, heavy metals and pharmaceuticals, which could result in lower
population diversity.
This study demonstrated the survival of IOs in a landfill bioreactor even after a prolonged
dry period. The implications of these results with respect to the fate of pathogens in the
waste should be further explored, as accurate assessment of the threat posed by the
release of pathogens into groundwater is desirable from the public health perspective.
Further research should be conducted to investigate the survival of pathogens during
waste degradation and its correlation to the survival of IOs to determine if current
monitoring practices are appropriate to protect public health.
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Anderson, K.L., Whitlock, J.E. and Harwood, V.J. (2005) Persistence and differential
survival of fecal indicator bacteria in subtropical waters and sediments. Appl Environ
Microbiol 71, 3041-3048.
Badgley, B.D., Thomas, F. I. M., Harwood, V. J. (2009) The effects of submerged
aquatic vegetation on the persistence of environmental populations of Enterococcus spp.
Unpublished.
Boothe, D.D.H., Smith, M.C., Gattie, D.K. and Das, K.C. (2001) Characterization of
microbial populations in landfill leachate and bulk samples during aerobic bioreduction.
Adv Environ Res 5, 285-294.
Burrell, P.C., O'Sullivan, C., Song, H., Clarke, W.P. and Blackall, L.L. (2004)
Identification, detection, and spatial resolution of Clostridium populations responsible for
cellulose degradation in a methanogenic landfill leachate bioreactor. Appl Environ
Microbiol 70, 2414-2419.
Cameron, R.D. and Mcdonald, E.C. (1977) Coliforms and Municipal Landfill Leachate.
Journal Water Pollution Control Federation 49, 2504-2506.
Deportes, I., Benoit-Guyod, J.L., Zmirou, D. and Bouvier, M.C. (1998) Microbial
disinfection capacity of municipal solid waste (MSW) composting. J Appl Microbiol 85,
238-246.
Eichner, C.A., Erb, R.W., Timmis, K.N. and Wagner-Dobler, I. (1999) Thermal gradient
gel electrophoresis analysis of bioprotection from pollutant shocks in the activated sludge
microbial community. Appl Environ Microbiol 65, 102-109.
71
El-Fadel, M. (1999) Leachate recirculation effects on settlement and biodegradation rates
in MSW landfills. Environ Technol 20, 121-133.
Ferris, M.J., Muyzer, G. and Ward, D.M. (1996) Denaturing gradient gel electrophoresis
profiles of 16S rRNA-defined populations inhabiting a hot spring microbial mat
community. Appl Environ Microbiol 62, 340-346.
Gerba, C.P., Huber, M.S., Naranjo, J., Rose, J.B. and Bradford, S. (1995) Occurrence of
Enteric Pathogens in Composted Domestic Solid-Waste Containing Disposable Diapers.
Waste Manag Res 13, 315-324.
Haack, S.K., Fogarty, L.R., West, T.G., Alm, E.W., McGuire, J.T., Long, D.T.,
Hyndman, D.W. and Forney, L.J. (2004) Spatial and temporal changes in microbial
community structure associated with recharge-influenced chemical gradients in a
contaminated aquifer. Environ Microbiol 6, 438-448.
Higgins, M.J., Chen, Y.C., Murthy, S.N., Hendrickson, D., Farrel, J. and Schafer, P.
(2007) Reactivation and growth of non-culturable indicator bacteria in anaerobically
digested biosolids after centrifuge dewatering. Water Res 41, 665-673.
Kjeldsen, P., Barlaz, M.A., Rooker, A.P., Baun, A., Ledin, A. and Christensen, T.H.
(2002) Present and long-term composition of MSW landfill leachate: A review. Crit Rev
Environ Sci Technol 32, 297-336.
Lleo, M.M., Tafi, M.C. and Canepari, P. (1998) Nonculturable Enterococcus faecalis
cells are metabolically active and capable of resuming active growth. Syst Appl Microbiol
21, 333-339.
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Mose, J.R. and Reinthaler, F. (1985) Microbial-Contamination of Hospital Waste and
Household Refuse. Zentralblatt Fur Bakteriologie Mikrobiologie Und Hygiene Serie B-
Umwelthygiene Krankenhaushygiene Arbeitshygiene Praventive Medizin 181, 98-110.
Muller, A.K., Westergaard, K., Christensen, S. and Sorensen, S.J. (2002) The diversity
and function of soil microbial communities exposed to different disturbances. Microb
Ecol 44, 49-58.
Nayak, B.S., Levine, A.D., Cardoso, A. and Harwood, V.J. (2009) Microbial population
dynamics in laboratory-scale solid waste bioreactors in the presence or absence of
biosolids. J Appl Microbiol 107, 1330-1339.
Ogino, A., Koshikawa, H., Nakahara, T. and Uchiyama, H. (2001) Succession of
microbial communities during a biostimulation process as evaluated by DGGE and clone
library analyses. J Appl Microbiol 91, 625-635.
Oliver, J.D. (1993) Formation of viable but nonculturable cells. In Starvation in Bacteria
(ed. S. Kjelleberg), Plenum Press, New York, pp. 239–272.
Peterson, M.L. (1974) Soiled Disposable Diapers - Potential Source of Viruses. American
Journal of Public Health 64, 912-914.
Reinhart, D.R., Chopra, M. B., Sreedharan, A., Koodhathinkal, B., Townsend, T.G.
(2003) Design and operational issues related to the co-disposal of sludges and biosolids
in class I landfills. Report #0132010-03 Florida Center for Solid and Hazardous Waste
Management.
Rose, J.B., Farrah, S. R., Harwood, V. J., Levine, A. D., Lukasik, J., Menendez, P. and
Scott, T. M. (2004) Reduction of pathogens, indicator bacteria and alternative indicators
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by wastewater treatment and reclamation processes. Final report. Water Environment
Research Foundation (WERF) 00-PUM-2T, IWA Publishing, London SW1H 0QS,
United Kingdom.
Sass, H., Wieringa, E., Cypionka, H., Babenzien, H.D. and Overmann, J. (1998) High
genetic and physiological diversity of sulfate-reducing bacteria isolated from an
oligotrophic lake sediment. Arch Microbiol 170, 243-251.
Tatsi, A.A. and Zouboulis, A.I. (2002) A field investigation of the quantity and quality of
leachate from a municipal solid waste landfill in a Mediterranean climate (Thessaloniki,
Greece). Adv Environ Res 6, 207-219.
Trost, M. and Filip, Z. (1985) Microbiological Investigations on Refuse from Medical
Consulting Rooms and Municipal Refuse. Zentralblatt Fur Bakteriologie Mikrobiologie
Und Hygiene Serie B-Umwelthygiene Krankenhaushygiene Arbeitshygiene Praventive
Medizin 181, 159-172.
U. S. Environmental Protection Agency (1997) Method 1600: Membrane filter test
methods for enterococci in water. Office of Water, Washington D.C. EPA-821/R-97/004.
Van Dyke, M.I. and McCarthy, A.J. (2002) Molecular biological detection and
characterization of Clostridium populations in municipal landfill sites. Appl Environ
Microbiol 68, 2049-2053.
Wreford, K.A., Atwater, J.W. and Lavkulich, L.M. (2000) The effects of moisture inputs
on landfill gas production and composition and leachate characteristics at the Vancouver
Landfill Site at Burns Bog. Waste Manag Res 18, 386-392.
74
Zaleski, K.J., Josephson, K. L., Gerba, C. P., Pepper, I. L. (2005) Survival, growth, and
regrowth of enteric indicator and pathogenic bacteria in biosolids, compost, soil,and land
applied biosolids. J Residuals Sci Tech 2, 49-63.
RESEARCH SIGNIFICANCE
Landfills are massive, subterranean areas where waste is deposited in compact cells over
a period of time. Each lateral and vertical section of the landfill is at a different stage of
the waste degradation process. The heterogeneous nature of the waste and the variability
in the “age” of different sections of landfills makes it difficult to design sampling
strategies for investigating the microbial communities in the waste. Leachates collected
from landfills carry a representative sample of the numerous microorganisms involved in
degradation of that waste. The physical, chemical and microbial characteristics of
leachate have been studied in detail by field experiments as well as the construction of
laboratory-scale bioreactors or lysimeters.
Microbial community structure
Bioreactors have been used in several studies to mimic waste degradation conditions in
landfills (Pohland and Kim 2000; Cooke 2001; Youcai et al. 2002; Ledakowicz and
Kaczorek 2004). Bioreactors were used in this study to simulate MSW-only and co-
disposal landfill conditions. Changes in the microbial community structures, influenced
by the presence or absence of biosolids, were investigated by sampling leachate over a
period of time. To date, this is the only study that has followed the population changes in
both prokaryotic domains (Archaea and Bacteria) for such an extended period of time
75
using denaturing gradient gel electrophoresis (DGGE). Temporal shifts were observed in
the archaeal and bacterial populations in bioreactors regardless of the waste content,
suggesting a succession process from an immature to a mature microbial community.
This study advances the basic understanding of changes in the microbial community
structure during solid waste decomposition. Future research efforts could focus on
identifying the dominant microbial species during specific time intervals with the
progression of waste degradation. A better understanding of the microbial community
structure in solid waste disposal facilities will allow more effective degradation of waste
and management of the infrastructure.
Methanogen populations
Methanogenic Archaea are responsible for the generation of methane in landfills.
Methanogens are strictly anaerobic and sensitive to changes in pH, temperature, moisture
levels etc (Gurijala and Suflita 1993; Mormile et al. 1996; Shen et al. 2001; Mori et al.
2003). Complete anaerobic decomposition of waste is achieved when most of the organic
carbon is transformed to methane and carbon dioxide. In this study, methanogen
sequences were retrieved from the leachate during the early and late phases of bioreactor
operation. Methanogen clones from the early phase were closely related to the members
of Methanosarcinales, Methanobacteriales and Methanomicrobiales while methanogen
clone sequences from the late phase were related to members of the Order
Methanomicrobiales. Gaining an understanding of the environmental factors that
influence the methane production by methanogens will require extensive research, but
this effort is justified by the potential for using this knowledge to improving methane
management practices.
76
Survival of indicator organisms during waste degradation
Compromised leachate collection systems, excessive rainfall or the release of improperly
treated leachate into surface waters are some of the ways in which pathogens from
landfill leachate can come into contact with the community in general. It is not feasible to
analyze treated leachate or groundwater suspected of leachate contamination for all of the
various pathogens that could be present. Hence, many studies evaluate the threat posed
by leachate-borne pathogens by enumerating the indicator organisms in treated leachate
(Wreford et al. 2000; Tatsi and Zouboulis 2002; Manios and Stentiford 2004).
I observed a decline in the culturable concentrations of indicator organisms in bioreactor
leachate during moisture-rich conditions, which is supported by other such studies
(Cameron and Mcdonald 1977; Deportes et al. 1998). Interestingly, I could detect fecal
coliforms and enterococci in the leachate even after a prolonged moisture-deprived stage.
The persistence of indicator organisms in the leachate signifies the potential for pathogen
survival and subsequent dissemination into the community in the event of a failure in
leachate management and disposal practices.
Waste degradation studies
The majority of the landfill-related studies are conducted on the European continent,
where land availability is scarce due to the small land areas of countries, or in Asia,
where increasing human population leads to production of vast amounts of waste.
Although the land to human ratio in the U.S. is large, it is imperative to establish better
landfilling practices in order to control problems such as clogging of leachate collection
77
systems due to mineral deposits and prevent unnecessary waste of land space. This study
has added to the existing knowledge of microbial populations in solid waste by tracking
the changes in archaeal and bacterial community structures, which exhibited high
diversity and distinct temporal trends. Methanogens actively involved in the early and
late stages of waste degradation were sequenced and identified, extending our knowledge
of these important fuel-producers and agents of greenhouse gas production. The effect of
moisture on the fate of indicator organisms was also studied. This research was funded by
the Hinkley Center for Solid and Hazardous Waste Management, FL and the information
obtained during the study was submitted as a report that can be used to improve landfill
management practices.
References
Cameron, R.D. and Mcdonald, E.C. (1977) Coliforms and Municipal Landfill Leachate.
Journal Water Pollution Control Federation 49, 2504-2506.
Cooke, A.J., Rowe, R.K., Rittman, B.E., VanGulck, J., Millward, S. (2001) Biofilm
growth and mineral precipitation in synthetic leachate columns. J Geotech Geoenviron
Eng, 849-856.
Deportes, I., Benoit-Guyod, J.L., Zmirou, D. and Bouvier, M.C. (1998) Microbial
disinfection capacity of municipal solid waste (MSW) composting. J Appl Microbiol 85,
238-246.
Gurijala, K.R. and Suflita, J.M. (1993) Environmental-factors influencing
methanogenesis from refuse in landfill samples. Environ Sci Technol 27, 1176-1181.
78
Ledakowicz, S. and Kaczorek, K. (2004) The effect of advanced oxidation processes on
leachate biodegradation in recycling lysimeters. Waste Manag Res 22, 149-157.
Manios, T. and Stentiford, E.I. (2004) Sanitary aspect of using partially treated landfill
leachate as a water source in green waste composting. Waste Manag 24, 107-110.
Mori, K., Sparling, R., Hatsu, M. and Takamizawa, K. (2003) Quantification and
diversity of the archaeal community in a landfill site. Can J Microbiol 49, 28-36.
Mormile, M.R., Gurijala, K.R., Robinson, J.A., McInerney, M.J. and Suflita, J.M. (1996)
The importance of hydrogen in landfill fermentations. Appl Environ Microbiol 62, 1583-
1588.
Pohland, F.G. and Kim, J.C. (2000) Microbially mediated attenuation potential of landfill
bioreactor systems. Water Sci Technol 41, 247-254.
Shen, D.S., He, R., Ren, G.P., Traore, I. and Feng, X.S. (2001) Effect of leachate recycle
and inoculation on microbial characteristics of municipal refuse in landfill bioreactors. J
Environ Sci (China) 13, 508-513.
Tatsi, A.A. and Zouboulis, A.I. (2002) A field investigation of the quantity and quality of
leachate from a municipal solid waste landfill in a Mediterranean climate (Thessaloniki,
Greece). Adv Environ Res 6, 207-219.
Wreford, K.A., Atwater, J.W. and Lavkulich, L.M. (2000) The effects of moisture inputs
on landfill gas production and composition and leachate characteristics at the Vancouver
Landfill Site at Burns Bog. Waste Manag Res 18, 386-392.
Youcai, Z., Luochun, W., Renhua, H., Dimin, X. and Guowei, G. (2002) A comparison
of refuse attenuation in laboratory and field scale lysimeters. Waste Manag 22, 29-35.
79
EVALUATION OF GENETIC RELATIONSHIPS AND PREVALENCE OF
VANCOMYCIN RESISTANCE IN ENVIRONMENTAL ENTEROCOCCI
BACKGROUND
Enterococci are ubiquitous in nature and are particularly challenging to study due to the
difficulty in differentiating among certain species and strains (Devriese et al. 1993;
Donabedian et al. 1995; Patel et al. 1998). Members of the genus Enterococcus are
normal inhabitants of the gastrointestinal (GI) tract of mammals and birds. Enterococcus
faecalis and Enterococcus faecium are the predominant species of enterococci colonizing
the human GI tract. As many as 108 colony forming units (CFU) of enterococci can be
found per gram of human feces (Noble 1978). Enterococci are used as regulatory tools
(U.S. Environmental Protection Agency 1986) and in microbial source tracking (MST)
methods (Harwood et al. 2004; Scott et al. 2005) as indicators of water quality in fresh
and saline waters. Enterococci have been known to persist in environmental waters and
have been isolated from food and clinical samples (Eaton and Gasson 2001; Cupakova
and Lukasova 2003; Giraffa 2003; Harwood et al. 2004). Some enterococci have the
potential to cause infections, especially in immunocompromised humans (Maki and
Agger 1988; Svec and Sedlacek 1999). Possession of virulence factors, pathogenicity
islands and the capacity to acquire antibiotic resistance genes make Enterococcus species
a formidable group from the public health perspective.
80
Rapid identification of Enterococcus species in clinical isolates is essential for treatment
and prevention of the nosocomial spread of the pathogen. Phenotypic methods are time
consuming and cannot discriminate between some Enterococcus species (Devriese et al.
1993). Therefore, DNA-based analyses such as genotyping, genetic sequencing and
targeting particular genes with specific probes and primers are employed (Malathum et
al. 1998; Harwood et al. 2004; Jackson et al. 2004). Methods used to genotype organisms
include, but are not limited to, restriction digestion of the chromosomal DNA or targeting
repetitive DNA sequences to generate a genomic “fingerprint” of the organism.
Genotyping facilitates identification of variant strains and epidemiological tracking of
virulent enterococci to determine their geographic distribution. From an environmental
standpoint, enterococcal diversity and prevalence in different habitats can be elucidated
by comparison of genotypes. This study was designed with both the environmental and
clinical perspective of determining the strain distribution and vancomycin resistance of
enterococci in local water bodies and wastewater by genotyping, phylogenetic
identification and antibiotic susceptibility testing.
Identification of enterococci
Enterococci are facultatively anaerobic, gram-positive, catalase-negative cocci. In
general, members of the genus are capable of growth at temperatures between 10 to 45°C,
with an optimum growth temperature of 35°C for most species. Some species of the
genus Enterocuccus are motile. Growth in broth containing 6.5% NaCl and hydrolysis of
esculin in the presence of bile salts are two important phenotypic characteristics used to
identify enterococci at the genus level (Facklam et al. 1974). Other tests such as
production of leucine aminopeptidase (LAP) and hydrolysis of pyrrolidonyl-β-
81
naphthylamide (PYR) are used to differentiate enterococci from other gram-positive,
catalase-negative cocci such as those belonging to the genera Leuconostoc/ Weissella and
Pediococcus (Facklam and Elliott 1995). They can be differentiated at the species level
by phenotypic methods using commercially available kits such as API 20 Strep
biochemical test kits (Sader et al. 1995; Manero and Blanch 1999), MicroScan gram-
positive identification panel (Iwen et al. 1999) and Biolog microbial ID/ characterization
systems (Moore et al. 2006; Graves et al. 2007). Yet, phenotypic identification and
classification of enterococci is difficult due to the phenotypic similarity of certain species
such as E. gallinarum and E. casseliflavus, E. cecorum and E. columbae, and E. hirae and
E. durans (Devriese et al. 1993).
Molecular characterization to the species level has been achieved using DNA-DNA
reassociation, 16S rRNA gene sequencing and whole-cell protein (WCP) analysis
(Farrow et al. 1983; Williams et al. 1991; Merquior et al. 1994). Presently, twenty-three
distinct Enterococcus species have been identified (Jackson et al. 2004). Molecular
typing methods such as fluorescent internally transcribed spacer region PCR (ITS-PCR)
(Tyrrell et al. 1997) , AFLP typing (Ulrich and Muller 1998) and repetitive extragenic
palindrome-PCR (REP-PCR) (Svec et al. 2005) have also been used in conjunction with
phenotypic methods for the identification and typing of Enterococcus isolates (Pangallo
et al. 2008). Several other molecular techniques used for typing and speciation of
enterococci include tRNA intergenic spacer PCR (t-DNA PCR) (Baele et al. 2000),
broad-range amplification of the 16S rDNA gene (Monstein et al. 1998), randomly
amplified polymorphic DNA analysis (RAPD) (Monstein et al. 1998; Quednau et al.
82
1998), pulsed field gel electrophoresis (PFGE) (Descheemaeker et al. 1997) and BOX-
PCR (Brownell et al. 2007; Hassan et al. 2007).
Genetic “fingerprinting” techniques used to type enterococci
Typing of bacterial isolates generates a unique “molecular fingerprint” for each isolate
that can be used for strain differentiation. Genotyping of enterococci facilitates
discrimination of closely related species and determination of dominant strains in
ecological studies (Brownell et al. 2007; Hassan et al. 2007). Supplementing genotype
studies with phylogenetic analysis will provide additional information about the
relationships among species and strains of enterococci. However, no study has
demonstrated that the discrete clusters formed by enterococcal BOX-PCR patterns are
phylogenetically related species/ strains. This study aims to shed light on the
relationships between enterococcal species and strains using both genotypic and
phylogenetic data.
Repetitive DNA sequences have been identified and their presence demonstrated in the
genomes of several prokaryotes as well as eukaryotes. DNA repeat sequences have been
useful in typing certain eukaryotes including protozoan parasites such as Trichomonas
vaginalis, Giardia lamblia, Trypanosoma spp. and Leishmania donovani and non-
pathogenic organisms such as Paramecium tetraurelia and Saccharomyces cerevisiae
(Riley et al. 1991). Palindromic units (PU) or repetitive extragenic palindromes (REP)
(Higgins et al. 1982; Gilson et al. 1984; Dimri et al. 1992) and the enterobacterial
repetitive intergenic consensus sequences (ERIC) (Sharples and Lloyd 1990; Hulton et al.
1991) are two of the most well studied DNA repeat sequences in bacteria. These
repetitive sequences have been identified in Escherichia coli (Gilson et al. 1984; Hulton
83
et al. 1991), Salmonella enterica Typhimurium (Hulton et al. 1991), Mycobacterium
tuberculosis (Eisenach et al. 1990; Ross et al. 1992), and Neisseria gonorrhoeae (Correia
et al. 1986) among others.
The presence of repeat DNA elements in prokaryotes is especially intriguing considering
the fact that prokaryotic genomes are small and it is generally thought that they cannot
afford any waste of coding space. These repeat sequences are postulated to preserve
themselves as “selfish DNA” and their proposed functions include regulation of gene
expression (Newbury et al. 1987; Stern et al. 1988), a structural role in chromosomal
rearrangements (Stern et al. 1984; Gilson et al. 1986; Gilson et al. 1987) and a role in
bacterial virulence (Haas and Meyer 1986). PCR-based DNA typing of organisms using
primers targeting repetitive DNA elements (known as rep-PCR) has proved valuable in
differentiating them at the sub-species level (Versalovic et al. 1991). The highly
reproducible and discriminatory nature of this typing technique has resulted in its
utilization in epidemiological, agricultural and industrial applications (de Bruijn 1992).
The first group of repetitive sequences identified in gram positive bacteria was designated
BOX elements (Martin et al. 1992), which were initially discovered in Streptococcus
pneumoniae. The sequence is highly conserved within its chromosomal intergenic
regions. There are approximately 25 copies of BOX elements in the genome of S.
pneumoniae. Based on their proximity to the competence-specific and virulence-related
genes, it is proposed that these elements play a functional role in regulation of genetic
transformation and virulence. BOX elements are comprised of boxA, boxB and boxC
subunits, which are 59, 45 and 50 base pairs long, respectively and have very low
sequence similarity to one another. The boxB subunit can exist alone or as a part of the
84
BOX group of elements. More than one repeat of the boxB subunit has also been reported
in some BOX elements (Martin et al. 1992). The boxA and boxC subunits have been
observed to form stable stem-loop structures. Among the three subunits, the boxA subunit
appears to be the most conserved among different bacterial species (Koeuth et al. 1995).
In bacteria other than S. pneumoniae, boxA-like elements were found to exist
independently of boxB-like and boxC-like subunits. The complete set of BOX elements
(boxA, boxB and boxC) have been found only in the genomes of S. pneumoniae and S.
agalactiae (Koeuth et al. 1995). About 25 copies of BOX elements have been detected in
these genomes.
Several studies have generated BOX-PCR patterns from other gram positive bacteria
such as Enterococcus spp. (Malathum et al. 1998; Brownell et al. 2007; Pangallo et al.
2008) and Lactobacillus spp. (Gevers et al. 2001). Recent studies have used BOX-PCR to
produce genotypic dendrograms of enterococcal isolates since fingerprints generated by
BOX-PCR are reproducible and can differentiate enterococci to the species (Pangallo et
al. 2008) and strain (Malathum et al. 1998; Proudy et al. 2008) levels. BOX-PCR
fingerprinting has been used to type enterococci in ecological studies (Brownell et al.
2007) and MST studies (Brownell et al. 2007; Hassan et al. 2007). These studies include
dendrograms that display population similarities based solely on BOX-PCR genotyping.
However, no study has demonstrated that BOX-PCR patterns of various strains of a
particular enterococcal species are more closely related than strains from different
species.
To increase the precision of the genotyping technique, Johnson et al developed a method
using fluorescently labeled primers and internal fluorophore-tagged markers that enable
85
precise alignment of bands within each gel and better normalization of data from
different gels (Johnson et al. 2004). This novel method, called the horizontal,
fluorophore-enhanced, repetitive extragenic palindromic-PCR (HFERP) technique, has
been used to fingerprint E. coli strains from different sources such as animal feces, soils
and environmental waters (Byappanahalli et al. 2006; Hamilton et al. 2006; Ishii et al.
2006; Ksoll et al. 2007). To date, this technique has not been applied to typing of
enterococcal isolates. Due to the increased precision of the method compared to
conventional BOX-PCR typing, it was chosen in the current study to type enterococci.
Virulence in enterococci
Enterococci are commensals that inhabit the gastrointestinal tracts of humans and other
mammals. It is postulated that commensal enterococci can acquire additional genes on
mobile genetic elements, which enable them to cause disease (Gilmore and Ferretti
2003). The virulence traits of E. faecalis strains are more extensively studied compared to
the virulence traits of other pathogenic Enterococcus species. E. faecalis strains capable
of causing disease frequently possess adhesins that mediate attachment to host cell
surfaces (Lowe et al. 1995; Archimbaud et al. 2002). Aggregation substance, a surface
protein, plays a role in conjugative transfer of a sex pheromone plasmid by binding donor
and recipient bacterial cells, but its similarity to eukaryotic fibronectin enables it to bind
to integrins on host epithelial cells (Galli 1990, Olmsted 1994). Furthermore, it prevents
respiratory burst after phagocytosis, making the organism resistant to the host immune
response (Ratika 1999, Sussmuth 2000). It is also known to activate the quorum sensing
mechanism that regulates cytolysin production by Enterococcus faecalis, thereby causing
further tissue damage (Chow 1993).
86
Another cell surface protein expressed by E. faecalis and E. faecium is the enterococcal
surface protein (esp). The esp gene, which was initially detected in E. faecalis, encodes a
protein that is believed to facilitate colonization of the urinary tract and biofilm formation
(Shankar et al. 1999; Shankar et al. 2001; Toledo-Arana et al. 2001). A variant of the E.
faecalis esp gene was later described in clinical isolates of E. faecium (Willems et al.
2001; Woodford et al. 2001; Leavis et al. 2004). The variant esp gene of E. faecium was
proposed as a marker of human fecal pollution in environmental waters (Scott et al.
2005). Several microbial source tracking (MST) studies have documented the presence of
the esp gene in E. faecium isolated from environmental sources and municipal
wastewater (McDonald et al. 2006; McQuaig et al. 2006; Whitman et al. 2007; Ahmed et
al. 2008a; Ahmed et al. 2008b).
Some E. faecalis strains secrete a toxin called cytolysin that is both hemolytic and
bactericidal (Gilmore 1991). The dual action of the cytolysin provides a competitive edge
for proliferation of the organism in the intestinal epithelium. The enterococcal
polysaccharide antigen (Epa) and other capsular carbohydrates may also contribute to
evasion of the host immune response (Arduino et al. 1994; Teng et al. 2002). Most E.
faecalis strains and some E. faecium strains produce extracellular superoxide, which
results in destruction of epithelial cells and deeper tissue invasion (Huycke et al. 1996).
Antibiotic resistance in enterococci
Enterococci can be resistant to a wide range of antimicrobial agents such as β-lactams
(penicillins, carbenepems and cephalosporins), aminoglycosides (gentamicin, tobramycin
and kanamycin) and glycopeptides (vancomycin and teicoplanin). Both vancomycin and
teicoplanin are glycopeptide antibiotics used to treat infections caused by gram-positive
87
organisms. Glycopeptides inhibit cell wall biosynthesis in gram-positive organisms by
complexing with the D-alanine-D-alanine (D-Ala-D-Ala) terminus of the pentapeptide,
thereby blocking the subsequent transglycosylation and transpeptidation steps.
Vancomycin resistance is achieved by modifying the pentapeptide ending in D-Ala-D-
Ala to one ending in a different amino acid such as D-Ala-D-lactate or D-Ala-D-serine
(Bugg et al. 1991; Billot-Klein et al. 1994).
Vancomycin resistance in enterococci is the result of either intrinsic or acquired genes
(Rice et al. 1995). The different known genotypes of vancomycin resistance include vanA
(main reservoir: E. faecium, also found in E. faecalis and other species), vanB (E.
faecium, E. faecalis) and vanC (E. gallinarum, E. casseliflavus) (Arthur and Courvalin
1993). The vanA type of resistance is clinically very important because enterococci
possessing this genotype exhibit a high level of resistance to teicoplanin (MIC: 16 - 512
µg/ml) in addition to vancomycin (MIC: 64 - 1000 µg/ml) (Klare et al. 2003). The vanA
operon can be located on a plasmid or on the chromosome as a transferable element
(usually Tn1546 or a member of Tn3 family) (Arthur et al. 1993). Approximately sixty
percent of the vancomycin resistant enterococci (VRE) isolated in the United States
possess the vanA genotype (Clark et al. 1993; Deshpande et al. 2007). Apart from
resistance to multiple antibiotics, there is a possibility of gene transfer from VREs to non-
resistant strains of enterococci and other organisms (such as Staphylococcus aureus)
(Noble et al. 1992; Rotun et al. 1999; Smith et al. 1999). Vancomycin resistant
Staphylococcus aureus (VRSA) strains studied to date have been shown to possess the
vanA gene on a Tn1546-like element (Weigel et al. 2003; Clark et al. 2005).
88
vanB is the second most clinically important genotype, conferring moderate to high level
resistance to vancomycin (MIC: 4 - 1000 µg/ml) but not teicoplanin. Similar to vanA, the
vanB operon can be plasmid-borne or chromosomally encoded as a transferable element
(Tn1547) (Quintiliani et al. 1993; Evers et al. 1994). vanC is the only intrinsic,
chromosomally encoded low-level type of resistance (MIC: 2 - 32 µg/ml), which can
occur as either vanC1 or vanC2/3 operons (Klare et al. 2003). The vanC genotype is only
found in certain species such as E. gallinarum and E. casseliflavus that are generally non-
pathogenic. Other lesser-known genotypes of vancomycin resistance include the vanD,
vanE and vanG types that are suspected to be chromosomally encoded and non-
transferable (Cetinkaya et al. 2000). The emergence of multi-drug resistant (MDR)
enterococci in clinical settings has amplified the importance of these organisms as
nosocomial pathogens. Genetic exchange can lead to the transfer of genes conferring
resistance to a wide range of antibiotics such as aminoglycosides, macrolides,
streptogramins, chloramphenicol and vancomycin.
Emergence of VRE
Commercial production of vancomycin derived from the actinomycete Amycolatopsis
orientalis began in the late 1950s (McCormick 1956). It was initially used for the
treatment of all staphylococcal infections but by the mid-1970s it was the drug of choice
for the treatment of infections caused by methicillin-resistant Staphylococcus aureus
(MRSA) (Sande and Johnson 1975). The emergence of vancomycin resistant enterococci
(VRE) in the U.S. and Europe are believed to be the result of different selective
pressures. The first clinical incidence of infections caused by VRE were reported from
the UK and France in 1986 (Leclercq et al. 1988; Uttley et al. 1989). The use of the
89
glycopeptide avoparcin as a growth promoter in animal feed is suspected to be the
primary cause of the emergence of VRE in Europe (McDonald et al. 1997). Several
studies have shown that the selective pressure exerted by avoparcin results in the
development of resistance to vancomycin in enterococcal strains (Aarestrup 1995; Bager
et al. 1997). When the association between the use of avoparcin as feed additive and the
growing incidence of VRE was recognized, the practice of using avoparcin in animal feed
was discontinued by the European Union in 1997 (Commission Directive 97/6 EC). After
the ban, the prevalence of VRE in the fecal flora of food animals and healthy humans has
decreased markedly (Bager et al. 1999; Klare et al. 1999; Pantosti et al. 1999;
Hammerum et al. 2007).
In 1987, one year after the emergence of VRE in Europe, clinical E. faecalis isolates
possessing the vanB gene were isolated in a hospital in St. Louis, MO (Uttley et al.
1988). Use of vancomycin to treat infections in US hospitals had increased from
approximately 2000 kg in 1984 to 10,000 kg in 1996 (Kirst et al. 1998). The increase in
the use of vancomycin to treat infections caused by Clostridium difficile and MRSA
probably provided the selective pressure for emergence of vancomycin resistant
Enterococcus strains (Tenover 2001). According to the National Nosocomial Infections
Surveillance System, the percentage of all nosocomial enterococcal isolates resistant to
vancomycin increased from 0.3% to 7.9% between 1989 and 1993 (NNIS 1994). By
1999, the percentage of VRE isolates went up to 25.9% (NNIS 2001).
In the 1990s, E. faecalis and E. faecium were the most frequently isolated enterococcal
species from clinical samples worldwide (Mutnick et al. 2003). E. faecalis was more
commonly recovered in comparison to E. faecium at a ratio of 5:1. Recent years have
90
seen a reversal of this trend, as recovery of E. faecium isolates has been tenfold higher
than that of E. faecalis (Deshpande et al. 2007; Top et al. 2007). The increase in the
prevalence of vancomycin resistant E. faecium (VREF) in clinical samples is attributed to
the worldwide dissemination of a highly virulent, hospital-adapted cluster designated
clonal complex (CC)-17 (Klare et al. 2005; Treitman et al. 2005; Willems et al. 2005;
Leavis et al. 2006; Top et al. 2008; Valdezate et al. 2009). Emergence of this pathogenic
lineage of VREF possibly resulted from acquisition of antibiotic resistance genes,
virulence factors and pathogenicity islands through horizontal gene transfer via plasmids,
transposons or chromosomal exchange (Rice et al. 1998; Woodford et al. 2001).
The CC-17 cluster is characterized by resistance to ampicillin and ciprofloxacin and the
presence of a variant esp gene (Willems et al. 2005; Deshpande et al. 2007). Many of
these isolates have acquired the variant esp gene on a putative pathogenicity island that
has been predominantly observed in VREF clones associated with disease and/or
epidemics and infrequently found in community VREF isolates (Willems et al. 2001;
Leavis et al. 2004). Genotyping methods such as multiple-locus variable-number tandem
repeat (VNTR) analysis (MLVA) or multi locus sequence typing (MLST) have been used
for epidemiological surveillance of this cluster and databases of patterns exist for
comparison of these patterns worldwide (Klare et al. 2005; Werner et al. 2007). Recently,
some of these strains have demonstrated resistance to chloramphenicol and linezolid; two
antibiotics used in the treatment of infections caused by VREF (Potoski et al. 2002;
Lautenbach et al. 2004). A combination of quinupristin and dalfopristin is the current
drug of choice although some studies have documented resistance against these
antibiotics as well (Baysallar et al. 2004; Lewis et al. 2005).
91
VRE in the environment
Antibiotic resistant enterococci have been detected in environmental waters, sewage,
agricultural runoff, animal feces and feces of healthy human hosts in parts of Europe
(Devriese et al. 1996; VanderAuwera et al. 1996; Aarestrup et al. 1998; Stobberingh et
al. 1999; Gambarotto et al. 2000; Dicuonzo et al. 2001; Guardabassi and Dalsgaard
2004). One study found VanA (high level) VRE in turkeys, turkey farmers, turkey
slaughterers and suburban residents in the Netherlands (Stobberingh et al. 1999). The
pulsed-field gel electrophoresis (PFGE) patterns of VRE isolates from human and animal
origin were different but in some cases, sequences of the vanA-containing transposon in
both groups were similar, suggesting that the vanA gene is transferable from animal to
human associated enterococci or that some VRE strains can colonize animal and human
gastrointestinal tracts. VRE exhibiting the VanA phenotype were isolated from seawater,
nonagricultural soils and blue mussels in Denmark (Guardabassi and Dalsgaard 2004).
High-level VRE were readily isolated from the non-hospital associated community in
European countries including the Netherlands, UK, Germany and Belgium (Aarestrup
and Wegener 1999; Wegener et al. 1999). The high frequency of VRE isolation from the
general community indicates that consumption of food products harboring VRE could be
a mechanism for VRE dissemination (Bates et al. 1994; Jensen et al. 1999). VRE have
been isolated from pork samples, minced beef and poultry products in Europe (Klein et
al. 1998; van den Braak et al. 1998). Comparison of Tn1546-like elements of enterococci
isolated from hospital patients and animal feces demonstrated high similarity, indicating
the possibility of horizontal gene transfer between isolates from different origins and/ or a
common reservoir of resistance genes (Jensen et al. 1998; Woodford et al. 1998).
92
Avoparcin was never approved for use in animal feed in the US and high-level VRE are
not commonly isolated from environmental waters or non-hospital related sources here.
Low level VanC VRE have been isolated from chicken (Harwood et al. 2001) and horse
(Thal et al. 1995) fecal samples and freshly slaughtered chickens and turkeys (Coque et
al. 1996) A recent study found vanA and vanB VRE in marine waters from Washington
and California (Roberts et al. 2009). In the US, high-level VRE are most commonly
isolated from clinical sources (Harwood et al. 2001; Harwood et al. 2004; Treitman et al.
2005).
Nosocomial VRE
Vancomycin resistant enterococci (VRE), particularly Enterococcus faecalis and
Enterococcus faecium, are notorious for causing nosocomial bacteremia (Noskin et al.
1995; Stosor et al. 1998), endocarditis (Megran 1992) and urinary tract infections (Gross
et al. 1976). According to the Centers for Disease Control (CDC), in 2004, one of every
three infections in hospital intensive care units were caused by VRE (Cardo et al. 2004).
VRE colonization of patients occurs due to prolonged antibiotic usage or exposure to
other infected patients (Calfee et al. 2003). Such patients can themselves be at risk or
may act as a reservoir for the transmission of VRE to other patients and healthcare
workers (Crossley 2001; Duckro et al. 2005). Immunocompromised, geriatric and organ
transplant patients have a higher risk of developing VRE infections than other patients
(Bonomo 2000). The emergence of multi-drug resistant (MDR) enterococci in clinical
settings has amplified the importance of these organisms as nosocomial pathogens.
93
Research goals
Enterococci isolated from water, sediments and vegetation of a lake (Lake Carroll), a
river (Hillsborough River) and an estuary (Ben T. Davis beach, Tampa Bay) in Florida
during one sampling event in summer were typed using the HFERP method and their 16S
rRNA genes were sequenced. This process was repeated for one sampling event in the
winter season. Dendrograms illustrating the relatedness of the isolates by BOX-PCR and
by 16S rRNA sequencing were generated. The purpose of this portion of the study was to
compare the ability of BOX-PCR to determine genetic relatedness with that of the “gold
standard” method, 16S rRNA gene sequencing. BOX-PCR typing is a high throughput
and cost-effective technique as compared to sequencing analysis for processing a large
number of isolates. If the typing results are comparable to the results obtained by
sequencing, studies involving greater sampling effort can rely on BOX-PCR typing to
produce reliable estimates of the presence of specific Enterococcus species and the
population diversity of the enterococci.
Hypothesis: Relationships projected by the genotypic BOX-PCR dendrograms will be
similar to those obtained by phylogenetic analysis.
Survival studies have demonstrated increased persistence of enterococci in sediments as
compared to environmental waters, indicating that sediments may play a role in
protecting the organisms from stressors such as elevated temperatures and ultraviolet
radiation from the sun (Sherer et al. 1992; Howell et al. 1996; Anderson et al. 2005).
Vegetation may possibly play a similar role in providing protection and acting as a
reservoir for these organisms. This study aims to investigate the incidence of VRE in
environmental matrices with particular emphasis on the clinically important VanA and
94
VanB phenotypes. Water, sediment and vegetation samples from two fresh water sites
(Lake Carroll and Hillsborough River) and one estuarine site (Ben T Davis beach) and
wastewater samples from a treatment plant, septic tanks and a hospital sewer line were
subjected to VRE detection and identification. Vancomycin resistance was determined
using the agar dilution method by inoculating known concentrations of enterococcal
isolates on vancomycin-amended media. These results were supplemented by molecular
methods such as the detection of vanA, vanB, vanC1 and vanC2/3 genes using PCR.
Hypothesis: Enterococcus spp. that are resistant to high levels of vancomycin (VanA and
VanB phenotype) will be isolated from hospital wastewater. The majority of the
enterococci isolated from environmental samples and residential wastewater will prove
susceptible to vancomycin.
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COMPARISON OF GENOTYPIC AND PHYLOGENETIC RELATIONSHIPS OF
ENVIRONMENTAL ENTEROCOCCUS ISOLATES BY BOX-PCR TYPING AND 16S
rRNA SEQUENCING
Introduction
Enterococci are facultatively anaerobic, gram-positive, catalase-negative cocci that are
commonly found in the gastrointestinal (GI) tract of mammals and birds. Members of the
genus Enterococcus are also readily isolated from soil, surface waters, sediments and
vegetation associated with surface waters, and sometimes food (Fujioka 1999; Bordalo et
al. 2002; Giraffa 2003; Anderson et al. 2005). Enterococci are used as regulatory tools to
assess water quality in fresh and saline waters (U.S. Environmental Protection Agency
1986). Some Enterococcus species possess virulence factors and antibiotic resistance
genes and are capable of causing disease (Shankar et al. 2001; Teng et al. 2002; Rice et
al. 2003). Vancomycin resistant enterococci (VRE) are important nosocomial pathogens
that have been isolated from approximately 30% of the patients in intensive care units in
US hospitals (NNIS 2004; Rice et al. 2004).
Accurate identification and classification of the different species belonging to the genus
Enterococcus is important for both environmental and clinical studies. Phenotypic
methods used to identify Enterococcus species are not very discriminatory or accurate
due to the phenotypic similarity of certain species such as E. gallinarum and E.
casseliflavus, E. cecorum and E. columbae, and E. hirae and E. durans (Devriese et al.
1993). Therefore, DNA-based analyses such as genotyping, genetic sequencing and
targeting particular genes with specific probes and primers are also employed for the
118
identification and classification of enterococci (Malathum et al. 1998; Harwood et al.
2004; Jackson et al. 2004).
Sequencing of the 16S rRNA gene is considered the “gold standard” for microbial
identification (Weisburg et al. 1991; Clarridge 2004; Harmsen and Karch 2004). The 16S
rRNA gene is highly conserved within species and among different species belonging to
the same genus (Woese 1987). Genotyping methods are high throughput and cost-
effective as compared to sequencing analysis for processing a large number of isolates.
Typing of bacterial isolates generates a unique “molecular fingerprint” for each isolate
that can be used for species and strain differentiation. Environmental studies employ
genotyping methods for determination of enterococcal diversity and prevalence in
different habitats (Seurinck et al. 2003; Anderson et al. 2005; Brownell et al. 2007;
Hassan et al. 2007; Pangallo et al. 2008). In clinical studies, genotyping facilitates
identification of variant strains and epidemiological tracking of virulent enterococci
(Malathum et al. 1998; Dicuonzo et al. 2001; Coque et al. 2005; Werner et al. 2007).
BOX-PCR is a genotyping method that amplifies the DNA sequences between highly
conserved repetitive sequences called BOX elements (Martin et al. 1992). BOX elements
are comprised of boxA, boxB and boxC subunits, which are 59, 45 and 50 base pairs
long, respectively and have very low sequence similarity to one another. Among the three
subunits, the boxA subunit appears to be the most conserved in different bacterial species
(Koeuth et al. 1995). The BOXA2R primer sequence is 22 bp long and was originally
derived from the boxA subunit of Streptococcus pneumoniae (Martin et al. 1992; Koeuth
et al. 1995). When BOXA2R sequences in the genome of a bacterial species are targeted
in PCR, it results in differently sized amplicons of the DNA sequences between the
119
interspersed repeats. Separation of these amplicons using gel electrophoresis leads to the
development of species- or strain- specific fingerprint patterns.
Diverse BOX-PCR patterns are generated due to distinct differences in the genome
organization of bacterial species. Better resolution of inter- and intra-species differences
can be achieved by BOX-PCR typing than 16S rRNA sequencing since BOX-PCR
targets DNA sequences in the entire genome while 16S rRNA sequencing targets a small
portion (~1500 bp) of the genome. The evolutionary conservation of BOX elements
(similar to that of the 16S rRNA sequences) enables the comparison of genotypic
relatedness of bacterial species with their phylogenetic relationships.
Several studies have used BOX-PCR typing in environmental studies to determine
genotypic relationships of enterococcal isolates (Brownell et al. 2007; Hassan et al.
2007). These studies include dendrograms that display population similarities based
solely on BOX-PCR genotyping. However, no study has demonstrated that the BOX-
PCR patterns of various strains of a particular enterococcal species are more similar than
strains from different species. Supplementing genotype studies with phylogenetic
analysis will provide additional comparative information about the relationships among
species and strains of enterococci.
The purpose of this study was to compare the ability of BOX-PCR to determine the
genetic relatedness of Enterococcus species and strains with that of the “gold standard”
16S rRNA gene sequencing. To this aim, enterococci were isolated from different
matrices (water, sediments and vegetation) of two freshwater sites and one estuarine site
in Florida during two separate sampling events in an effort to maximize sampled strain
diversity. These isolates were typed using BOX-PCR and their 16S rRNA genes were
120
sequenced. We hypothesized that the relationships projected by the genotypic BOX-PCR
dendrograms will be similar to those obtained by phylogenetic analysis.
Materials and methods
Sample collection and processing
Water, sediment and vegetation samples were collected during two separate sampling
events from Lake Carroll, Hillsborough River and Ben T Davis beach (Tampa Bay). A
list of isolates used in this study according to site and isolation matrix is compiled in
Table 3. Samples were collected in sterile bottles, maintained at 4 C and processed
within 4 hours of collection. Sediment and vegetation samples were diluted 1:10 with
phosphate buffered dilution water (0.0425 g · L-1
KH2PO4 and 0.4055 g · L-1
MgCl2; pH
7.2) (American Public Health Association (APHA) 1998) and particle-associated
bacteria were dislodged using sonication (Anderson et al. 2005). Microorganisms in
water (10ml, 100ml), sediment (2ml, 20ml) and vegetation (2ml, 20ml) samples were
concentrated by membrane filtration through 0.45 µm pore size filters and the filters were
incubated on mEI agar at 41ºC for 18-22 hours (U. S. Environmental Protection Agency
1997). Individual colonies with a blue halo (presumptive enterococci) were picked using
sterile toothpicks and inoculated into Enterococcosel broth (EB) in 96-well microtitre
plates. After incubation at 37ºC for approximately 22 hours, glycerol was added to each
well and the EB plate was frozen at -80ºC until cultures were reanimated. Individual
isolates grown in EB were streaked on TSA for further isolation and incubated overnight
at 37ºC. Individual colonies were picked, inoculated in 2ml of BHI and grown overnight
at 37ºC. DNA was extracted from the broth cultures using the GenElute Bacterial
Genomic DNA kit (Sigma-Aldrich, St. Louis, MO) as per manufacturer’s instructions.
121
Table 3. Isolates used in this study listed according to site (Lake Carroll, Hillsborough
River and Ben T. Davis beach) and environmental matrix (water, sediment and
vegetation).
Isolate ID Site Source
(matrix)
LC12W08 Lake Carroll water
LC17W08 Lake Carroll water
LC1W08 Lake Carroll water
LC3W08 Lake Carroll water
LC15W07 Lake Carroll water
LC28S07 Lake Carroll sediment
LC1S07 Lake Carroll sediment
LC11S07 Lake Carroll sediment
LC24S08 Lake Carroll sediment
LC8S07 Lake Carroll sediment
LC8V08 Lake Carroll vegetation
LC1V07 Lake Carroll vegetation
HR17W07 Hillsborough River water
HR1W07 Hillsborough River water
HR30W07 Hillsborough River water
HR28W08 Hillsborough River water
HR31W07 Hillsborough River water
HR11W07 Hillsborough River water
HR3W07 Hillsborough River water
HR5W07 Hillsborough River water
HR8W07 Hillsborough River water
HR31S07 Hillsborough River sediment
HR26S08 Hillsborough River sediment
HR4S07 Hillsborough River sediment
HR17S07 Hillsborough River sediment
HR1S08 Hillsborough River sediment
HR2S07 Hillsborough River sediment
HR25S08 Hillsborough River sediment
HR26S07 Hillsborough River sediment
HR28S08 Hillsborough River sediment
HR9V07 Hillsborough River vegetation
HR31V08 Hillsborough River vegetation
HR24V08 Hillsborough River vegetation
HR6V07 Hillsborough River vegetation
HR2V08 Hillsborough River vegetation
HR20V07 Hillsborough River vegetation
HR17V07 Hillsborough River vegetation
HR4V08 Hillsborough River vegetation
HR27V08 Hillsborough River vegetation
HR12V07 Hillsborough River vegetation
122
Isolate ID Site Source
(matrix)
BD1W07 Ben T Davis beach water
BD5W07 Ben T Davis beach water
BD8W08 Ben T Davis beach water
BD13W08 Ben T Davis beach water
BD17W07 Ben T Davis beach water
BD20W08 Ben T Davis beach water
BD11S08 Ben T Davis beach sediment
BD31S07 Ben T Davis beach sediment
BD8S08 Ben T Davis beach sediment
BD28S07 Ben T Davis beach sediment
BD5S07 Ben T Davis beach sediment
BD9S08 Ben T Davis beach sediment
BD29S08 Ben T Davis beach sediment
BD25S07 Ben T Davis beach sediment
BD26V08 Ben T Davis beach vegetation
BD22V08 Ben T Davis beach vegetation
BD1V08 Ben T Davis beach vegetation
BD21V08 Ben T Davis beach vegetation
BD11V08 Ben T Davis beach vegetation
BD9V08 Ben T Davis beach vegetation
BD19V08 Ben T Davis beach vegetation
Sequencing the 16S rRNA gene
DNA extracted from individual isolates was amplified using the bacterial universal
primers 8f (5'-AGA GTT TGA TCM TGG CTC AG-3') and 1492r (5'-GGT TAC CTT
GTT ACG ACT T-3') (Lane 1991). The PCR master mix was prepared using 13.5µl of
Jumpstart ReadyMix Taq (Sigma, St. Louis, Missouri), 8.5 µl sterile water, 1µl of each
primer (10 µM) and 1µl of extracted DNA (5 to 15 µg ml-1
adding up to a total volume of
25µl). PCR conditions were as follows: initial denaturation at 94°C for 5 min, followed
by 20 cycles of 94°C for 1 min, 55°C for 1 min, 72°C for 10 min. PCR products were
purified using the QIAQuick PCR Purification Kit (Qiagen, Valencia, CA), and were
shipped to Macrogen Corp. (Rockville, MD). Each sample was sequenced in duplicate
123
using the forward primer 8f and approximately 900 bp of clean sequences were obtained.
Sequences were assembled using Sequencher 4.8 (Gene Codes Corp., Ann Arbor, MI)
and analyzed using BLAST (http://www.ncbi.nlm.nih.gov/BLAST/) to confirm their
identities. Phylogenetic dendrograms were created using the neighbor-joining method and
the bootstrap test was carried out with 500 replications (MEGA 4.0, Tempe, AZ).
BOX-PCR genotyping of enterococci
Enterococcus isolates were typed using the horizontal, fluorophore-enhanced, repetitive
extragenic palindromic-PCR (HFERP) technique (Johnson et al. 2004). Working primer
stock was prepared by mixing 0.09 µg of unlabeled BOX A2R primer (Malathum et al.
1998) (0.68 µg µl-1
) per µl and 0.03 µg of 6-FAM (fluorescein) labeled BOX A2R primer
(0.74 µg µl-1
) per µl. The PCR master mix was prepared using 11.6 µl of sterile water, 5x
Gitschier buffer (Kogan et al. 1987), 2.5µl of 10% DMSO, 1.5 µl BOX A2R working
primer, 0.4µl of 2% BSA, 1mM dNTP mixture, 1µl of Taq DNA polymerase and 1µl of
extracted DNA, all adding up to a total volume of 25µl. The following PCR conditions
were used: an initial denaturation at 95°C for 7 min, followed by 35 cycles of 90°C for 30
s, 40°C for 60 s, 65°C for 8 min. and a final extension at 65°C for 16 min. Enterococcus
faecium C68 was used as the control strain in PCR and loaded onto individual gels to
determine inter gel variability.
A ladder plus non-migrating loading dye mixture was prepared by mixing 5 µl of
Genescan-2500 ROX internal lane standard (Applied Biosystems, Foster City, CA) and
20 µl dye (150 mg Ficoll 400 per ml, and 25 mg blue dextran per ml). 12.5 µl of
individual PCR products was mixed with 3.3 µl of the ROX-dye mixture, loaded in a
1.5% agarose gel and electrophoresced at 90V for 4 hours. Gel images were scanned
124
using a Typhoon 8600 Variable Mode Imager (GE Healthcare). BOX-PCR gel images
were subsequently imported into Bionumerics (Applied Maths, Belgium) and analyzed
using the Pearson similarity coefficient by constructing dendrograms using the
unweighted pair group method with arithmetic mean (UPGMA) (optimization 1.0%,
tolerance 0.5%). BOX-PCR patterns were also compared visually to confirm results.
Known Enterococcus species, including Enterococcus faecalis ATCC 19433,
Enterococcus faecalis ATCC 29212, Enterococcus faecalis ATCC 49383, Enterococcus
faecalis ATCC 700802, Enterococcus faecium C68, Enterococcus faecium ATCC 49224
and Enterococcus casseliflavus ATCC 700327 were also sequenced and typed for
comparison purposes.
Results and Discussion
BOX-PCR dendrograms were in good agreement (77%) with the 16S rRNA phylogenetic
tree as hypothesized. 16S rRNA sequencing was incapable of differentiating among the
known strains of E. faecalis and also among the known strains of E. faecium (Figure 10);
however it could discriminate between the two species. The BOXA2R patterns of three
known strains of E. faecalis (ATCC 19433, 29212 and 700802) were 95% similar and
identical when examined by eye (Figure 11). Minor differences (90%) were observed in
the BOXA2R patterns of the two known strains of E. faecium. Since BOXA2R patterns
of the control strain E. faecium C68 from different gels were 89% similar, patterns with
similarity values greater than 89% were considered indistinguishable.
BOX-PCR typing enabled better differentiation of strains within individual
environmental Enterococcus species as compared to 16S rRNA gene sequencing (Figures
10 and 11). This can be explained in part by the difference in methodology between 16S
125
rRNA gene sequencing and BOX-PCR typing. The 16S rRNA gene sequence (~1500 bp)
has both highly conserved and variable regions and is widely used for determination of
taxonomical evolution (Woese 1987). However, the resolution capacity of the 16S rRNA
gene is limited when identifying closely related organisms (Fox et al. 1992; Stackebrandt
and Goebel 1994). 16S rRNA gene sequencing concentrates on a much smaller portion
of the genome as compared to BOX-PCR. BOX-PCR typing targets sequences located
between interspersed repetitive DNA elements resulting in amplification products of
different sizes that generate a unique genomic fingerprint of individual bacterial strains.
The number and location of bands in the fingerprint depend upon the size of the genome
and the number of primer binding sites. Variation in genome sizes among different strains
of a particular species leads to generation of multiple strain-specific fingerprint patterns.
For example, Oana et al mapped the genomes of four strains of E. faecium and reported
genome sizes that varied from 2550 to 2995 kb (Oana et al. 2002). Therefore, BOX-PCR
typing can differentiate between different strains of the same species better than 16S
rRNA sequencing.
The 16S rRNA gene sequences of E. faecium and E. mundtii were approximately 98%
identical and formed a single cluster in the phylogenetic tree. In contrast, the BOX-PCR
patterns of E. faecium and E. mundtii demonstrated less than 60% similarity. This
demonstrates the ability of BOX-PCR genotyping to differentiate between closely related
species of enterococci. In a similar study, Rademaker et al compared genotypic
relationships of Xanthomonas species and strains by BOX-PCR with DNA-DNA
hybridization studies and observed a high correlation between the two methods
(Rademaker et al. 2000). This observation is in agreement with the high correlation
126
between the genotypic and phylogenetic relationships of Enterococcus spp. observed in
our study. In contrast, other studies have found poor correlation between genotypic and
phylogenetic methods used to project species/ strain relationships (Tacao et al. 2005;
Binde et al. 2009).
The 16S rRNA gene sequence of an environmental strain of Lactococcus garvieae served
as an outgroup while constructing the phylogenetic tree (Figure 10). The 16S rRNA
sequence of Lactococcus garvieae is 11.4 to 11.8% different from that of Enterococcus
species (Patel et al. 1998). The BOX-PCR pattern of Lactococcus garvieae clustered
together with other species of enterococci and did not form an outgroup in the BOX-PCR
dendrogram (Figure 11). This indicates that certain closely related genera might not be as
distinguishable using BOX-PCR typing as they are by 16S rRNA sequencing.
Of the 61 isolates sequenced in this study, only one isolate was identified as a non-
Enterococcus. This finding demonstrates the specificity of mEI agar, the medium used
for isolation of enterococci from surface waters. In other studies, mEI agar was found to
be less specific since organisms belonging to other genera were isolated from
environmental samples (Moore et al. 2006), biosolids (Viau and Peccia 2009) and clinical
samples (Goh et al. 2000) along with Enterococcus spp. According to the US
Environmental Protection Agency (USEPA), the false-positive rate for isolation of non-
enterococci from environmental samples on mEI agar is 6% (U. S. Environmental
Protection Agency 1997; Messer and Dufour 1998). In comparison, we observed a very
low false-positive rate (1.6%) for isolation of non-enterococci from environmental
matrices in our study.
127
A BLAST query was performed on the entire genomes of E. faecium C68
(ACJQ00000000) and E. faecalis ATCC 700802 (AE016830) with the BOXA2R primer
(5’- ACG TGG TTT GAA GAG ATT TTC G -3’) as the target sequence. The BOXA2R
primer sequence appeared 33 times within the genome of E. faecium C68 and 36 times
within the genome of E. faecalis ATCC 700802. In the first documentation of BOX
elements in Streptococcus pneumoniae, the authors noted the presence of 25 BOX
sequences in the genome of the organism. Further analysis of the distance between the
BOXA2R sequence segments might be useful in predicting the length of amplicons
produced during BOX-PCR typing.
Genotypic and phylogenetic relationships between Enterococcus species and strains were
compared using BOX-PCR typing and 16S rRNA sequencing, respectively. Although
BOX-PCR genotyping was found to be more discriminatory at the strain level than 16S
rRNA sequencing, the incorrect grouping of some strains with strains belonging to a
different species instead of its own species group raises doubts about the ability of the
method to correctly project relatedness of all Enterococcus strains. While studies relying
solely on BOX-PCR typing should exercise caution while interpreting phylogenetic
relationships projected by BOX-PCR dendrograms, the method does provide a useful
approximation of phylogeny. BOX-PCR typing may be an excellent tool for investigating
strain diversity but this method should not be employed exclusively for species
identification and association.
128
Figure 10. Phylogenetic tree constructed using the neighbor-joining algorithm to
evaluate the distance between 16S rRNA gene sequences of environmental enterococci.
E. mundtii/
faecium
E. hirae
E. gilvus
129
Figure 10. continued
E. casseliflavus/
flavescens
E. faecalis
130
Figure 10. continued
E. silesiacus/ moraviensis/
caccae
E. faecalis
Lactococcus
garvieae
131
100 80 60 40
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.
.
.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
HR 2S 07
HR 31W
07 E. casseliflavus
BD 13W 08
BD 20W 08
LC 12W 08
HR 26S 07
BD 9S 08
HR 27V 08
LC 1W 08
BD 5S 07
HR 28S 08
BD 8S 08
BD 28S 07
HR 4V 08
BD 8W 08
HR 25S 08
HR 28W 08
BD 17W 07
HR 1S 08
LC 1V 07
LC 3W 08
LC 24S 08
HR 17S 07
BD 26V 08
BD 1V 08
LC 17W 08
HR 30W 07
HR 17W 07
E. faecium C68
E. faecium 49224
BD 1W 07
Figure 11. Dendrogram demonstrating the similarity of BOX-PCR patterns of
Enterococcus species isolated from environmental matrices.
E. casseliflavus
E. hirae
E. casseliflavus/
flavescens
E. hirae
E. mundtii
E. faecium
132
.
.
.
.
.
.
.
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.
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.
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.
.
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.
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.
.
.
.
.
.
.
.
.
E. faecium 49224 BD 1W 07 HR 24V 08
LC 8V 08
BD 19V 08
E. faecalis 29212
E. faecalis 700802
E. faecalis 19433
BD 25S 07
E. faecalis 49383
LC 1S 07
LC 28S 07
LC 15W 07
BD 29S 08
BD 21V 08
BD 9V 08
BD 11V 08
LC 8S 07
HR 4S 07
HR 26S 08
HR 2V 08
HR 6V 07
BD 5W 07
BD 22V 08
BD 31S 07
HR 9V 07
HR 31V 08
LC 11S 07
HR 1W 07
BD 11S 08
HR 8W 07
HR 11W 07
HR 17V 07
HR 3W 07
HR 5W 07
HR 20V 07
HR 31S 07
HR 12V 07
Figure 11. continued
E. silesiacus/ caccae
E. silesiacus/ caccae
E. hirae
E. faecalis
E. mundtii
E. gilvus
E. faecium
E. faecalis
E. gilvus
E. faecium
L. garvieae
E. gilvus
133
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PREVALENCE OF VANCOMYCIN RESISTANT ENTEROCOCCI IN
ENVIRONMENTAL MATRICES AND WASTEWATER
Abstract
Vancomycin resistant enterococci (VRE) are important nosocomial pathogens whose
prevalence in environmental waters is infrequently investigated. Environmental and
wastewater samples from Florida (USA) were screened for VRE. Low-level VRE (< 32
µg ml-1
) were a proportionally greater fraction of enterococci at freshwater vs. estuarine
sites, while high-level VRE ( 32 µg ml-1
) were not detected. Between 20% and 61% of
the total enterococci isolated from surface water sites were low-level VRE. Genotype
vanC2/3 Enterococcus casseliflavus-flavescens dominated environmental VRE
populations while vanC1 E. gallinarum was dominant in residential wastewater. High-
level VRE (vanA E. faecium) were only isolated from hospital sewer line samples, and all
displayed intermediate resistance to ampicillin and ciprofloxacin but were sensitive to
chloramphenicol and rifampin. They also had indistinguishable BOX-PCR genotypes.
Twenty percent of the nosocomial isolates possessed the virulence-associated esp gene
140
variant that is unique to E. faecium. Two esp-positive E. faecium strains were isolated
from surface waters, but were vancomycin-sensitive. Infections caused by low-level VRE
can be difficult to treat as they sometimes demonstrate vancomycin susceptibility in vitro
while being resistant in vivo. While the relative rareness of high-level VRE in sources
tested other than hospital wastewater is encouraging, the high proportion of low-level
VRE isolated from surface waters and the preponderance of high-level VRE in hospital
wastewater are cause for concern and should be the subject of better surveillance.
Introduction
Enterococci can cause blood stream infections, endocarditis, and urinary tract infections
(Gross et al. 1976; Megran 1992; Pfaller et al. 1998). Vancomycin resistant enterococci
(VRE) have been isolated from approximately 30% of the patients in intensive care units
in US hospitals (NNIS 2004; Rice et al. 2004). VRE have been detected in environmental
waters, sewage, agricultural runoff, animal feces and feces of healthy human hosts in
parts of Europe (Devriese et al. 1996; VanderAuwera et al. 1996; Aarestrup et al. 1998;
Stobberingh et al. 1999; Gambarotto et al. 2000; Dicuonzo et al. 2001; Guardabassi and
Dalsgaard 2004). This widespread prevalence in Europe is mainly attributed to the
practice of using the glycopeptide avoparcin in animal feeds for growth promotion
(McDonald et al. 1997). In contrast, high-level VRE have seldom been reported in
environmental waters or non-hospital related sources in the US (Coque et al. 1996;
Harwood et al. 2001), although high-level VRE were isolated from marine waters in
Washington and California (Roberts et al. 2009). Harwood et al isolated vanA VRE from
hospital wastewater, whereas chicken feces and residential wastewater isolates exhibited
the low level vanC genotype (Harwood et al. 2001).
141
Vancomycin resistance in enterococci is attributed to the possession of gene clusters
designated vanA (E. faecium, E. faecalis), vanB (E. faecium, E. faecalis) and vanC (E.
gallinarum, E. casseliflavus) (Cetinkaya et al. 2000). vanA and vanB mediated resistance
can be plasmid-borne or chromosomally encoded as a transferable element (Arthur et al.
1993; Rice et al. 1998). vanA confers high-level resistance to vancomycin (MIC: 64 -
>1000 µg ml-1
) and teicoplanin (MIC: 16 - 512 µg ml-1
) while vanB confers moderate to
high-level resistance to vancomycin only (MIC: 4 - 1000 µg ml-1
). vanC is an intrinsic,
chromosomally encoded, low-level type of resistance (MIC: 2 - 32 µg ml-1
), found in
certain species that are generally non-pathogenic, but that occasionally cause disease
(Cetinkaya et al. 2000). Other low-level VRE genotypes include vanD, vanE and vanG
(Fines et al. 1999; Cetinkaya et al. 2000).
E. faecalis and E. faecium also possess virulence factors such as adhesins, cytolysins,
gelatinase, and serine protease. The esp gene, which encodes the enterococcal surface
protein, may facilitate colonization of the urinary tract (Shankar et al. 2001) and biofilm
formation (Toledo-Arana et al. 2001; Heikens et al. 2007). esp was initially described in
clinical E. faecalis isolates (Shankar et al. 1999). An esp variant was later discovered in
E. faecium isolates from nosocomial infections (Eaton and Gasson 2001; Willems et al.
2001) and is now used as a marker of human fecal pollution in environmental waters
(Scott et al. 2005). Several PCR methods have been developed for the detection of the E.
faecium esp gene in microbial source tracking (MST) (McDonald et al. 2006; McQuaig et
al. 2006; Brownell et al. 2007; Whitman et al. 2007; Ahmed et al. 2008a; Ahmed et al.
2008b) and clinical studies (Eaton and Gasson 2002; Leavis et al. 2003; Coque et al.
2005).
142
Recently a hospital-adapted sub-population of vancomycin resistant E. faecium (VREF)
has been implicated in nosocomial outbreaks in five continents, including North America
(Top et al. 2007; Valdezate et al. 2009). Multi-locus sequence typing (MLST) revealed a
specific genetic lineage of VREF designated clonal complex-17 (CC-17), which is also
characterized by intermediate to high resistance to ampicillin and ciprofloxacin (Leavis et
al. 2006b; Deshpande et al. 2007; Top et al. 2007). Many of these isolates possess the E.
faecium variant esp gene on a putative pathogenicity island (Willems et al. 2005; Leavis
et al. 2006a; Leavis et al. 2006b; Top et al. 2008). Previous studies have documented the
co-occurrence of the esp gene with the hyaluronidase (hyl) gene in VREF strains (Rice et
al. 2003; Vankerckhoven et al. 2004; Klare et al. 2005). The hyl gene is a virulence-
associated gene that potentially contributes to invasion of the nasopharynx (Rice et al.
2003).
The growing incidence of vanA and vanB VRE infections in hospitals in the US
highlights the need for surveillance of the prevalence of VRE in surface waters, as well as
associated matrices such as sediments and vegetation, to determine the overall threat to
the community. Survival studies have demonstrated increased persistence of enterococci
in sediments as compared to environmental waters, indicating that sediments may play a
role in protecting the organisms from stressors such as elevated temperatures and
ultraviolet radiation from the sun (Sherer et al. 1992; Howell et al. 1996; Anderson et al.
2005). Vegetation may play a similar role in providing protection and act as a reservoir
for these organisms (Byappanahalli et al. 2003; Whitman et al. 2003), yet the prevalence
of antibiotic-resistant bacteria in vegetation and sediment has been infrequently explored
(Cordova-Kreylos and Scow 2007; Matyar et al. 2008).
143
Enterococci isolated from environmental water, sediment and vegetation samples as well
as residential and hospital wastewater were tested for vancomycin resistance using
culture-based methods followed by molecular typing. We hypothesized that Enterococcus
spp. that are resistant to high levels of vancomycin (vanA and vanB genotypes) would be
isolated from hospital wastewater whereas the majority of the enterococci isolated from
environmental samples and residential wastewater will prove susceptible to vancomycin.
Materials and methods
Sample collection and processing
Water, sediment and vegetation samples were collected from Lake Carroll, Hillsborough
River and Ben T Davis beach (Tampa Bay). The Lake Carroll site (28º 2.918’ N, 82º
29.828’ W) is on a small residential lake in West Tampa surrounded completely by
suburban housing. The Hillsborough River site (28º 4.260’ N, 82º 22.671’ W) is at the
University of South Florida's Riverfront Park, downstream of a substantial amount of
protected, undeveloped land. The upper bay site, Ben T Davis Beach (27º 58.141’ N, 82º
34.522’ W), is west of the City of Tampa in Old Tampa Bay. Dominant vegetation
species at the freshwater sites were Alternanthera philoxeroides (alligator weed), Egaria
densa (Brazilian waterweed), Hydrilla verticilata, Myriophyllum aquaticum (parrot
feather), and Vallisenaria Americana (eel grass). The dominant vegetation species at the
estuarine site was Halodule wrightii (shoal grass). Vegetation coverage at all of these
sites ranged from moderate to heavy and sediments were composed of fine quartz sand.
Samples were collected in sterile bottles, maintained at 4 C and processed within 4 hours
of collection. Sediment and vegetation samples were diluted 1:10 with phosphate
buffered dilution water (0.0425 g · L-1
KH2PO4 and 0.4055 g · L-1
MgCl2; pH 7.2)
144
(American Public Health Association (APHA) 1998) and particle-associated bacteria
were dislodged using sonication (Anderson et al. 2005). Microorganisms in water (1ml,
10ml), sediment (2ml, 20ml) and vegetation (2ml, 20ml) samples were concentrated by
membrane filtration through 0.45 µm pore size filters and the filters were incubated on
mEI agar at 41ºC for 18-22 hours (U. S. Environmental Protection Agency 1997).
Individual colonies with a blue halo (presumptive enterococci) were picked using sterile
toothpicks and inoculated into Enterococcosel broth (EB) in 96-well microtitre plates.
After incubation at 37ºC for approximately 22 hours, glycerol was added to each well and
the EB plate was frozen at -80ºC until cultures were reanimated. Water (100 ml) and
sediment (25 ml) samples were also filtered and the filters incubated on mEI agar with
vancomycin (6 µg ml-1
) (to determine the percentage of VRE from the total enterococci).
Water (3 liters) and sediment (25 ml) samples were also filtered and the filters incubated
on mEI agar with vancomycin (32 µg ml-1
) for isolation of high-level VRE.
Wastewater samples were collected from the Falkenburg Road Advanced Wastewater
Treatment Plant in Tampa, FL, from a hospital sewer line and from a septic pump truck
(three samples each). Samples were handled as detailed above, but enterococci were
isolated as follows. Hospital wastewater samples (1 ml and 100 µl) were filtered and
incubated on mEI agar and mEI agar with vancomycin (6 µg ml-1
) in triplicate (to
determine the percentage of VRE from the total enterococci). Residential and septic
pump truck wastewater samples were filtered and filters were incubated on mEI agar (1
ml and 100 µl) and mEI agar with 6 µg ml-1
vancomycin (10 ml and 25 ml) in triplicate.
145
Vancomycin susceptibility testing
The agar dilution screening method was used to screen for VRE (Swenson et al. 1994).
Isolates grown in EB were streaked on trypticase soy agar (TSA) for further isolation and
incubated overnight at 37ºC. Individual colonies were inoculated in brain heart infusion
(BHI) broth and grown overnight at 37ºC. The turbidity of the overnight culture was
adjusted to match the 0.5 McFarland standard and 10µl of the suspension was spot
inoculated on BHI agar with vancomycin (6 µg ml-1
), which might potentially discourage
the growth of a few low-level vanB and many low-level vanC VRE isolates. Isolates
displaying growth were further tested by spot inoculation on BHI agar with 32 µg ml-1
vancomycin. VRE isolates that grew at 6µg ml-1
concentration of vancomycin but did not
grow at 32 µg ml-1
were reported as LL-VRE whereas the VRE isolates that grew at 32
µg ml-1
of vancomycin were reported as HL-VRE. These results were reconfirmed by
amplifying the vancomycin resistance genes of individual isolates. Vancomycin MICs for
50 randomly selected LL-VRE isolates were determined by both agar dilution and broth
microdilution methods (4, 6 and 8 µg ml-1
vancomycin).
DNA was extracted from the overnight broth cultures using the GenElute Bacterial
Genomic DNA kit (Sigma-Aldrich, St. Louis, MO) as per manufacturer’s instructions.
Genes conferring vancomycin resistance were targeted in a multiplex PCR (Table 4)
using extracted DNA from the individual isolates as template (Dutka-Malen et al. 1995).
Positive controls used include Enterococcus faecalis A256 (vanA), Enterococcus faecium
C68 (vanB), Enterococcus gallinarum ATCC 49573 (vanC1) and Enterococcus
casseliflavus ATCC 700327 (vanC2/3). The vancomycin susceptible strain Enterococcus
146
faecalis ATCC 29212 was used as a negative control. The presence of products of the
correct molecular size was confirmed using gel electrophoresis.
Isolates identified as HL-VRE by multiplex PCR and phenotype on vancomycin-
amended media were further tested for susceptibility to ampicillin (12, 16 and 20 µg ml-
1), chloramphenicol (6, 8 and 10 µg ml
-1), ciprofloxacin (2, 4 and 6 µg ml
-1) and rifampin
(1 and 2 µg ml-1
) as described in the Clinical and Laboratory Standards Institute
guidelines using standard breakpoints (Clinical and Laboratory Standards Institute 2006).
The choice of antibiotics was based on those identified as therapeutically important by
the SENTRY Antimicrobial Surveillance Program (Deshpande et al. 2007). Isolates were
tested in triplicate by both the agar dilution and broth microdilution methods.
Enterococcus faecalis ATCC 29212 was used as a negative control. Vancomycin MICs
for HL-VRE isolates were determined by both agar dilution and broth microdilution
methods (64, 128, 256 and 512 µg ml-1
vancomycin).
Sequencing the 16S rRNA gene and vanA gene
DNA extracted from individual isolates was amplified using the bacterial universal
primers 8f and 1492r (Lane 1991) (Table 4). The PCR master mix was prepared using
13.5µl of Jumpstart ReadyMix Taq (Sigma, St. Louis, Missouri), 8.5µl sterile water, 1µl
of each primer (10 µM) and 1µl of extracted DNA (5-15 ng µl-1
) adding up to a total
volume of 25µl. PCR conditions were as follows: initial denaturation at 94°C for 5 min,
followed by 20 cycles of 94°C for 1 min, 55°C for 1 min, 72°C for 10 min. PCR products
were purified using the QIAQuick PCR Purification Kit (Qiagen, Valencia, CA), and
were shipped to Macrogen Corp. (Rockville, MD). Each sample was sequenced in
duplicate using the forward primer 8f and approximately 900 bp of clean sequences were
147
obtained. The vanA gene (~ 640bp) was amplified using previously published primers
and PCR conditions (Dutka-Malen et al. 1995) and the PCR amplicons were sequenced
in both forward and reverse directions using the vanA primer set. Sequences were
assembled using Sequencher 4.8 (Gene Codes Corp., Ann Arbor, MI) and analyzed using
BLAST (http://www.ncbi.nlm.nih.gov/BLAST/) to confirm their identities.
Table 4. Primers used in this study.
Amplified
gene
Product
size
(bp)
Oligonucleotide sequence Reference
vanA 732 A1 (5’-GGGAAAACGACAATTGC-3’)
A2 (5’-GTACAATGCGGCCGTTA-3’)
Dutka-Malen
et al, 1995
vanB 635 B1 (5’-ATGGGAAGCCGATAGTC-3’)
B2 (5’-GATTTCGTTCCTCGACC-3’)
Dutka-Malen
et al, 1995
vanC1 822 C1 (5’-GGTATCAAGGAAACCTC-3’)
C2 (5’-CTTCCGCCATCATAGCT-3’)
Dutka-Malen
et al, 1995
vanC2/3 439 D1 (5’-CTCCTACGATTCTCTTG-3’)
D2 (5’-CGAGCAAGACCTTTAAG-3’)
Dutka-Malen
et al, 1995
16S
rRNA 1484
8f (5'-AGA GTT TGA TCM TGG CTC AG-3')
1492r (5'-GGT TAC CTT GTT ACG ACT T-3') Lane, 1991
esp 680
F (5’-TAT GAA AGC AAC AGC ACA AGT T-
3’)
R (5’-ACG TCG AAA GTT CGA TTT CC-3’)
Scott et al,
2005
esp 510 14F (5’-AGATTTCATCTTTGATTCTTGG-3’)
12R (5’-AATTGATTCTTTAGCATCTGG-3’)
Leavis et al,
2003
esp 956
esp11 (5’-TTGCTAATGCTAGTCCACGACC-
3’) esp12 (5’-
GCGTCAACACTTGCATTGCCGAA-3’)
Leavis et al,
2003
hyl 661
hylEfm F (5’-GAGTAGAGGAATATCTTAGC-
3’)
hylEfm R (5’-AGGCTCCAATTCTGT-3’)
Rice et al,
2003
BOXA2R variable 5’- ACG TGG TTT GAA GAG ATT TTC G -3’ Malathum et
al, 1998
148
BOX-PCR genotyping of enterococci
Enterococcus isolates were typed using the horizontal, fluorophore-enhanced, repetitive
extragenic palindromic-PCR (HFERP) technique (Johnson et al. 2004). Working primer
stock was prepared by mixing 0.09 µg of unlabeled BOX A2R primer (Malathum et al.
1998) (0.68 µg µl-1
) per µl and 0.03 µg of 6-FAM (fluorescein) labeled BOX A2R primer
(0.74 µg µl-1
) per µl. The PCR master mix was prepared using 11.6µl of sterile water, 5x
Gitschier buffer (Kogan et al. 1987), 2.5µl of 10% DMSO, 1.5 µl BOX A2R working
primer, 0.4µl of 2% BSA, 1mM dNTP mixture, 1µl of Taq DNA polymerase and 1µl of
extracted DNA, all adding up to a total volume of 25µl. The following PCR conditions
were used: an initial denaturation at 95°C for 7 min, followed by 35 cycles of 90°C for 30
s, 40°C for 60 s, 65°C for 8 min. and a final extension at 65°C for 16 min. Enterococcus
faecium C68 was used as the control strain in PCR and loaded onto individual gels to
determine inter gel variability. A ladder plus non-migrating loading dye mixture was
prepared by mixing 5 µl of Genescan-2500 ROX internal lane standard (Applied
Biosystems, Foster City, CA) and 20 µl dye (150 mg Ficoll 400 per ml, and 25 mg blue
dextran per ml). 12.5 µl of individual PCR products was mixed with 3.3 µl of the ROX-
dye mixture, loaded in a 1.5% agarose gel and electrophoresced at 90V for 4 hours. Gel
images were scanned using a Typhoon 8600 Variable Mode Imager (GE Healthcare).
Screening for virulence factors
Isolates were tested for the presence of the esp and hyl genes encoding the enterococcal
surface protein of E. faecium and the hyaluronidase enzyme, respectively. The esp gene
was amplified using one primer set employed by microbial source tracking (MST) studies
and two sets of primers described in clinical studies (Table 4). The primer set used in
149
MST studies was described as a proposed marker of human fecal pollution (Scott et al.
2005). The primer set 14F and 12R was used to amplify the esp gene as described
previously by Leavis et al (Leavis et al. 2003). Isolates negative for the presence of the
esp gene were tested using another primer set esp11 and esp12 to ensure accuracy of
results (Leavis et al. 2003).
The PCR master mix for amplification of the hyl gene (Table 4) was comprised of 12.5
µl of GoTaq Green, 8.5 µl of sterile water, 1 µl each of 10µM forward and reverse
primers (Rice et al. 2003) and 2 µl of template DNA. PCR conditions were as follows: an
initial denaturation at 94°C for 3 min, followed by 35 cycles of 94°C for 45 s, 55°C for
45 s, 72°C for 30 s and a final extension at 72°C for 5 min. Enterococcus faecium C68
was used as the positive control and Enterococcus faecalis ATCC 29212 was used as the
negative control for detection of both virulence factors.
Statistical analysis
BOX-PCR gel images were imported into Bionumerics (Applied Maths, Belgium) and
analyzed using the Pearson similarity coefficient in order to construct dendrograms using
the unweighted pair group method with arithmetic mean (UPGMA) (optimization 1.0%,
tolerance 0.5%). BOX-PCR patterns were also compared visually to confirm results.
Differences in the frequency of observation of LL-VRE within each matrix between sites
were calculated using the chi-square test (GraphPad InStat, La Jolla, USA).
Results
Vancomycin resistant enterococci isolated from the freshwater sites, Lake Carroll (LC)
and Hillsborough River (HR), as well as those isolated from estuarine waters at Ben T
150
Davis (BTD) beach exhibited the VanC phenotype (henceforth referred to as low-level or
LL-VRE) (Tables 5 and 6). The minimum inhibitory concentration (MIC) of vancomycin
for fifty randomly selected LL-VRE isolates was 8 µg ml-1
. The majority of the VRE
strains isolated from environmental matrices at the three sites tested positive for the
vanC2/3 gene and were identified as E. casseliflavus-flavescens (the two species are
indistinguishable by small subunit rRNA sequencing). One isolate from LC sediment and
one isolate each from BTD water and sediment tested positive for the vanC1 gene. No
VanA or VanB (high-level or HL-VRE) phenotypes were isolated from these sites. HL-
VRE were not detected even when larger sample volumes were screened on mEI agar
with 32 µg ml-1
vancomycin, an inhibitory concentration for LL-VRE.
Table 5. VRE genotypes observed from environmental water and wastewater samples.
Source
No. of
isolates
typed
Genotypes observed % VRE (total
Enterococcus
concentration)d
vanA vanB vanC
Lake Carroll 187a
0 0 124 61 (0.52 x 102)
Hillsborough River 192a
0 0 100 36 (0.47 x 102)
Ben T Davis beach 188a
0 0 46 20 (0.21 x 102)
Residential wastewater
25b,c
0 0 25 1.6 (3.0 x 104)
Hospital wastewater
54b
25 0 29 35 (4.9 x 103)
aIsolated on mEI (no vancomycin) and screened post-isolation
bPre-screened for vancomycin resistance on mEI + 6 g ml
-1 vancomycin
c20 isolates from WWTP influent and 5 isolates from a septic pump truck
dCFU ml
-1
151
The proportion of VRE (VRE as a percentage of total enterococci screened) observed in
the water, sediment and vegetation samples (hereafter termed matrices) within each
surface water site is listed in Table 6. In some cases, i.e. LC water and HR sediment, LL-
VRE were by far the majority of total enterococci screened. Significant differences in the
proportion of LL-VRE were observed in all site-by-site comparisons. Significant
differences were also observed when each matrix was compared across the three sites.
VRE proportions in estuarine waters were markedly and significantly lower than
freshwater for all three matrices. LL-VRE proportions for each site were highest in either
water or sediment samples, but never vegetation (Table 6). In fact, for the HR and BTD
sites, the lowest VRE proportions were observed in vegetation samples.
Table 6. Low-level VRE as a percentage of total enterococci in each matrix (water,
sediment, vegetation) at each site.
Source
% VRE (total isolates screened)
Total
Water Sediment Vegetation
Lake Carroll 97 (62) 35 (63) 68 (62) 66 (187)
Hillsborough River 53 (64) 73 (64) 30 (64) 52 (192)
Ben T Davis beach 25 (64) 47 (60) 3 (64) 24 (188)
Two isolates from a Hillsborough River vegetation sample carried the variant esp gene
that is unique to E. faecium and is associated with human feces (Scott et al. 2005;
Brownell et al. 2007). The BOXA2R genotypes of these two isolates were 99% similar
152
Figure 12. Dendrogram demonstrating the similarity of BOX-PCR patterns of vanA
VREF isolated from hospital wastewater, esp- positive isolates are underlined.
and DNA sequencing identified both as E. faecium. Both isolates were susceptible to
vancomycin and tested negative for vanA, vanB and vanC by PCR.
100 98 96 94 92 90
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
U3A4
U3E6
U3A2
U3A1
UH2
U2B6
U2C7
U2C6
U2C12
U3A6
UC2
UE2
UA2
UF2
UB2
UD2
UA3
UF3
UB3
UE3
UC3
UH3
UG3
UD3
UG2
.
.
.
.
.
.
153
Approximately 1.6 % of the total enterococci (3 x 104 CFU ml
-1; average of three
separate sampling events) isolated from the residential wastewater demonstrated low-
level vancomycin resistance and carried the vanC1 genotype (Table 5). These LL-VRE
were identified as E. gallinarum by 16S rRNA gene sequencing. No HL-VRE were
isolated from residential wastewater.
Approximately 35% (1.7 x 103 CFU ml
-1) of the enterococci (4.9 x 10
3 CFU ml
-1; average
of three separate sampling events) isolated from the hospital wastewater were
vancomycin resistant, including both LL-VRE and HL-VRE (Table 5). Forty-six percent
of the 54 VRE tested carried the vanA gene and their BOXA2R patterns were 89%
similar (Figure 12) while the rest of the isolates demonstrated the vanC1 genotype. Since
BOXA2R patterns of the control strain E. faecium C68 were also 89% similar, the vanA
VRE BOX patterns were indistinguishable, and may represent clones.
Seven vanA VRE isolates chosen at random were sequenced and identified to the species
level as E. faecium (GenBank Accession #s GQ489017 to GQ489023). The VREF strains
were resistant to intermediate levels of ampicillin (MIC > 16 µg ml-1
) and ciprofloxacin
(MIC > 4 µg ml-1
) but sensitive to chloramphenicol (MIC = 8 µg ml-1
) and rifampin (MIC
< 2 µg ml-1
). The MIC for vancomycin was 512 µg ml-1
. The esp gene was detected in
20% (5/ 25) of the VREF isolates, and all three sets of esp primers gave the same result.
None of the isolates carried the hyl gene. The vanA gene sequences of esp positive and
esp negative VREF isolates were 100% identical as determined by aligning the sequences
on BLAST (GenBank Accession #s GQ489012 to GQ489016).
154
Discussion
Water, sediment, and vegetation samples were collected from two freshwater and one
estuarine site in Florida for this study, which is the first study to evaluate VRE in
vegetation samples and to compare VRE proportions in freshwater vs. estuarine waters
and across different matrices. In fact, very few studies in the US have attempted to
determine the vancomycin susceptibility of environmental enterococci (Harwood et al.
2001; Moore et al. 2008; Roberts et al. 2009). In our study, LL-VRE were readily
isolated from modest volumes of environmental water samples (2ml to 100ml) without
enrichment or use of vancomycin-amended media for the initial screening. Moore et al
assessed E. faecalis and E. faecium from ocean waters and sewage in Southern California
for vancomycin resistance (Moore et al. 2008) by screening on media amended with 16
µg ml-1
vancomycin. Under these conditions all isolates were vancomycin-susceptible;
however, this concentration inhibits most LL-VRE. It is therefore not possible to compare
the frequency of isolation of environmental enterococci from the California study with
the current study.
Another study reported the detection of HL-VRE at public beaches in Washington and
California (Roberts et al. 2009). The vast majority of LL-VRE enterococci were also
excluded from this study, since bacteria were screened on mE agar supplemented with 18
µg ml-1
vancomycin. HL-VRE were infrequently detected over the eight year study,
which is noteworthy because HL-VRE had not previously been detected outside hospital
or wastewater settings in the US (Roberts et al. 2009). In contrast, we found no HL-VRE
in any environmental water matrix, but a very high proportion of LL-VRE. Our samples
were collected over a short time period (6 months), and in light of the findings of Roberts
155
et al. and the prevalence of HL-VRE in hospital wastewater, greater efforts toward
surveillance of VRE in these waters is appropriate.
The high prevalence of LL-VRE observed in the surface waters in this study is a cause
for concern from the public health perspective. LL-VRE can cause serious infections
such as endocarditis, bacteremia and meningitis, particularly in immunocompromised
patients (Yoshimoto et al. 1999; Kurup et al. 2001; Reid et al. 2001; Dargere et al. 2002).
A six-year survey in a hospital in Japan found LL-VRE associated with approximately
12% of all enterococcal bacteremia cases (Koganemaru and Hitomi 2008). Another study
found no differences in the severity of illness and mortality rates between E. faecalis
bacteremia and LL-VRE bacteremia (de Perio et al. 2006). Treatment of infections
caused by intrinsically resistant LL-VRE can be difficult because they sometimes
demonstrate vancomycin susceptibility in vitro while being resistant in vivo
(Ratanasuwan et al. 1999; Reid et al. 2001). Recently a strain of E. casseliflavus/
gallinarum possessing the vanA gene and one possessing both vanA and vanB genes were
isolated from beaches in WA and CA (Roberts et al. 2009). Acquisition of high-level
vancomycin resistance genes by LL-VRE is of particular concern because phenotypic
identification from clinical samples can confound treatment strategy and infection control
measures (Dutka-Malen et al. 1994; Yoshikazu 1996; Coombs et al. 1999; Roberts et al.
2009), and because they are so prevalent in the environment.
The proportion of LL-VRE (calculated as percentage of total enterococci) isolated from
the environmental matrices was significantly higher than the proportion of LL-VRE
isolated from the residential wastewater. Although very few vanC E. gallinarum were
isolated from residential wastewater in Tampa eight years prior to this study, the
156
difference in screening methods does not permit a direct comparison with the proportion
of LL-VRE isolated from residential wastewater in the current study (Harwood et al.
2001). The only documented isolation of HL-VRE from community wastewater in the
US is the detection of 49 E. faecium isolates possessing the vanA gene and one
possessing the vanB gene from a semiclosed agri-food system in Texas (Poole et al.
2005).
In this study, vanA VREF with indistinguishable BOX-PCR patterns were consistently
isolated from the hospital wastewater during three separate sampling events (at least one
month apart). In other studies multiple genotypes of vanA VREF were isolated from
clinical samples or hospital wastewater using other typing methods such as pulsed-field
gel electrophoresis (PFGE) (Thal et al. 1998; Ko et al. 2005; Kotzamanidis et al. 2009)
and MLST (Ko et al. 2005; Caplin et al. 2008). Differences between genotyping methods
could be responsible for the observance of indistinguishable VREF genotypes in our
study and multiple VREF genotypes in other studies. BOX-PCR typing is not a very
discriminatory typing method as evidenced by a difference in the PFGE patterns of a few
nosocomial VREF isolated in our study (data not shown). VREF isolates demonstrated
high-level vancomycin resistance and intermediate resistance to ampicillin and
ciprofloxacin and the esp gene was detected in some of the isolates; characteristics used
to define the hospital-adapted CC-17 cluster. Determining the relationship of the VREF
strains isolated in this study to VREF isolates belonging to the CC-17 cluster using typing
methods such as MLST or multiple-locus variable-number tandem repeat (VNTR)
analysis (MLVA) can be of epidemiological significance (Willems et al. 2005; Leavis et
al. 2006a; Top et al. 2008).
157
Twenty percent of the vanA VREF isolated in this study were esp positive but all isolates
were negative for hyl. Pantosti et al reported the presence of the esp gene in 94% of
clinical VREF isolates but none of them carried the hyl gene (Stampone et al. 2005).
Some studies have reported the co-occurence of the esp and hyl virulence genes in a
small proportion of clinical VREF strains (Klare et al. 2005; Novais et al. 2005b)
whereas others have found both genes in the majority of the isolates (Rice et al. 2003;
Vankerckhoven et al. 2004). The association of these virulence factors in VREF seems to
vary among populations.
VRE can potentially be disseminated into environmental waters by compromised sewer
systems or improper treatment practices, which poses a health risk for the community
(Harwood et al. 2001; Iversen et al. 2004; Novais et al. 2005a). Horizontal transfer of
virulence determinants and antibiotic resistance genes from VRE to other bacteria in the
environment has been documented in a number of studies (Schaberg and Zervos 1986;
Huycke et al. 1998; Baquero 2004), and the recent finding of an HL-VRE E.
casseliflavus/ gallinarum strain in WA with high frequencies of in vitro conjugal transfer
accentuates this risk (Roberts et al. 2009). Surveillance studies should be initiated to
monitor the presence of HL-VRE in environmental waters and associated matrices.
Experiments to explore their ability to survive and proliferate in environmental matrices
are also warranted based on these and other recent findings.
Acknowledgements
We would like to thank Dr. Lalitagauri Deshpande, JMI Laboratories, North Liberty,
Iowa for performing PFGE analysis of our isolates, Advanced Septic Tanks for providing
septic tank samples and Paul Siddall (Lake Carroll resident) for the use of his property to
158
access Lake Carroll. In addition we would like to thank Miriam Brownell, Asja Korajkic
and Shannon McQuaig (USF Dept. of Integrative Biology) for providing help with
sample collection.
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Yoshikazu, I., A. Ohno, S. Kashitani, M. Iwata, K. Yamaguchi (1996) Identification of
vanB-type vancomycin resistance in Enterococcus gallinarum from Japan. J Infect
Chemother 2, 102-105.
Yoshimoto, E., Konishi, M., Takahashi, K., Majima, T., Ueda, K., Murakawa, K.,
Sakamoto, M., Maeda, K., Mikasa, K., Narita, N., Sano, R., Masutani, T., Ishii, Y. and
Yamaguchi, K. (1999) Enterococcus gallinarum septicemia in a patient with acute
myeloid leukemia. Kansenshogaku Zasshi 73, 1078-1081.
RESEARCH SIGNIFICANCE
Members of the genus Enterococcus are commensals that inhabit the gastrointestinal
tracts of humans and other mammals. The metabolic flexibility of enterococci facilitates
their survival and proliferation in a wide range of environments such as soils, surface
waters, sediments and vegetation associated with surface waters and food (Fujioka 1999;
Eaton and Gasson 2001; Harwood et al. 2004). Accurate identification and classification
of enterococci is important for both environmental and clinical purposes. It is difficult to
identify some Enterococcus species due to their phenotypic similarity (Devriese et al.
1993). Therefore, molecular methods such as genotyping and genetic sequencing are
increasingly employed for identification purposes (Malathum et al. 1998; Harwood et al.
2004).
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BOX-PCR genotyping of enterococci
Genotyping methods have the capacity to process a large number of isolates (high
throughput) and are cost-effective as compared to sequencing studies. Although BOX-
PCR fingerprinting has been used to type enterococci in ecological studies (Brownell et
al. 2007) and MST studies (Brownell et al. 2007; Hassan et al. 2007), till date, no study
has demonstrated that the discrete clusters formed by enterococcal BOX-PCR patterns
are phylogenetically related species/ strains. The purpose of this study was to compare
the ability of BOX-PCR typing to determine genetic relatedness of enterococci with that
of the “gold standard” method, 16S rRNA gene sequencing. Enterococci isolated from
two freshwater sites and one estuarine site were typed using BOX-PCR and their 16S
rRNA genes were sequenced. It was hypothesized that the relationships projected by the
genotypic BOX-PCR dendrograms will be similar to those obtained by phylogenetic
analysis.
As hypothesized, BOX-PCR dendrograms were fairly congruent (77%) with the
phylogenetic tree created by 16S rRNA sequencing. The majority of the strains within the
different Enterococcus species could be clearly differentiated using BOX-PCR typing. In
comparison, the resolution capacity of the 16S rRNA gene is limited when identifying
closely related Enterococcus species such as E. faecium and E. mundtii. The BOXA2R
profile of the genus Lactococcus clustered with the BOXA2R patterns of other
Enterococcus species whereas it emerged as an outgroup in the phylogenetic tree. This
indicates that closely related genera might not be as distinguishable using BOX-PCR
typing. To the best of my knowledge, this is the first study to shed light on the
relationships between enterococcal species and strains using both genotypic (BOX-PCR
173
typing) and phylogenetic (16S rRNA sequencing) data. Although BOX-PCR typing is an
excellent tool for investigating strain diversity, genotypic studies relying solely on BOX-
PCR typing should exercise caution while interpreting phylogenetic relationships
projected by BOX-PCR dendrograms.
Vancomycin resistance in enterococci
Acquisition of antibiotic resistance genes and virulence determinants through conjugative
plasmids and transposons make some Enterococcus species capable of causing disease
(Tenover 2001). Enterococci can be resistant to a wide range of antimicrobial agents such
as β-lactams, aminoglycosides and glycopeptides. Vancomycin is a glycopeptide
antibiotic used to treat infections caused by gram-positive organisms. Vancomycin
resistance in enterococci was first reported in 1986 in Europe and has since been isolated
from clinical and environmental samples worldwide (Leclercq et al. 1988; Uttley et al.
1989). The plasmid-borne vanA and vanB genes confer moderate to high-level
vancomycin resistance in enterococci while low-level resistance (vanC1 and vanC2/3
genes) is chromosomally encoded.
Vancomycin resistant enterococci (VRE) have been detected in environmental waters,
sewage, agricultural runoff, animal feces and feces of healthy human hosts in parts of
Europe (Aarestrup 1995; Devriese et al. 1996). In comparison, VRE are not commonly
isolated from environmental waters or non-hospital related sources in the US (Coque et
al. 1996; Harwood et al. 2001; Roberts et al. 2009). This study investigated the incidence
of VRE in the water, sediment and vegetation samples from two freshwater and one
estuarine site. Vancomycin susceptibility was determined using both culture-based (agar
dilution method) and molecular (multiplex PCR targeting vancomycin resistance genes)
174
methods. VRE were also isolated from municipal and hospital wastewater samples. It was
hypothesized that enterococci isolated from environmental matrices and municipal
wastewater would be susceptible to vancomycin whereas vancomycin resistant
enterococci would be isolated from hospital wastewater.
Isolation of VRE from environmental sources
Low-level VRE (LL-VRE) (< 32 µg ml-1
) isolated from environmental matrices
demonstrated the vanC2/3 genotype and were identified as Enterococcus casseliflavus-
flavescens by 16S rRNA gene sequencing. Although no high-level VRE were isolated
from surface waters, the high proportion of LL-VRE in environmental matrices is a cause
for concern from the public health perspective. LL-VRE have the potential to cause
disease, particularly in immunocompromised and geriatric patients. Treatment of
infections caused by LL-VRE can be problematic because they sometimes demonstrate
vancomycin susceptibility in vitro while being resistant in vivo. Enterococcus gallinarum
(vanC1 genotype) were isolated from municipal wastewater and were a much lower
proportion (1.6%) than the proportion of LL-VRE isolated from environmental matrices
(20% to 60%).
Multi-drug resistant enterococci with indistinguishable BOX-PCR genotypes were
isolated from hospital sewer line samples. These isolates were identified as Enterococcus
faecium possessing the vanA genotype and resistant to high levels of vancomycin (MIC =
512 µg/ml). They also demonstrated intermediate resistance to ampicillin (MIC > 16 µg/
ml) and ciprofloxacin (MIC > 4 µg/ ml). Twenty percent of these isolates carried the
variant esp gene which may facilitate colonization of the urinary tract (Shankar et al.
2001) and biofilm formation (Toledo-Arana et al. 2001; Heikens et al. 2007). The
175
detection of the vanA genotype exclusively and the absence of the vanB genotype in
hospital wastewater is an unusual finding.
This is one of the few studies in the US that has attempted to determine the vancomycin
susceptibility of environmental enterococci. This is also the first study to evaluate VRE in
vegetation samples and to compare VRE proportions in freshwater vs. estuarine waters
and across different matrices. Dissemination of high-level VRE (HL-VRE) into the
groundwater or recreational waters due to compromised sewer lines or improper
treatment practices poses a health risk for the community. The acquisition of virulence
factors and antibiotic resistance genes from HL-VRE by LL-VRE and other indigenous
vancomycin susceptible enterococci accentuates this risk. There is a need for surveillance
studies to monitor the presence of VRE and determine their ability to survive and persist
in environmental matrices.
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ABOUT THE AUTHOR
Bina Nayak received a Bachelor’s degree in Microbiology from the Ramnarain
Ruia College of Arts and Sciences in Mumbai, India in 1995. She enrolled in the
University of South Florida, Tampa, FL in 2002 as a non-thesis Master’s student in the
Department of Biology. Based on her excellent prowess, her committee recommended a
change of program towards the Doctorate in Biology degree in Spring 2004.
During her Ph.D. program, Bina worked on two major projects involving research
in landfill microbiology and environmental enterococci. She also worked as a Research
Assistant on two projects with governmental funding agencies. She presented her
research findings at several Southeastern branch (SEB) and General meetings of the
American Society for Microbiology (ASM). She was awarded the Tharp Summer
Research Fellowship from the USF, Department of Biology. She was employed part-time
as a teaching assistant for the following courses: General Microbiology, Determinative
Bacteriology and Microbial Physiology.