Microbial Population Analysis in Leachate From Simulated

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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.

i

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

ii

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

iii

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

iv

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

vi

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


vanA VREF isolated from hospital wastewater, esp- positive isolates are

underlined. .............................................................................................................. 152

vii

MICROBIAL POPULATION ANALYSIS IN LEACHATE FROM SIMULATED

SOLID WASTE BIOREACTORS

AND


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.

1

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



% of

clone

library

clone ME_dig80_2_15

(DQ680456)

JME29 (FJ435843) Methanobacteriales

Bovine rumen clone

unfaunated-mcrA-13

(AB244709) 3


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


Upland pasture soil

clone SI_18

(DQ994847) 3


Methanosarcina mazei

strain LYC

(AB300782) 3

JME41 (FJ435853) Methanomicrobiales

Cattle manure clone

G4INMC365

(DQ274999) 3

DME1,35 (FJ435855,


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



% 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,


Biogas plant clone

F10RTCR23

(DQ261495) 6

DME23 (FJ435875) Methanomicrobiales Sewer clone

(EF628141) 3

DME24,26

(FJ435876,


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.

References

American Public Health Association (APHA), A.W.W.A., Water Environment

Federation, (1998) Standard methods for the examination of water and wastewater (20th

ed). Clesceri LS, Greenberg AE, Eaton AD, eds. Washington, DC: American Public

Health Association.

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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.







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.

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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.




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.



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.



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.



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


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

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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

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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.


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





LC28S07 Lake Carroll sediment





LC8V08 Lake Carroll vegetation

LC1V07 Lake Carroll vegetation

HR17W07 Hillsborough River water









HR31S07 Hillsborough River sediment









HR9V07 Hillsborough River vegetation










122

Isolate ID Site Source

(matrix)

BD1W07 Ben T Davis beach water






BD11S08 Ben T Davis beach sediment








BD26V08 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


E. silesiacus/ moraviensis/

caccae

E. faecalis

Lactococcus

garvieae

131

100 80 60 40

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

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


Enterococcus species isolated from environmental matrices.

E. casseliflavus

E. hirae

E. casseliflavus/

flavescens

E. hirae

E. mundtii

E. faecium

132

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

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


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.


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


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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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).

172


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.

Documents

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