Thông tin tài liệu
INDICATORS OF MICROBIOLOGICAL QUALITY IN
RECREATIONAL WATERS
HO DANLIANG
(B.Sc. (Hons), NUS)
A THESIS SUBMITTED
FOR THE DEGREE OF MASTER OF SCIENCE
DEPARTMENT OF MICROBIOLOGY
NATIONAL UNIVERSITY OF SINGAPORE
2014
i
Declaration
I hereby declare that the thesis is my original work and it has been written by me in its
entirety. I have duly acknowledged all the sources of information which has been used in the
thesis.
This thesis has also not been subrnitted for any degree in any university previously.
'4
1
Ho Danliang
06 lanuary 2014
Acknowledgements
My heartfelt thanks goes to my supervisor, A/P Lee Yuan Kun for his invaluable guidance and kind
encouragement throughout the course of my research. I thank him especially for challenging me to
think further and deeper about my work, for engaging me in new ideas, for giving me intellectual
freedom to conduct my own research, and on the overall for grooming me to become a better scientist.
I would like to thank our collaborator on the other aspect of our project, Dr Karina Gin from the
Department of Civil and Environmental Engineering, for her interest and support in our research, and
her group members Dr Goh Shin Giek, Dr Saied Rezaeinejad, Mr Fang Haoming, Ms Liang Liang
and Ms Genevieve Gabrielle Vergara for sharing their knowledge, equipment and reagents with us.
My thanks goes to Ms Jennifer Yap and Ms Soh Liting from National Environmental Agency (NEA),
Dr Lim Tien Tze and Ms Chang Siao Yun from Public Utilities Board (PUB) for their interest and
input in this project, as well as for their generous sharing of experiences.
My sincere gratitude goes to my mentor, Dr Koh Yiling, Eileen, for teaching me necessary laboratory
and research skills ('tricks of the trade') in the first few months of my research candidature, and for
providing guidance and advice in the nitty-gritty details of study design, benchwork as well as
bioinformatics analysis. I would also like to thank her for her leadership, friendship and
companionship; without which our laboratory would not be like what it is now.
I am deeply indebted to Mr Low Chin Seng for his assistance and support in the logistic and technical
aspects of conducting my experiments and fieldwork. I would like to thank him for being a fatherfigure to me in a laboratory setting, for his genuine care and concern (albeit under a rough exterior),
and also for unselfishly imparting me his skills and knowledge.
All results described in this thesis were accomplished with the help and support of members of the
'Water Group': Eileen Koh, Henry Teo, Siti Radiah. I would like to thank them for fighting this battle
with me till the end, for helping me accomplish the impossible, and all in all for making my research
stint an enjoyable and memorable experience.
I would like to thank the other members of our laboratory for their wonderful guidance and constant
friendship: the post-docs Dr Shen Hui and Dr Ng Yikai for their patience in answering all my queries,
fellow graduate students Lin Huixin, Daphne Ng, Kelvin Koh, Zhao Ran, Yao Lina, Kenneth Tan for
their comradeship, banter, and all the heated discussions on how my research should proceed, our ex
and current honours students Chen Kang Ting, Huang Ningxin, Regina Ang, Angela Lim, Lee
Huiting, Yow Kok Siang, Ang Sze Chien, Jolene Phua for their precious friendship and for generating
much-needed laughter in an otherwise monotonous laboratory setting.
Lastly but not least, my family members and my husband, Lim Suang, for their unwavering love and
support. This work is dedicated to them.
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Table of contents
DECLARATION ................................................................................................................................... ii
ACKNOWLEDGEMENTS ................................................................................................................... iii
TABLE OF CONTENTS ....................................................................................................................... iv
LIST OF TABLES ............................................................................................................................... viii
LIST OF FIGURES ............................................................................................................................... ix
LIST OF ABBREVIATIONS ................................................................................................................ xi
SUMMARY ......................................................................................................................................... xiv
CHAPTER 1: INTRODUCTION ........................................................................................................... 1
1.1 Overview of recreational water quality..................................................................................... 1
1.2 Recreational water quality assessment...................................................................................... 2
1.3 Limitations of current methods of water quality monitoring ................................................. 3
1.4 Research objectives ..................................................................................................................... 3
1.5 Scope of study .............................................................................................................................. 4
CHAPTER 2: LITERATURE REVIEW ................................................................................................ 6
2.1 Waterborne diseases ................................................................................................................... 6
2.1.1 Overall prevalence and epidemiology of waterborne diseases ......................................... 6
2.1.2 Health criteria of waterborne pathogens ........................................................................... 7
2.1.2.1 Aeromonas hydrophilia..................................................................................... 7
2.1.2.2 Escherichia coli O157....................................................................................... 8
2.1.2.3 Legionella pneumophila.................................................................................... 9
2.1.2.4 Mycobacterium avium complex ...................................................................... 10
2.1.2.5 Pseudomonas aeruginosa ............................................................................... 11
2.1.2.6 Staphylococcus aureus .................................................................................... 12
2.2 Indicator approach ................................................................................................................... 13
2.2.1 Historical background and current status ........................................................................ 13
2.2.2 Attributes of an ideal indicator ....................................................................................... 15
2.2.2.1 Biological attributes ........................................................................................ 15
2.2.2.2 Method attributes ............................................................................................ 16
2.2.3 Limitations of traditional indicators ................................................................................ 16
2.2.4 Alternative indicators ...................................................................................................... 18
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2.2.4.1 Fecal anaerobes ............................................................................................... 18
2.2.4.2 Chemical compounds ...................................................................................... 19
2.2.5 Classical methods of bacterial indicator detection .......................................................... 19
2.2.5.1 Multiple-tube fermentation technique............................................................. 20
2.2.5.2 Membrane filtration technique ........................................................................ 21
2.2.6 Emerging methods in indicator detection ....................................................................... 21
2.2.6.1 Immunological methods.................................................................................. 22
2.2.6.2 Real-time PCR ................................................................................................ 22
2.2.7 Issues with molecular detection of indicators ................................................................. 23
2.2.7.1 Differentiating cell viability ............................................................................ 23
2.2.7.1 Overcoming PCR inhibition ........................................................................... 24
2.3 Direct pathogen monitoring ..................................................................................................... 25
2.3.1 Phylogenetic analysis using 16S ribosomal RNA gene .................................................. 26
2.3.2 DNA microarrays ............................................................................................................ 27
2.3.3 Sequencing studies .......................................................................................................... 28
2.3.3.1 Next-generation sequencing overview ............................................................ 28
2.3.3.2 Application to metagenomics and pathogen detection.................................... 29
2.3.3.3 Bioinformatics ................................................................................................ 30
CHAPTER 3: QUANTIFYING MOLECULAR MARKERS AS INDICATORS OF WATERBORNE
PATHOGENS ................................................................................................................ 31
3.1 Introduction ............................................................................................................................... 31
3.2 Materials and methods ............................................................................................................. 32
3.2.1 Sample collection and processing ................................................................................... 32
3.2.1.1 Study sites ....................................................................................................... 32
3.2.1.2 Sampling procedures ....................................................................................... 35
3.2.1.3 Chemical tests ................................................................................................. 35
3.2.1.4 Membrane filtration ........................................................................................ 35
3.2.1.5 DNA extraction from water samples .............................................................. 36
3.2.2 Real-time polymerase chain reaction (qPCR) analysis ................................................... 36
3.2.2.1 Reference strains used in standards ................................................................ 36
3.2.2.2 Development of plasmid standards ................................................................. 37
3.2.2.3 Validation of primer set using end-point PCR ................................................ 38
3.2.2.4 Validation of primer set using real-time PCR ................................................. 41
3.2.2.5 Real-time PCR of samples .............................................................................. 42
3.2.2.6 Data analysis ................................................................................................... 43
3.2.3 Differentiation between live/dead bacteria ..................................................................... 43
3.2.3.1 Culture strain................................................................................................... 43
3.2.3.2 Preparation of dead cells ................................................................................. 43
3.2.3.3 Comparing PMA and EMA pre-treatment ...................................................... 43
3.2.3.4 Determining and validating optimum PMA concentration ............................. 44
3.2.4 PCR inhibition ................................................................................................................ 45
3.2.5 Field tests ........................................................................................................................ 45
3.2.5.1 Pathogen detection .......................................................................................... 45
3.2.5.2 Evaluating pathogen-indicator relationship .................................................... 46
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3.3 Results ........................................................................................................................................ 46
3.3.1 Comparing the effectiveness of PMA and EMA pre-treatment ...................................... 46
3.3.2 PMA optimisation ........................................................................................................... 47
3.3.3 Validation of PMA pre-treatment in environmental water matrix .................................. 49
3.3.4 PCR inhibition ................................................................................................................ 51
3.3.5 Detection of selected potential pathogens in environmental samples ............................. 53
3.3.6 Examination of indicator-pathogen relationship in single-point studies......................... 54
3.4 Discussion................................................................................................................................... 57
3.4.1 Development of optimisation procedures for qPCR bacterial marker quantification ..... 57
3.4.2 Indicator-pathogen occurrence and relationships in recreational water samples ............ 60
3.5 Conclusion ................................................................................................................................. 63
CHAPTER 4: OCCURRENCE, DISTRIBUTION AND DECAY OF SELECTED MOLECULAR
MARKERS .................................................................................................................... 64
4.1 Introduction ............................................................................................................................... 64
4.2 Materials and methods ............................................................................................................. 65
4.2.1 Study site description ...................................................................................................... 65
4.2.2 Sample collection and processing ................................................................................... 65
4.2.2.1 Sampling for spatial variability ...................................................................... 66
4.2.2.2 Intensive sampling ......................................................................................... 66
4.2.3 Culture analysis............................................................................................................... 67
4.2.4 Laboratory microcosm study .......................................................................................... 72
4.2.5 Statistical procedures ...................................................................................................... 73
4.3 Results ........................................................................................................................................ 73
4.3.1 Determination of spatial variability of bacterial markers ............................................... 73
4.3.2 Determination of temporal variability of bacterial markers ............................................ 75
4.3.3 Marker decay kinetics ..................................................................................................... 80
4.4 Discussion................................................................................................................................... 83
4.4.1 Spatial and temporal variability in bacterial marker distribution.................................... 83
4.4.2 Bacterial marker survival and persistence ...................................................................... 84
4.5 Conclusion ................................................................................................................................. 86
CHAPTER 5: DIRECT PATHOGEN DETECTION AND DISCOVERY USING SEQUENCING
METHODS .................................................................................................................... 88
5.1 Introduction ............................................................................................................................... 88
5.2 Materials and methods ............................................................................................................. 89
5.2.1 Establishment of 16S rRNA clone library ...................................................................... 89
5.2.1.1 PCR amplification........................................................................................... 90
5.2.1.2 DNA ligation, transformation and plasmid extraction .................................... 91
5.2.1.3 Verification of insert ....................................................................................... 92
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5.2.1.4 Restriction fragment length polymorphism (RFLP) screening ....................... 92
5.2.1.5 Bioinformatics analysis ................................................................................... 93
5.2.2 454-sequencing ............................................................................................................... 93
5.2.2.1 PCR amplification and purification ................................................................ 94
5.2.2.2 Sample clean-up, quantification and quality check ........................................ 94
5.2.2.3 Sequence analysis and phylogenetics assignment .......................................... 95
5.2.2.4 Sequence identity calculation ......................................................................... 95
5.3 Results ........................................................................................................................................ 97
5.3.1 Phylogenetic characterisation using clone libraries ........................................................ 97
5.3.2 Taxonomic assignment of sequences from 454-sequencing ......................................... 103
5.3.3 Within-sample diversity analysis .................................................................................. 106
5.3.4 Comparison against pathogen database ........................................................................ 109
5.4 Discussion................................................................................................................................. 111
5.4.1 Overall bacterial community structure .......................................................................... 111
5.4.2 Pathogen discovery and detection ................................................................................. 113
5.5 Conclusion ............................................................................................................................... 115
CHAPTER 6: CONCLUSIONS ......................................................................................................... 116
REFERENCES ................................................................................................................................... 119
APPENDIX I ...................................................................................................................................... 132
APPENDIX II ..................................................................................................................................... 133
APPENDIX III .................................................................................................................................... 135
APPENDIX IV.................................................................................................................................... 137
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List of tables
Table 3.1
Location characteristics of sampled recreational water facilities
Table 3.2
List of gene-targeted primers used in this study
Table 3.3
Validation of amplification efficiency and lowest limit of detection of primer set
Table 3.4
Mean, standard deviation, minimum and maximum qPCR counts obtained when BJ,
IMM and VC samples were diluted 1X, 10X and 100X.
Table 3.5
Regression analyses and correlations of pathogens and indicators
Table 4.1
Summary of the type and purpose of each culture medium used to culture viable
bacteria from collected environmental water samples
Table 4.2
Regression analyses and correlations of indicator and potential pathogens
Table 4.3
Variable loadings on the first five rotated principle components
Table 5.1
Human pathogenic bacteria, disease and accession number of reference sequences
Table 5.2
Identity and amount of sequences corresponding to potentially pathogenic bacteria
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List of figures
Figure 3.1
Geographical locations of sampling sites
Figure 3.2
Individual site images - (A) BJ, (B) CS, (C) IMM, (D) SB, (E) SC, (F) VC
Figure 3.3
Example of gel used to check PCR product size and primer specificity
Figure 3.4
Comparison between PMA and EMA treatment of viable and heat-killed E. coli
mixtures
Figure 3.5
Chart showing the relationship between different PMA concentrations and
enumeration of E. coli spiked in pure water
Figure 3.6
Chart showing the relationship between different PMA concentrations and
enumeration of E. coli spiked in environmental water matrix
Figure 3.7
Chart showing the final counts (in CFU/100ml) obtained from qPCR results for
different dilutions of samples collected from three different locations
Figure 3.8
Chart showing pathogen distribution within sampled interactive fountains
Figure 3.9
Chart showing distribution of (A) fecal markers and (B) pathogens within sampled
interactive fountains
Figure 4.1
Illustrative diagram of fountain water recycling system
Figure 4.2
Hierarchical chart depicting processing workflow for every liter of water sample
Figure 4.3
Bacteria counts for (A) total heterotrophic plate count, (B) total/fecal coliforms, (C)
Pseudomonas and (D) Streptococcus and fecal enterococci as obtained by culture
Figure 4.4
Bacteria counts for (A) total bacterial load, (B) Enterobacteriaceae, (C) Aeromonas,
(D) Pseudomonas, (E) Legionella and (F) Mycobacterium as obtained by qPCR
Figure 4.5
Temporal patterns of distribution for bacteria markers enumerated using (A) culture
and (B) qPCR
Figure 4.6
Scree plot showing a sharp drop after PC1, indicating that most of the variance in
the data had been explained by the first component
Figure 4.7
Survival profiles of selected pathogens (A-D) and indicators (E-F)
Figure 5.1
Flowchart summarizing the steps involved in the establishment of each clone library
Figure 5.2
Illustration of the 16S rRNA gene
Figure 5.3
Example of different RFLP patterns obtained from double digest of 12 clones within
the Science Centre (SC) clone library
Figure 5.4
Phylogenetic tree of sequences obtained from water samples collected from the
following locations: (A) Bugis Junction, (B) IMM, (C), Science Centre and (D)
VivoCity
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Figure 5.5
Proportions of unique sequences found in each taxon
Figure 5.6
Taxonomic classification of bacteria at the phylum/class level within (A) Bugis
Junction, (B) IMM, (C) Science Centre and (D) VivoCity
Figure 5.7
Collector's curves obtained for samples from BJ, IMM, SC and VC using the (A)
Chao1 and (B) Inverse Simpson diversity estimator
Figure 5.8
Rarefaction analysis of observed richness in bacterial communities sampled from BJ,
IMM, SC and VC
Figure 5.9
Heatmap comparing relative abundance of potentially pathogenic sequences that
occurred in the samples with red to green continuum representing high to low
abundance respectively
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List of abbreviations
A. hydrophilia
Aeromonas hydrophilia
ANOVA
Analysis of Variance
APHA
American Public Health Association
ATCC
American Type Culture Collection
B. thetaiotaomicron
Bacteroidetes thetaiotaomicron
BCYE
Buffered Charcoal Yeast Extract
BD
Becton, Dickinson and Company
BJ
Bugis Junction
BLAST
Basic Local Alignment Search Tool
bp
Basepair
CFU
Colony-forming units
CS
City Square
CT
Cycle threshold
DNA
Deoxyribonucleic acid
E. coli
Escherichia coli
EC broth
Escherichia coli broth
ELISA
Enzyme-linked immunosorbent assay
EMA
Ethidium monoazide
FA/PCA
Factor analysis/Principal components analysis
FIB
Fecal-indicator bacteria
Gb
Gigabyte
GC-MS
Gas chromatography-mass spectrometry
GI
Gastro-intestinal
GM
Geometric mean
GVPC
Glycine, Vancomycin, Polymyxin B sulphate, Cyclohexamide
HUS
Hemolytic-uremic syndrome
IEA
Immuno-enzyme assay
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IFA
Immunofluorescence assay
k
Decay coefficient
kB
Kilobase
L. pneumophila
Legionella pneumophila
LB
Luria Bertani
LB-A
LB-ampicillin
LOD
Limit of detection
M. avium
Mycobacterium avium
M. smithii
Methanobrevibacter smithii
MAC
Mycobacterium avium complex
MF
Membrane filtration
ML
Maximum Likelihood
MPN
Most probable number
MRSA
Methicillin-resistant Staphylococcus aureus
MTF
Multiple-tube fermentation
MUG
4-methylumbelliferyl-β-D-glucuronide
NEA
National Environmental Agency
NGS
Next-generation sequencing
NRC
National Research Council
NTAC
National Technical Advisory Committee
NTC
Non-template control
P. aeruginosa
Pseudomonas aeruginosa
P. alcaligenes
Pseudomonas alcaligenes
PCR
Polymerase chain reaction
PMA
Propidium monoazide
PUB
Public Utilities Board
PVC
Polyvinyl chloride
qPCR
Real-time polymerase chain reaction
xii
RDP
Ribosomal Database Project
RNA
Ribonucleic acid
rRNA
Ribosomal RNA
S. aureus
Staphylococcus aureus
SB
Sembawang Shopping Centre
SC
Science Centre Singapore
SMEWW
Standard Methods for the Examination of Water and Wastewater
Kendall's tau correlation coefficient
TDS
Total dissolved solids
U.K.
United Kingdom
U.S.A.
United States of America
USCDC
United States Centers for Disease Control and Prevention
USEPA
United States Environmental Protection Agency
USPHS
United States Public Health Service
UV
Ultraviolet
VBNC
Viable-but-non-culturable
VC
VivoCity
w/v
Weight per volume
WBDOSS
Waterborne Disease and Outbreak Surveillance System
WHO
World Health Organisation
xiii
Summary
Worldwide, recreational water quality criteria are based on the measurement of fecal indicator
bacteria using culture techniques. However various limitations of using traditional fecal indicators in
monitoring bathing water quality and predicting disease outbreak exist; these include low levels of
correlation with pathogens, low detection sensitivity, ability to multiply outside the water column, as
well as inability to predict pathogens of non-fecal sources, thus raising questions on the applicability
of current measurement criteria on recreational water facilities.
In the present study, molecular techniques were evaluated for the ability to detect and predict
pathogen occurrence. Optimisation procedures including development of standards, sensitivity and
specificity testing, determination of LOD and amplification efficiency for detection of selected
indicators and pathogens using qPCR were performed. The differentiation of viable and dead bacteria
using EMA and PMA pre-treatment, as well as the extent of PCR inhibition occurring within
environmental samples was also evaluated. In comparison to EMA, PMA pre-treatment on samples
spiked in pure water performed better with maximum dead cell exclusion and minimum live cell
signal suppression, and 30µM was determined to be the optimum concentration. PCR inhibition was
found to be present in one out of the three studied sites, highlighting the necessity of conducting such
verification tests prior to any molecular analyses of environmental samples.
The optimised qPCR assays were used to analyse indicator and pathogen count and distribution within
six recreational water facilities in Singapore using a point-sampling approach. A more intensive
sampling for spatial and temporal variability of bacterial markers were also conducted within a single
recreational water facility. Using correlation analysis, linear regression and FA/PCA, it was
discovered that Enterobacteriaceae and M. smithii were good predictors of pathogen presence in
study sites subjected to point-sampling, but none of the measured indicators predicted pathogens well
when temporal variability was considered. It was found that the bacterial indicators and pathogenic
markers possessed similar transport characteristics but markedly different decay rates under the same
conditions, possibly explaining for the observed lack of correlation.
xiv
The inability to establish any indicator-pathogen relationship necessitated the use of direct pathogen
detection as an alternative method to water quality assessment. Direct pathogen detection and
discovery was performed using the traditional Sanger sequencing approach to construct clone libraries,
and 454-sequencing that allows for alpha and beta-diversity analyses as well as comparison against a
database of known pathogens. Both methods revealed the presence of consistently dominant taxa
which included Alpha, Beta and Gamma-proteobacteria, as well as Bacteroidetes. In addition, 454sequencing identified a number of rare phyla which included Acidobacteria, Chlamydia, Clostridia,
Delta-proteobacteria and Verrucomicrobiae. On the other hand, the distribution of pathogenic genera
were markedly different across samples, and several genera that were not known to be associated with
an aqueous environment (Neisseria, Streptococcus, Chlamydia, Rickettsia) were discovered.
Furthermore, a few potentially pathogenic genera (Pseudomonas and Mycobacterium) were found to
persist in large amounts within all samples. The methods described in this study have been used to
characterise the bacterial and pathogenic community present within recreational water and could also
potentially be used to screen for indicators that better correlate with water-borne pathogens.
xv
Chapter 1: Introduction
1.1 Overview of recreational water quality
Worldwide, there is an increase in the popularity of recreational activities that involve contact
with water. In Singapore, government initiatives have resulted in a redesign of our waterways to
incorporate various lifestyle attractions including kayaking and windsurfing activities (PUB,
2013). There is also a trend in the increasing use of indoor water recreation such as commercial
spa pools, Jacuzzi and interactive water playgrounds. Such frequent contact with water can
expose individuals to health hazards such as viral, bacterial or protozoan pathogenic microorganisms within the water environment, and increases the risk of spreading water-borne diseases.
Epidemiological reports have demonstrated a link between adverse health effects and prior
immersion in poor quality recreational waters (Craun et al., 2005; USCDC, 2008; USCDC, 2011),
and these reports are mostly based on acute, self-limiting diseases such as gastro-intestinal (GI)
illnesses, which are transmitted via the fecal-oral route. On the other hand, many studies have
shown that pathogens associated with more severe outcomes or secondary health problems may
also be spread by the use of contaminated water (Bartram and Rees, 2000; Pond, 2005). These
pathogens, which include Legionella, Mycobacterium avium complex, Vibrio vulnificus,
Naegeria fowleri, Hepatitis A and Hepatitis E virus, have been isolated from surface waters and
their role in pathogenesis are well-documented (Bartram and Rees, 2000; Pond, 2005). However
due to the possibilities of other transmission routes for the pathogens-in-question,
epidemiological research demonstrating a causative link between recreational water usage and
contracting such diseases have been scarce. It is equally plausible that other unknown pathogens
could be transmitted by use of recreational water and this association has not been investigated.
There is much that is not understood about the microbial risks present in recreational water
settings. Therefore, from a research standpoint, it is important to investigate what is the nature of
1
the microbial community, including pathogens, that is present in our recreational waters. It is also
integral to develop accurate, reliable and scientifically defensible methods that assess water
quality, in order to protect public health.
1.2 Recreational water quality assessment
The risks of developing GI illnesses from fecal-polluted recreational waters are well-studied and
established; as such water quality assessment efforts have been focused on fecal contamination
(Craun et al., 2005; USCDC, 2008; USCDC, 2011). However due to the low concentration of
pathogens, direct detection is difficult to perform, hence bacteria that are correlated to pathogen
levels have traditionally been used as surrogate measures. Fecal indicator bacteria (FIB) such as
fecal coliforms, Enterococcus and Escherichia coli are often used for water quality assessment.
Being commonly found in the lower intestines of humans and other warm-blooded mammals, FIB
are thought to indicate the presence of fecal material, thus co-occur with and signal the presence
of human pathogens. The extensive use of FIB to measure water quality is due to well-established
and relatively inexpensive detection and enumeration methods, as well as several epidemiological
studies supporting the use of FIB as predictors for gastro-intestinal illnesses among swimmers
(Wade et al., 2003; Shah et al., 2011).
First developed for drinking water standards, the use of FIB was later expanded and adopted for
ambient and recreational water criteria. Currently, the United States Enviromental Protection
Agency (USEPA)‟s recommended recreational water quality criteria, which is adopted by 95% of
states within the United States of America (U.S.A.), suggests that within a 30-day interval, the
rate of GI disease attributable to swimming should be 36 cases per 1000 recreators if the monthly
geometric mean (GM) of Enterococcus is less than 35 colony-forming units (CFU)/100ml, or if
the GM of E. coli is less than 126 CFU/100ml (USEPA, 2012). In Singapore, the National
Environmental Agency (NEA) adopts guidelines from the World Health Organisation (WHO)
2
standards (2003) stating that „95% of the time, the Enterococcus counts should be less than or
equal to 200 counts per 100ml of water‟ (NEA, 2013) .
1.3 Limitations of current methods of water quality monitoring
Despite the widespread usage of fecal indicator bacteria (FIB) in water quality monitoring, the
limitations of using the existing bacterial indicator approach to assess water quality are becoming
increasingly evident (Section 2.2.3 and 2.2.4). There is little evidence to suggest that fecal
contamination is the major source of pathogens in recreational water settings, however the actual
pathogen diversity present and the ability of FIB to predict them has not been well studied.
Furthermore, even in water bodies where fecal contamination is known to occur, research has
shown that the strength of indicator/pathogen relationship is weak, raising questions on their
suitability as predictors of disease (Griffin et al., 1999, Nobel and Fuhrman, 2001).
Current monitoring methods are also based on traditional culturing techniques which are less
accurate, sensitive and more time consuming as compared to molecular methods. Several
methods have been proposed, including enzyme-linked immunosorbent assay (ELISA),
microarrays and real-time polymerase chain reaction (qPCR), of which the latter is the most
promising. However qPCR procedures for water quality assessment are currently unstandardised;
they also suffer from several limitations: 1) the inability to differentiate live and dead bacteria and
2) PCR methods tend to be negatively affected by presence of inhibitor compounds. As such is
unable to replace culture techniques as the gold standard yet.
1.4 Research objectives
The suitability of FIB as markers of water quality assessment has been questioned. Furthermore,
there is a paucity of studies performed on how applicable current methods of water quality
assessment are in determining microbial risks of recreational waters from aquatic facilities in
3
Singapore. Despite widespread hype of the potential of using molecular techniques in water
quality monitoring, they have not been fully optimised for use in recreational water settings.
Hence, the objectives of this study are as follows:
1) To validate and optimize real-time quantitative detection methods for the detection of
FIB in environmental waters,
2) To determine correlation and predictive powers of FIB against pathogens in
environmental waters in spatial and temporal studies, and hence determine the
applicability of current indicators in Singapore recreational waters, and
3) To develop alternative methods of assessing recreational water quality, should the current
indicators be found to be unsuitable.
1.5 Scope of study
Because the field of recreational water microbiology encompasses various aspects, prior to
conducting this study, it is necessary to specify the scope of research based on the conducted
literature review (Chapter 2). Unlike coastal waters and swimming pool bodies, interactive water
fountains and spa pools currently do not possess specific microbiological quality guidelines. Due
to logistic constraints it is difficult to obtain access to spa facilities for sampling purposes. As
such this study will focus on interactive water fountains (splash pads or water play areas) as a
subset of all recreational water facilities in Singapore. Furthermore, there is a general consensus
that alternative pathways of pathogen transmission (other than fecal-oral pathway) occur
frequently in the recreational water context, and that there is little research conducted on them
(NRC, 2004; Pond, 2005). As such this study will focus more on pathogens endemic to the water
environment and those spread via aerosols or skin contact. These pathogens are mostly of
bacterial origin, such as Legionella spp. and Pseudomonas aeruginosa. Based on these set of
4
criteria, this study will attempt to characterize only the bacterial population found in interactive
water fountains.
5
Chapter 2: Literature Review
2.1
Waterborne diseases
Water is essential to life. However, water also serves as a medium for disease transmission,
resulting in undesirable health outcomes in humans. This section briefly reports on the prevalence
of waterborne diseases worldwide, as well as disease epidemiology in the United States.
Subsequently, it will discuss the health criteria of selected waterborne bacterial pathogens
including their characteristics and ecology, health effects, transmission routes and evidence for
association with recreational water supplies.
2.1.1 Overall prevalence and epidemiology of waterborne diseases
At the global scale, water-based infectious outcomes have a large impact on public health.
Worldwide, at least 4.0% of deaths and 5.7% of disease burden worldwide is attributable to
waterborne diseases which included diarrheal diseases, schistosomiasis and trachoma (Pruss et al.,
2002; Kosek et al., 2003; Lewin et al., 2007). In total 1415 microorganisms are reported to be
pathogenic, out of which 348 are known to be water-associated, causing 115 infectious diseases
(Taylor et al., 2001).
In the United States, disease outbreak data is collected by the Waterborne Disease and Outbreak
Surveillance System (WBDOSS), which is a national surveillance system that monitors the nature,
scope and health effects of waterborne disease outbreaks in coastal and inland recreational waters,
including treated (spas, pools, interactive fountains) and untreated (reservoir) waters. In 2007 and
2008, a total of 134 outbreaks associated with recreational water was reported, resulting in at least
13,699 cases of illnesses, in which 40% of outbreaks comprised of dermatological illnesses, acute
respiratory conditions as well as combined illnesses. The main etiological agents in these cases
were of bacterial origin, which included Legionella spp. and P. aeruginosa (USCDC, 2011).
6
2.1.2 Health criteria of waterborne bacterial pathogens
2.1.2.1 Aeromonas hydrophilia
Aeromonas spp. are gram negative rods belonging to the family Enterobacteriaceae which can
cause a variety of gastro-intestinal and extra-intestinal diseases. Aeromonas hydrophilia is one of
the most well-characterised species out of this group and is the most medically-relevant, as it has
been isolated from wounds and can produce heat-labile enterotoxins, which result in the
production of haemolysins and cytotoxins (Burke et al., 1981). Aeromonas spp. are ubiquitous in
aquatic environments including surface waters, drinking and recreational waters. They grow over
a wide temperature range with optimal growth at 30°C, and can remain viable for extended
periods of time (Warburton, 2000). They may colonise water supplies as well as form biofilms
which are resistant to disinfection (Holmes et al., 1996; Bomo et al., 2004).
The primary health effect of Aeromonas infection is acute diarrheal disease; however wound
infections may also occur following traumatic injury in an aqueous environment. Serious
complications include meningitis, septicemia and pneumonia, and they have also been implicated
in traveler's diarrhoea. Both healthy adults as well as immunocompromised individuals are
susceptible to infection (Janda and Abbott, 1996; Joseph, 1996).
There are few documented outbreaks of Aeromonas infections, out of which they are mostly
associated with untreated drinking water supplies (Ghaenem et al., 1993). However superficial
infections resulting from environmental exposure to recreational water are relatively common and
has been described extensively in medical literature. The health risk of this pathogen is
controversial: on one hand, there has been no reported outbreaks associated with drinking water
supplies, yet the presence of several virulence factors resulting in the potential to cause disease in
susceptible populations should warrant concern (Pavlov et al., 2004). As such it has been placed
7
in the USEPA Candidate Contaminant List and is subjected to further research, evaluation and
risk assessment (USEPA, 1998).
2.1.2.2 Escherichia coli O157
Belonging to the family Enterobacteriaceae, pathogenic Escherichia coli has been found to be
responsible for a variety of GI ailments, of which those caused by the O157 serogroup presents
the most severe symptoms. E. coli O157 is enteric and mainly found in the intestines of humans
and other mammals. Tolerance to low pH and cold temperatures aids the transmission of this
bacterium. Furthermore, it has several virulence factors that contribute to pathogenesis: the
production of Shiga-like toxins, the production of a haemolysin that enhances the toxicity of the
Shiga toxins, as well as the ability to attach to and colonise intestinal surfaces (Kuntz and Kuntz,
1999).
The primary disease symptom is abdominal cramping and bloody diarrhoea, while the most
severe outcome is hemolytic-uremic symptom (HUS), which develops in around 2-8% of cases
and is characterised by hemolytic anemia, acute kidney failure and low platelet counts (Rowe et
al., 1991; Lansbury and Ludlam, 1997). Secondary health outcomes of patients suffering from
HUS include abnormal kidney function requiring long-term dialysis, high blood pressure,
seizures, blindness and paralysis (Sieglar et al., 1994).
E. coli O157 is mainly transmitted by contaminated foods and water, as well as direct contact
with animals and person-to-person spread. It is strongly associated with recreational water and
there is much evidence linking outbreaks of E. coli O157 to bathing in contaminated waters,
particularly swimming in freshwater ponds or wading pools (Brewster et al., 1994, Keene et al.,
1994; Wang and Doyle, 1998; USCDC, 1998). These studies have identified the development of
HUS in a subset of patients and have highlighted the severe clinical outcomes of infection by E.
8
coli O157. In most cases, outbreaks resulted from the use of pools that were not adequately
chlorinated.
2.1.2.3 Legionella pneumophila
The Legionella group of organisms belong to family Legionellaceae and genus Legionella, of
which Legionella pneumophila is the organism most frequently associated with pathogical
outcome, namely Legionnaire's disease. Various other Legionella species including L. anisa, L.
micdadei and L. wadsworthii are also associated with disease (Hunter, 1998, Bartram et al., 2007).
Legionella spp. occur in natural environments such as rivers and streams and have also been
isolated from sewage-contaminated waters. Legionella are gram-negative rods that require Lcysteine and iron salts for growth in vitro. They are highly resistant to environmental factors and
have been shown to multiply at high temperatures up to 60°C, as well as persist for long periods
of time (up to 10 years) in water systems (Lawrence et al., 1999). They are intracellular
pathogens and require the presence of other bacteria or protozoa as hosts for survival and growth.
L. pneumophila causes Legionnaire's disease, of which common symptoms include high fever,
severe headache, muscle and abdominal pain, diarrhoea and changes in mental status. The major
cause of death is respiratory failure (Roig et al., 1993). Secondary health outcomes are numerous
and include pulmonary complications, renal failure, cerebral abscesses, arthritis and seizures
(Loveridge, 1981; Mamane et al, 1983; Smeal et al., 1985; Peliowski and Finer, 1986; Anderson
and Sogaard, 1987).
Legionnaire's disease is mostly community-acquired through direct inhalation of aerosols
(Evenson, 1998). The infectivity dose is believed to be small as contaminated aerosols generated
from a considerable distance were shown to be sufficient for infection. Susceptible populations
include the elderly, people who are immuno-compromised, people with damaged respiratory
tracts e.g. those with lung problems or smokers, as well as people with chronic illnesses such as
9
renal disease. It is thought to be one of the most frequent causes of community-acquired
pneumonia (Thi Minh Chau and Muller, 1983) and outbreaks are likely to be under-reported due
to misdiagnosis (Helms et al., 1980). Legionella is most strongly associated with spa waters such
as hot tubs and saunas (Bornstein et al., 1989; Kuroki et al., 1998) but have also been isolated
from fountains and wading pools (USCDC, 2011). In Singapore, Legionella spp. has been
isolated from cooling towers and interactive water fountains (Lam et al., 2011), however there is
little epidemiological evidence linking reported cases of Legionnaire's disease to these
environmental isolates.
2.1.2.4 Mycobacterium avium complex
Mycobacterium belong to the order Actinomycetales, family Mycobacteriaceae. Several species
are both associated with water and cause disease in humans and animals. The organism of
greatest public health concern is the Mycobacterium avium complex (MAC), which comprises of
M. avium and M. intracellulare (Pedley et al., 2004). Mycobacterium can be found in various
different environments including surface waters. MAC is highly resistant to antibiotics, heavy
metals and disinfectants due to the presence of an impermeable cell wall, which allows it to grow
at temperatures up to 45°C and at low oxygen concentrations (Rastogi et al., 1981; Miyamoto et
al., 2000; Taylor et al., 2000).
Primary infections caused by MAC include cough, production of sputum and chest pain. Severity
of infections depends on the exposed persons' level of immunity: healthy adults suffer from mild
cases of pneumonia while immunocompromised individuals could present with life-threatening
respiratory illnesses. Besides pulmonary infections, Mycobacterium has also been associated with
infections of the middle ear (Trupiano and Prayson, 2001). Lastly there is also research
suggesting that MAC is one of the factors known to trigger Crohn's disease, a type of
autoimmune disease primarily affecting the GI tract (Pickles et al., 2001).
10
MAC is primarily transmitted by environmental exposure such as inhalation of aerosols or
ingestion of contaminated water. It is found endemic in natural waters as well as man-made
facilities such as swimming pools, spas and footbaths. There is strong evidence linking exposure
to environmental MAC to development of skin and soft tissue infections, and even pneumonia
(Collins et al., 1984; Shelton et al., 1999). In most cases, disease outbreaks can be attributed to a
high bather load, lack of disinfection, or inadequate maintenance of filters and pipes.
2.1.2.5 Pseudomonas aeruginosa
The pseudomonads are a group of Gram-negative, curved rods classified into the Gammaproteobacterium phylum, genus Pseudomonas. They can grow in a wide range of habitats and are
highly ubiquitous in the environment, contributing to food spoilage, degradation of petroleum and
other materials, as well as plant and animal diseases. P. aeruginosa is of greatest medical
importance as it is commonly associated with human illnesses and is potentially lethal.
Pseudomonas are highly persistent and can survive for prolonged periods in filtered freshwater,
possibly due to biofilm formation as well as the ability to grow at very low nutrient levels (van
der Kooij et al., 1982; de Vicente et al., 1988). They are also reported to be strongly resistant to
disinfectants such as quaternary ammonium compounds, phenolics and hypochlorite (Hollyoak et
al., 1995).
P. aeruginosa, ranked as the 5th most frequent cause of nosocomial infections, is by itself
responsible for 10% of nosocomial pneumonia in hospital settings (Dembry et al., 1998), and has
been documented to cause cerebrospinal infections and bacterimia (Fick, 1992). In healthy
individuals, P. aeruginosa frequently causes ear and skin infections, and has also been linked to
infections of the urinary-tract and gastro-intestinal tract. P. aeruginosa-induced infections are
notoriously difficult to treat, associated with peculiar clinical aspects, may result in serious
11
complications and have a high morbidity rate (Pollack, 1995; Shigemura et al., 2006; Goldman et
al., 2008).
P. aeruginosa is naturally found in surface waters; however numbers have been reported to be
increased in water contaminated by fecal material, stormwater discharge and high bather load,
linking its presence to human activities. Other reported sources of P. aeruginosa is urban run-off
and water leeching from soil (Reali and Rosati, 1994; Geldreich, 1996). The presence of P.
aeruginosa in recreational water bodies such as spas, whirlpools and hydrotherapy pools is
epidemiologically linked to outbreaks of outer ear and skin diseases (Ratnam et al., 1986;
Aspinall and Graham, 1989; Craun et al., 2005). Factors include warm temperatures, water
aeration and high bather load, as well as inadequate disinfection (Brett and du Vivier, 1985;
Ratnam et al., 1986). Strong adherance of P. aeruginosa to polyvinyl chloride (PVC) piping and
filters through biofilm formation makes removal difficult.
2.1.2.6 Staphylococcus aureus
Staphylococcus are a group of gram-positive, cocci-shaped bacteria that are usually arranged in
grape-like clusters. They belong to the Gamma-proteobacterium phylum, genus Staphylococcus,
with at least 15 different species within the genus. Within the group, S. aureus, S. epidermidis and
S. saprophyticus are associated with human diseases; however S. aureus is the most medically
significant due to potential lethality in clinical settings (WHO, 2013). It is capable of growing
aerobically or anaerobically, and is able to produce enterotoxins which are heat-stable and
resistant to degrading enzymes, and can cause disease when ingested (Le Loir et al., 2003).
Staphylococcus aureus infections include that of the skin, GI disease, septicaemia, endocarditis,
pneumonia and bacterimia (Lowry, 1998). One of the most well-characterised clinical
complication is toxic-shock syndrome, which manifests as a persistently high fever, low blood
pressure and confusion, and is rapidly followed by coma and multiple organ failure (Lowry,
12
1998). The emergence of methicillin-resistant strains of Staphylococcus aureus (MRSA) further
complicates treatment.
Staphylococcus have a short duration of persistence in the environment and has been reported to
be easily controlled by conventional disinfection procedures. On the other hand, being commonly
found as part of the normal microflora of human skin, they are easily released into water
environments and thus transmitted to susceptible populations. Substantial quantities of S. aureus
and MRSA have been reported to be shedded by humans in bathing waters (Plano et al., 2011),
and outbreaks of S. aureus infections have been associated with exposure to marine waters
(Charoenca and Fujioka, 1993, 1995).
2.2
Indicator approach
As awareness increased about disease transmission by water, there was a growing realisation of
the importance of monitoring water supplies in order to protect public health. This section
describes the history of recreational water monitoring practices including an explanation of the
indicator approach and the guidelines that Singapore currently practices, followed by a
description of the attributes of an ideal indicator, as well as limitations of traditional indicators,
their detection methods and emerging trends in indicator detection and method development.
2.2.1 Historical background and current status
The earliest outbreak that was linked to disease was documented by a physician, John Snow, who
determined that drinking sewage-polluted water led to cholera in the 1800s (Paneth, 2004).
Following his work, early efforts by researchers were focused on the prevention of diseases
transmitted by the fecal-oral route. Because methods used for direct detection and monitoring of
pathogens were not sensitive and hence not protective of public health, an index used to discern
fecal-contaminated water based on E. coli was developed by Theobold Smith (Smith, 1891).
13
Subsequently the American Public Health Association (APHA) standardized methods of E. coli
detection and published the results in the Standard Methods for the Examination of Water and
Wastewater (SMEWW) (Wolfe, 1972). Following studies showing that E. coli displayed higher
persistence and a strong positive correlation to several pathogenic organisms, E. coli as an
indicator for fecal-contaminated waters came into widespread use (Kerr and Butterfield, 1943;
Wattie and Butterfield, 1944). However, ongoing concerns about the suitability of E. coli led to
the development of alternative indicators, notably Enterococcus and Clostridium perfringens
(NRC, 2004).
Concerns over the quality of recreational water followed the development of drinking water
standards. The United States Public Health Service (USPHS) was the first governmental agency
to study the association between usage of recreational water and enteric disease transmission, and
unequivocally demonstrated a link between an epidemic of typhoid fever to “bathing in polluted
waters” (Stokes, 1927a, b). Guidelines for recreational water usage first appeared in the 1950s;
however different jurisdictions employed standards differing in terms of both the type of indicator
(total coliform, Enterococcus or E. coli) as well as statistical reporting (geometric mean or
median) (Garber, 1956). In an attempt to standardize recreational water criteria, the National
Technical Advisory Committee (NTAC) proposed a series of guidelines which included the use
of fecal coliforms in 1968. In 1986, the USEPA adopted and modified the proposed
microbiological criteria, to include Enterococci and E. coli. These guidelines remain in use today
and have only been revised slightly in the latest edition of recreational water quality criteria
(USEPA, 2012). Based on the criteria promulgated by USEPA and other epidemiological studies,
WHO also published a set of standards for coastal and fresh waters in 2003, and for swimming
pools and other similar environments in 2006 (WHO, 2003, 2006).
Following the promulgation of guidelines by USEPA and a growing awareness about the link
between poor water quality and disease spread, governmental agencies in developed countries
14
such as the United Kingdom (U.K.), Australia and Singapore began to routinely employ methods
that test water quality in bathing waters. In Singapore, NEA adopted the guidelines recommended
by WHO in 2003 and established the following criteria in August 2008 (NEA, 2013):
For recreational beaches, the parameters used to assess water quality are:
1. The Enterococcus counts should be less than or equal to 200 counts per 100ml of water
2. Susceptibility of the location to fecal influence
3. Only beaches classified as „Good‟ and above will be considered suitable for primary
contact activities
For freshwater bodies1, 95% of the time:
1. The Enterococcus counts should be less than or equal to 200 counts per 100ml of water
2. Chlorophyll-a‟s concentration should be less than or equal to 50mg/L of water
2.2.2 Attributes of an ideal indicator
Since the beginnings of water quality research, many indicators have been proposed but all
possess shortcomings in one way or another. In 2004, several attributes of an ideal biological
indicator and its measurement criteria are listed as follows (NRC, 2004):
2.2.2.1 Biological attributes
1. It should possess a strong quantifiable relationship to the pathogen and the degree of
public risk
1
Refers only to inland waters exposed to outdoor conditions such as reservoirs and swimming pools.
15
2. It should possess similar survival and persistence levels as pathogens, in reference to
environmental hazards such as ultraviolet (UV) light exposure, temperature, salinity,
predation, desiccation, freezing and response to disinfectants
3. It should have similar fate and transport mechanisms as pathogens, in terms of behavior
during filtration, sedimentation, and particle adsorption
4. It should occur in greater numbers than pathogens and thus be easily detectable
5. It should be specific to the source of contamination
2.2.2.2 Method attributes
1. Ability to quantify
2. Specificity to desired target organism/group
3. Broad applicability to different settings
4. Adequate sensitivity
5. Results rapidly obtained
6. Able to measure viable/infective target organism
7. Logistical feasibility in terms of cost, manpower and training, as well as sampling and
processing requirements
2.2.3 Limitations of traditional indicators
Due to historical reasons as explained in Section 2.2.1, fecal indicator bacteria (FIB), which
includes total and fecal coliforms, E. coli and Enterococcus are currently being used as
monitoring tools to indicate water quality and predict the presence of pathogens. However, it is
widely recognised that they are not ideal indicators and there are numerous limitations associated
with their usage. This section attempts to discuss the shortcomings of FIB with respect to the
ideal biological attributes as stated in Section 2.2.2.
16
There is an abundance of papers reporting on the lack of correlation between concentrations of
FIB and that of various types of pathogens, including enteroviruses, bacteriophages,
Campylobacter, Salmonella and Cryptosporidium, in several different water mediums such as
sewage effluent, coastal waters, estuarine systems and inland surface waters (Carter et al., 1987;
Ferguson et al., 1996; Baggi et al., 2001; Noble and Fuhrman, 2001; Lemarchand and Lebaron,
2003; Savichtcheva and Okabe, 2006). As the most important biological attribute that an indicator
should possess is a strong association between indicator and pathogen numbers, such reports
raises serious questions on the protective value of FIB and their suitability in predicting the
degree of public health risk.
FIB have been reported to possess differential survivability and persistence as compared to
pathogens. FIB are less resistant to harsh environmental conditions, disinfection processes and
survive for a shorter period of time as compared to pathogens which are more hardy such as the
protozoan pathogens (Cryptosporidium and Giardia), parasitic or biofilm-forming bacteria
(Legionella or Pseudomonas), as well as several types of viruses (McFeters, 1990; Hurst et al.,
2002; Baggi et al., 2001; Hörman et al., 2004). Furthermore, FIB are shown to be able to regrow
and multiply in the water column, which do not favour the survival of enteric pathogenic
organisms (Davies et al., 1995; Byappanahalli and Fujioka, 2004; Byappanahalli et al., 2006).
This leads to a decoupling of the pathogen-indicator relationship and further erodes their
association.
Models of bacterial fate and transport kinetics have shown that FIB are able to survive in the
water column by attaching to and persisting in fine particles or sediments, especially in the
presence of organic matter (Ferguson, 1994; Friesa et al., 2008). On the other hand pathogen
transport and distribution is more complicated and depends on several factors including
temperature, salinity, dissolved oxygen, pH, microbial size, growth, predation, metal or nutrient
availability (Metge, 2013). In particular, pathogens are prone to loss through settling and
17
inactivation due to temperature, UV exposure and grazing (Brooks et al., 2004). Such differential
transport mechanisms results in a changing of the relationship between pathogen and indicator at
varying distance from the source contamination and at different time periods, making it difficult
to assess water quality accurately.
FIB are good predictors of GI diseases as they are strongly associated with pathogens which are
enteric and transmitted via the fecal-oral route. However, they are not considered source-specific
with regards to two criteria: 1) they do not allow distinction whether the fecal contamination
source is from humans or from animals, and 2) the source input of various non-enteric pathogens
such as Legionella and Mycobacterium is likely to be different because of different host ecology,
and thus it is unlikely that the existing FIB approach can predict them accurately (Cappelluti et al.,
2003; Lam et al., 2011).
2.2.4 Alternative indicators
The vast majority of alternative indicators proposed have focused on markers for fecal
contamination, and they include fecal anaerobes, chemical compounds and viral indicators. Of the
three categories, only the first two pertain to the detection of bacterial pathogens which is of
interest in this study, while viral indicators are mainly used as surrogate indicators for enteric
viruses. As such this section focuses solely on the description of fecal anaerobes and chemical
compounds.
2.2.4.1 Fecal anaerobes
These group of organisms usually belong to the group of Bacteroides or Bifidobacterium and are
strongly associated with fecal matter in warm-blooded animals. The primary advantage of using
Bacteroides and Bifidobacterium as indicators is their inability to tolerate atmospheric oxygen
and survive in aerobic conditions. As such, detection of these organisms in large amounts indicate
recent and extensive fecal contamination (Savichtcheva and Okabe, 2006). Several Bacteroides
18
species are host-specific and as such, tracking methods can be developed to identify the source of
fecal contamination (Kreader, 1995; Simpson et al., 2004). Due to difficulties in cultivation in
anaerobic conditions, detection and enumeration is usually performed using molecular
approaches such as end-point and qPCR. However, a lack of correlation to non-enteric pathogens,
a lack of studies on the transport and geographical distribution of these markers, as well as
fluctuating levels of persistence due to seasonal changes are all problems associated with
measurement of these molecular indicators (Savichtcheva and Okabe, 2006).
2.2.4.2 Chemical compounds
Chemical compounds such as fecal sterols (which include cholesterol and coprostanol) have been
proposed as alternative indicators to address the shortcomings of most microbial markers, such as
multiplication in the water column as well as non-source specificity. In particular, coprostanol is
one of the major fecal sterol found in human and animal feces, and possesses a half-life of less
than 10 days at 20°C (Isobe et al., 2002), as such detection indicates the presence of recent fecal
contamination events. Fecal sterols can be detected and measured routinely using gas
chromatography-mass spectrometry (GC-MS). However, a major limitation of fecal sterols as
indicators is the absence of source specificity as a result of different transport mechanisms
between fecal sterols and pathogens: coprostanol can associate with and be preserved within
sediments therefore detection may imply old or remote fecal contamination. There is also a lack
of studies that determine host specificity, detection sensitivity and correlation to pathogen levels,
as such they are not commonly used as indicators of microbial water quality (Savichtcheva and
Okabe, 2006).
2.2.5 Classical methods of bacterial indicator detection
Traditionally, indicator bacteria such as coliforms have been detected using culture-dependent
methods. These methods are regarded as the 'gold-standard' methods and are routinely performed
19
in monitoring agencies. However, they possess several limitations that detract them from being
regarded as ideal indicators as described in Section 2.2.2. The major shortcoming is the lack of
sensitivity, which could lead to an underestimation of numbers especially when aiming to detect
stressed or starved bacterial cells. They are also labourious and time-consuming to perform,
resulting in a time-lag between sample collection and obtaining of results, between which the
microbial composition and numbers may have experienced drastic changes. Most importantly,
several studies have shown that FIB levels exhibit extreme variability over short time scales
(Leecaster and Weisberg, 2001; Boehm, 2007). This leads to the possibility that FIB
concentrations may vary more quickly than the monitoring results obtained, such that by the time
culture results are available, the water quality may have changed substantially. This creates
problems for policy makers who have to rely on outdated and possibly erroneous information
when deciding whether or not to issue swimming bans or advisories.
2.2.5.1 Multiple-tube fermentation technique
Multiple-tube fermentation (MTF) involves serial dilution of the collected water sample, followed
by inoculation of each dilution into a set of tubes containing lactose or lauryl tryptose broth.
Tubes are monitored for coliform growth and production of gas following a 24hr incubation
period at 37°C. The number of tubes with a positive, presumptive reaction is noted, and subjected
to a second, confirmatory test using brilliant green lactose bile broth. This method can be
modified to test for fecal coliforms and E. coli by using Escherichia coli broth (EC broth) and EC
broth supplemented with 4-methylumbelliferyl-β-D-glucuronide (MUG) respectively. Results are
expressed statistically in terms of the most probable number (MPN) of bacteria, or the mean
number of organisms expected to be present in the sample considering the number of positive
tubes and the dilution factor. This technique suffers from low precision and possible interference
from non-coliform bacteria in the environmental water sample, resulting in an underestimation of
20
coliform numbers. Furthermore it is more time-consuming as compared to the membrane
filtration technique.
2.2.5.2 Membrane filtration technique
The membrane filtration (MF) technique involves the filtration of a known volume of water
sample through a 0.45µm sterile nitrocellulose membrane. The membrane is retained and
incubated on selective medium such as m-Endo or Tergitol-TTA agar; subsequently the number
of total coliforms is enumerated via colony count on the membrane. This method is considered
presumptive and typically confirmatory tests are conducted by inoculating individual colonies in
brilliant green lactose bile broth, EC broth or EC-MUG broth for total coliforms, fecal coliforms
and E. coli respectively. MF is widely accepted as a technique for water quality monitoring
(APHA, 1998) and presents several advantages over MTF, which includes a shorter processing
time, and the ability to examine larger quantities of water, thereby resulting in greater sensitivity
as compared to MTF. However, questions have been raised about the problem of interference
from competing bacteria, resulting in suppression of coliform growth and an underestimation of
their numbers. Another problem is the inability to recover stressed or injured coliforms due to
exposure to disinfection agents such as chlorine. Both problems result in lowered accuracy of this
technique.
2.2.6 Emerging methods in indicator detection
Culture-based methods for detecting indicator organisms, as elaborated upon in the earlier section,
suffer from several limitations such as long processing times, low sensitivity, specificity,
accuracy and reliability. Newly developed methods for the detection of indicators boast higher
rapidity, sensitivity and specificity; these same attributes also make them suitable for the direct
detection of low pathogen levels directly from the environmental sample. This section will cover
immunological techniques, which are common techniques used for the detection of indicators,
21
and give an in-depth analysis of qPCR as a method that is able to detect and quantify both
indicators and pathogens.
2.2.6.1 Immunological methods
Immunological methods are based on specific antigen-antibody interactions to detect target
bacteria. Generally either cell-capture using ELISA, cell-detection using immunofluorescence
assay (IFA) or immuno-enzyme assay (IEA) can be performed, using polyclonal or monoclonal
antibodies. All of these tests are simple, rapid and relatively sensitive, allowing for specific
detection, identification and quantification, and as such attempts have been made at using
immunological methods for detection of water indicators. However, the main limitation is that
there are very low numbers of target cells in the sample, and the accuracy depends on the
specificity of the antibody. Various studies found that these methods require pre-culture of the
sample for 24hrs (Obst et al., 1989), demonstrated cross-reactivity with competitors of the target
organism (Levasseur et al., 1992) as well as produced false positive results despite using
monoclonal antibodies (Hubner et al., 1992). As such, immunological methods have been met
with limited success.
2.2.6.2 Real-time PCR
Real-time PCR is a molecular technique that enables quantification by detecting the amount of
amplified products as measured by the emission of fluorescence. Several PCR protocols have
been developed to detect and/or measure the levels of traditional fecal indicators (e.g. E. coli,
Enterococcus, fecal coliforms) and pathogens (e.g. Salmonella) (Iqbal et al., 1997; Tsen et al.,
1998; Waage et al., 1999). The high sensitivity makes it a technique that is not only suitable to
monitor indicator count but also detect pathogen levels directly. By using primers that target
phylogenetic markers (e.g. 16S rRNA, gyrB or rpoD) and/or virulence factors, it is possible to
detect and identify waterborne protozoa, bacteria and viruses, and even determine whether the
22
targeted organism possesses the virulence marker, therefore indicating possible pathogenicity
(Joly et al., 2006; Matsuda et al., 2009; Benitez and Winchell, 2013; Le Gall et al., 2013). qPCR
not only possesses higher sensitivity and specificity as compared to microarray and end-point
PCR, it also has a faster rate of detection as well as the ability to provide quantitative results. On
the other hand, detection is limited to microorganisms with known sequences, and only one gene
(or a few genes, if multiplex protocols are used) may be targeted at any one time.
qPCR has been successfully demonstrated to quantify various pathogens including Giardia,
Cryptosporidium, Vibrio parahaemolyticus, pathogenic Candida, Campylobacter, Legionella,
Aeromonas and Salmonella in various different environmental matrixes including surface waters
(tap, pond and river water), rainwater and sewage (Blackstone et al., 2003; Brinkman et al., 2003;
Guy et al., 2003; Ahmed et al., 2008). However, its major limitation is the inability to
differentiate nucleic acid material obtained from viable and dead cells, as well as PCR inhibition
issues arising from the humic substances and colloid matter commonly present in environmental
samples (Rompre et al., 2002). Nonetheless, with the multiple advantages over traditional culture
and immunological methods, various research groups have recommended its use as a
complementary tool or even total replacement of standard methods in the monitoring of
recreational waters (Metcalf et al., 1995; Haugland et al. 2005; USEPA, 2012).
2.2.7
Issues with molecular detection of indicators
2.2.7.1 Differentiating cell viability
Although molecular methods such as real time PCR offer great advantages in terms of detecting
and enumerating indicator organisms, one major limitation is the inability to differentiate DNA
contributed from live and dead cells. Studies have shown that DNA may persist for extended
periods of time in the environment after cell death (Josephson et al., 1993; Masters et al., 1994),
which will result in generation of false positive signals or an overestimation of the target
23
organism when molecular methods are used for detection and enumeration, resulting in an
inaccurate measure of pathogen risk. While incorporation of microscopic visualization or flow
cytometry techniques allows for live/dead cell discrimination, it is difficult to perform for water
samples where pathogen load is low and is not feasible for routine monitoring purposes.
In the last few years, propidium monoazide (PMA) and ethidium monoazide (EMA) have been
reported to be a promising alternative method to distinguish between live and dead cells in
environmental samples (Nocker et al., 2007). PMA and EMA are intercalating dyes that
covalently binds to DNA to form a stable nitrogen-carbon bond, modifying DNA permanently.
They cannot penetrate through intact membrane of live cells and thus can be used to selectively
bind to DNA contributed from dead cells with compromised membrane structure while leaving
DNA from live cells intact. Various studies have reported on the feasibility of using PMA/EMA
to reduce signal from dead bacteria within selected environmental water matrixes (Nogva et al.,
2003; Rudi et al., 2005; Nocker and Camper, 2006; Nocker et al., 2006). In addition, PMA
combined with qPCR has been successfully applied for the detection and enumeration of fungi,
protozoa, enteric viruses and bacteriophages (Vesper et al., 2008; Brescia et al., 2009; Fittipaldi
et al., 2010; Parshionikar et al., 2010). However, as environmental matrixes are highly complex
and contains a number of factors (e.g. turbidity) that may affect the effectiveness of PMA/EMA
treatment, it is still necessary to optimize and evaluate this approach when applied to 'real-world'
recreational water samples (Gedalanga and Olson, 2009).
2.2.7.2 Overcoming PCR inhibition
PCR inhibitors are inhibitory molecules or particles that are usually found in clinical and
environmental samples such as soil and fecal material, and inhibits PCR by mechanisms such as:
interacting and inactivating DNA polymerase or DNA intercalating dyes such as SYBR Green I,
interfering with probe/primer binding, degradation or capture of nucleic acids or interference with
24
the DNA extraction reaction through inhibition of the release of nucleic acid material (Wilson,
1997; Rådström et al., 2003). Because PCR inhibition can affect the yield and specificity of the
reaction resulting in lowered amplification efficiency as well as an underestimation of the amount
of gene target, it is important to determine their effect and optimize the PCR reaction when
working with environmental samples (Katcher and Schwartz, 1994).
In qPCR, the effect of PCR inhibitors is usually investigated by studying the amplification
efficiency and the linear range of amplification, typically performed by doing a dilution series of
DNA spiked in the environmental matrix in comparison with nuclease-free H2O. In the presence
of PCR inhibition problems, it is then necessary to perform pretreatment of the samples to reduce
or remove the effect of inhibitors, using techniques such as density gradient centrification
(Lindqvist et al., 1997), dilution (Al-Soud and Rådström, 1998), DNA extraction (Dahlenborg et
al., 2001) or filtration (Lantz et al., 1999). Alternatively, inhibitor-tolerant thermostable
polymerases or inclusion of amplification facilitators which are able to enhance PCR efficiency in
the presence of inhibitors could be used in the PCR reaction (Al-Soud and Rådström, 1998).
2.3
Direct pathogen monitoring
There are many unanswered questions with regards to the severity and frequency of disease in
recreational water settings, and it is highly plausible that various diseases arising from usage of
contaminated water may have gone unnoticed. Most characterized illnesses are acute and of GI
origin and therefore research in this field have mostly focused on developing methods to detect
fecal contamination using the indicator approach. As described in the previous section, the
indicator approach is flawed and possesses several limitations such as different contamination
sources and differences in survival, persistence, fate, transport therefore resulting in a lack of
correlation between indicator and pathogen levels. With the advent of molecular tools which
boasts greater rapidity, sensitivity and specificity as compared to traditional culture-based
25
methods, it is now also possible to conduct direct detection and monitoring of pathogens. Not
only a wider range of pathogens can be detected, novel pathogens can be discovered and studied
for associations with waterborne diseases (Aw and Rose, 2012).
Methods for pathogen discovery and detection commonly employ the 16S ribosomal RNA (16s
rRNA) gene for phylogenetics characterisation and assignment of identity. This section first
discusses the use of the 16S rRNA gene as a phylogenetic marker for characterising
microorganisms, as well as describes the various molecular tools which are available for direct
pathogen detection, including microarrays and sequencing techniques.
2.3.1 Phylogenetic analysis using 16S ribosomal RNA gene
The 16S rRNA gene is a valuable tool for studying bacterial phylogeny and taxonomy due to a
number of reasons. These reasons include 1) it is present and conserved in almost all known
bacterial species, 2) sequence differences between bacterial groups are a result of random
sequence changes which makes it a good measure of evolution, 3) the structure of this gene
consists of conserved regions interspersed with regions prone to mutations (variable regions),
allowing for the possibility of designing universal primers that target almost all bacterial groups,
and 4) the large size (1500bp) of the gene is suitable for bioinformatics purposes. The ease in
sequencing the 16S rRNA gene for bacterial identification has resulted in an explosion of the
number of recognized taxa (13537 published names to date compared to 62 in 1980 for
taxonomic category of Class and below), many of which belong to uncultured bacterial species
(LPSN, 2013). To handle this data and make it accessible to the general public, as well as to
provide tools for the analysis of 16S rRNA data, several online databases have been set up.
Ribosomal
Database
Project
(RDP)
(http://rdp.cme.msu.edu/),
Greengenes
(http://greengenes.lbl.gov) and Silva (http://www.arb-silva.de/) are examples of repositories that
are quality-checked, regularly updated and therefore of greater use to the scientific community.
26
The widespread availability of 16S rRNA data also makes possible the identification up to genus
and species level bacterial isolates that are not culturable or that generate ambiguous biochemical
profiles.
In spite of the usefulness of 16S rRNA gene sequencing in classifying bacterial groups, it should
be noted that due to a high degree of similarity (only a few nucleotide differences) between
closely related species, the phylogenetic power is low at the species level and there may be an
inability to discriminate between certain genus. For example the type strains of Edwardsiella
species are 99.35 to 99.81% similar in DNA sequence and are not able to be differentiated by 16S
rRNA analysis, yet they are distinguishable biochemically and by DNA homology. There is also
currently no scientific consensus on the definition of species identification using 16S rRNA gene
sequencing, and studies vary widely as to calling a species match (Janda and Abbott, 2007). Other
issues include sequencing accuracy, isolate purity and DNA extraction methods, all of which may
affect the final identification.
2.3.2
DNA microarrays
DNA microarrays are two-dimensional ordered arrays that consist of a high density of
immobilized nucleic acids, which serve as probes that hybridize to complementary sequences
found in the sample, as such detecting thousands of genes in a single assay. Although
theoretically useful to directly detect and analyse the pathogens present in environmental samples,
this technology suffers from limitations such as the lack of pathogen sequence information, and
lesser sensitivity and specificity as compared to qPCR (refer to Section 2.2.6.2), making it
impractical for the routine monitoring and detection of low levels of pathogen in environmental
matrixes. On the other hand, it has been useful in analyzing the microbial profile and determining
the community structure in complex environmental samples such as soil and wastewater, when
conducted in tandem with an enrichment procedure such as PCR amplification. To this end,
27
phylogenetic microarrays targeting the 16S rRNA gene (PhyloChip) have been applied to the
analysis of microbial community structure of bacteria (DeSantis et al., 2007), and is useful as a
research tool to discover and characterize novel pathogens within the environmental sample.
2.3.3
Sequencing studies
Prior studies of bacteria evolution and phylogenetics, followed by the discovery of 16S rRNA
gene sequences heralded a paradigm shift from culture to molecular-based in the field of
pathogen identification. It was realised that targeted sequencing of the 16S rRNA gene could
enable investigators to establish an identity of several microbial species that could not be
identified by traditional culture methods (Pace et al., 1985). Initial studies were based on Sanger
sequencing of the entire 1.5kB gene, however, this method was laborious and not suitable for
routine use. As more work was conducted on the 16S rRNA gene, it was found that the gene
consisted of hypervariable regions interspersed with conserved regions, and sequencing a number
of the hypervariable regions (approximately 500bp) is sufficient for genus and species-level
identification. The precise definition of sequence targets to produce shorter reads, coupled with
the introduction of next-generation sequencing (NGS) platforms which allowed for a tremendous
increase in output, resulted in an explosion of data on microbial discovery and characterisation.
2.3.3.1 Next-generation sequencing overview
Also referred to as massively parallel sequencing, NGS allows for sequencing reactions to be
carried out at a scale not possible to be achieved by traditional Sanger sequencing. In addition, it
offers several advantages, such as the miniaturization of sequencing chemistries and higher
throughput, leading to lower cost per sequenced base as well as reduction in time and manpower.
The principle is based on segregation of target DNA strands (emulsified in lipid droplets as in
Roche 454-sequencing or attached to flow cell surface as in Illumina sequencing), followed by
controlled binding of nucleic acids and detection of bioluminescence to monitor pyrophosphate
28
release. Currently, Roche's GS-FLX+ system allows for a throughput of more than 1,000,000
reads in a single run within 23 hours while Illumina's Hiseq system produces 176Gb of data
within 40 hours easily, with both platforms achieving more than 10X coverage when sequencing
human genomes (Roche, 2013; Illumina, 2013).
2.3.3.2 Application to metagenomics and pathogen detection
NGS is mostly applied to re-sequencing studies, such as the metagenomics analysis of bacterial
and viral diversity in environmental samples (Bibby et al., 2010, 2011; Kristiansson et al., 2011;
Ye and Zhang, 2011). This is achieved by extracting genomic DNA and converting it into a NGS
library, followed by sequencing. The reads are aligned to known reference sequences for
microorganisms that are thought to be present within the sample. Because of the relatively short
read lengths (400-500bp) produced by sequencing as well as the availability of large, well-curated
public databases of the 16S rRNA gene, most applications have focused on the sequencing of
selected hypervariable regions within this gene. Examples of metagenomics studies include the
sequencing of microbial diversity in oceans, soils, human gut microbiota and oral cavity (Sogin et
al., 2006; Huber et al., 2007; Keijser et al., 2008; Turnbaugh et al., 2009; Urich et al., 2008).
NGS can also be applied to detection of pathogens in environmental waters using similar
procedures as those involved in the study of microbial ecology, which includes concentration of
bacteria, viruses or protozoa, followed by DNA/RNA extraction, pyrosequencing and
bioinformatics analysis. Using this approach, it is possible to detect novel pathogens in an
unbiased, high-throughput manner that will otherwise not be discovered by PCR and microarray
methods where investigators are limited by sequence information and must decide on which
known pathogen groups to target (Aw and Rose, 2012). Studies that have utilised NGS techniques
for pathogen discovery include the discovery of a novel arenavirus transmitted through solidorgan transplantation,
29
2.3.3.3 Bioinformatics
To ensure the accurate classification and identification of pathogens, it is necessary to consider
the challenges of downstream bioinformatics analysis of the data output. Starting from sequence
collection, algorithms should be used to trim off primer sequences and filter off poor quality reads
which have a higher chance of containing ambiguous bases or to be aberrantly long or short
(Huse et al., 2007). Alignment of sequences can be conducted using alignment programs such as
Clustal-W, MUSCLE or NAST (Thompson et al., 1997; DeSantis et al., 2003; Edgar, 2004).
Chimeric-sequences may be generated as a result of PCR errors that couple individual DNA
strands during amplification; as such they should be removed via tools such as Bellaphon (Huber
et al., 2004) or online programs available in Greengenes and RDP. After the obtainment of highquality data, sequences may then be identified using classifiers such as the Bayesian Classifier in
RDP or prokMSA in Greengenes, and then screened for the presence of pathogenic species.
During metagenomics characterisation of previously unsequenced microbial habitats, the species
diversity may be of interest to the researcher. As such there are multiple indices that have been
developed for this purpose. These indices include the Chao and Ace estimates of species richness,
the Simpson and Shannon indices that measure the abundance and evenness of species present, as
well as rarefaction curves that accesses species diversity as a result of sampling and allows one to
determine whether the amount of obtained reads from sequencing is sufficient to encompass the
true diversity that is present.
30
Chapter 3: Quantifying molecular markers as indicators of waterborne
pathogens
3.1
Introduction
Traditional methods of water quality assessment relied on conventional culture techniques to
enumerate indicators of fecal contamination such as total and fecal coliforms, Enterococcus sp.
and E. coli. These methods are not necessarily indicative of pathogens that are introduced via
extra-enteric pathways such as urban runoff and human bather load or that have different levels of
persistence and transportation routes within the water system. There are also several flaws in the
existing indicator approach such as a lack of a consistent strong relationship between the
pathogen and the indicator, as well as a necessary delay between sample collection and results
analysis because of culture requirements. In Singapore, assessment of recreational waters from
aquatic facilities is based on such methods despite the lack of data documenting the applicability
of these guidelines in our context.
Quantifying bacterial markers using qPCR has proven to be a viable option against traditional
molecular methods. Several research groups have proposed the usage of certain molecular
markers such as Bacteroidetes thetaiotaomicron alpha-mannanase gene and Methanobrevibacter
smithii nifH gene as indicators for fecal contamination (Yampara-Iquise et al., 2008; Johnston et
al., 2010), however these methods have not been tested on recreational waters from aquatic
facilities. Furthermore, qPCR suffers from the inability to discriminate between viable and dead
cells, and are hence may not be indicative of the true risk of contracting diseases through
exposure to polluted waters. Even though DNA intercalating dyes such as PMA and EMA are
proposed as a solution by suppressing signals produced by dead cells within the sample, the
extent of such suppression effects is unknown and can vary considering the complex nature of the
environmental matrix. In addition, environmental samples typically contain PCR inhibitory
31
substances that may affect the amplification efficiency of qPCR, and any attempts at designing a
qPCR assay should take this into account.
This section will focus on the work that has been performed to assess the effectiveness of some
proposed bacterial markers on interactive water fountains in Singapore. In particular, it will cover
the development of real-time assays targeting several commonly used and alternative indicators,
as well as pathogens that are believed to be often found in environmental waters. This is followed
by assay optimisation tests including a comparison between the effects of PMA and EMA, the
effects of using different concentrations of PMA on laboratory-grade water as well as
environmental matrix, and the determination of possible inhibitory effects from the collected
samples and the environmental matrix. Lastly, it will determine the correlation and predictive
ability of tested indicators against known pathogens found in several interactive fountain water
samples.
3.2
Materials and methods
3.2.1 Sample collection and processing
3.2.1.1 Study sites
Water samples were collected from the following locations:
Bugis Junction (BJ) Mall Level 1 outdoor interactive fountain
City Square (CS) Mall Level 1 outdoor interactive fountain
IMM building Level 3 Garden Plaza playground
Sembawang Shopping Centre (SB) Level 3 outdoor playground
Science Centre Singapore (SC) Level 1 Waterworks
VivoCity (VC) Level 3 wading pool
32
The geographical distribution and individual site images are shown in Figures 3.1 and 3.2
respectively while Table 3.1 shows the characteristic of each recreational facility. Sampling
conditions were kept consistent with regards to time of collection and weather condition, and was
avoided on days during or after heavy rainfall.
Figure 3.1: Geographical locations of sampling sites. Map data © 2014 Google, MapIT. Captions have been added by the author.
33
A
B
C
D
E
F
Figure 3.2: Individual site images - (A) BJ, (B) CS, (C) IMM, (D) SB, (E) SC, (F) VC. (B) has been adapted from kidsandparenting.com
(Kids and Parenting, 2013); all other images are author's own.
Table 3.1: Location characteristics of sampled recreational water facilities.
Location
Type of facility
Water system
Exposure status
Bugis Junction (BJ)
Fountain
Recycled
Open
City Square (CS)
Fountain
Recycled
Sheltered
IMM
Water playground
Recycled
Open
Sembawang (SB)
Water playground
Recycled
Sheltered
Science Centre (SC)
Water playground
Recycled
Sheltered
Wading pool
Recycled
Open
VivoCity (VC)
34
3.2.1.2 Sampling procedures
Water samples were collected according to the recommended procedures in Standard Methods for
the Examination of Water and Wastewater (SMEWW), Section 9060 (APHA, 1988). Preliminary
results (data not included) had determined that 1L sampling volume was sufficient to capture
most microorganisms without significant impact on water flow during membrane filtration; as
such sampling volume was standardised to 1L for the duration of this study. Samples were
collected either from the water reservoir or through water nozzle jets, depending on whether the
facility was a fountain/wading pool, or an outdoor playground, respectively. Samples were
collected using a pre-autoclaved 1L measuring beaker, and transferred into pre-autoclaved sterile
plastic screw-cap bottles (Thermo Scientific, U.S.A) or disposable Whirl-Pak® Stand-up Bags
(Nasco, U.S.A). Commercial sodium thiosulfate pills (Nasco, U.S.A) used to dechlorinate
residual chlorine levels were added to water samples to give a final concentration of 120mg/L.
Samples were placed on ice-packs and transported using cooler bags to the laboratory, and were
processed within 6hrs of collection.
3.2.1.3 Chemical tests
Temperature was measured on-site using a Multi-parameter PCSTestr™ 35 probe (Eutech
Instruments, U.S.A). Measurement of other water parameters were conducted in the laboratory on
duplicate 50ml water samples with no added sodium thiosulfate.
Total Dissolved Solids (TDS), salinity, conductivity and pH were measured using the PCSTestr™
35 probe (Eutech Instruments, U.S.A). In addition, specific colorimetric test strips (Waterworks,
U.S.A) were used to test for hardness, total and free chlorine as well as alkalinity. All procedures
were performed according to manufacturer‟s instructions.
3.2.1.4 Membrane filtration
35
Membrane filtration was carried out according to the recommended procedures in SMEWW
Section 9222 (APHA, 1988). Sample filtration was carried out using a pre-autoclaved filter unit
connected to a vacuum pump. Water samples were pooled and mixed thoroughly, and
subsequently filtered through a 0.22µm nitrocellulose membrane (Merck Millipore, Germany)
under aseptic conditions. A pre-filtration step was conducted for turbid water samples. This step
was performed if the flow rate was less than 1L in 30min through a 0.22µm nitrocellulose
membrane (arbitrary value chosen in consideration to the amount of water sample that can be
processed within 6hrs of collection). Samples were first filtered aseptically through a 3.0µm
cellulose nitrate filter (Sartorius, Germany) into a clean, autoclaved bottle to remove large debris
and particulate matter, before the filtrate was again filtered through a 0.22µm nitrocellulose
membrane. The 3.0µm membrane for pre-filtration membrane was discarded while the 0.22µm
nitrocellulose membrane was retained for culture analysis or DNA extraction.
3.2.1.5 DNA extraction from water samples
Membranes were stored in 5ml RapidWater™ Bead Tubes (MO BIO, U.S.A.) at -20°C until
ready for DNA extraction. DNA extraction was performed using RapidWater™ DNA Isolation
Kits (MO BIO, U.S.A.) according to manufacturer‟s instructions.
3.2.2 Real-time polymerase chain reaction (qPCR) analysis
3.2.2.1 Reference strains used in standards
All bacterial strains used in this study were obtained from the American Type Culture Collection
(ATCC, U.S.A.), with the exception of Aeromonas hydrophilia which was obtained from the
Department's culture stock. A. hydrophilia and E. coli (ATCC 11775) were cultured in nutrient
broth (Oxoid, U.S.A.) at 37°C. Enterococcus faecalis (ATCC 19433) was cultured in brain heart
infusion broth (Oxoid, U.S.A.) at 37°C. Pseudomonas alcaligenes (ATCC 53877) and
Staphylococcus aureus (ATCC 6538) were cultured in tryptic soy broth (Oxoid, U.S.A.) at 28°C.
36
Confirmation of culture identity was performed using the appropriate API test kits (bioMérieux
SA, France) prior to standard development.
Total bacterial counts were determined by serial dilution of each culture strain grown in the
appropriate culture broth and conditions (as described above) overnight. 100µl portions of each
dilution were spread on the following agar culture medium: nutrient agar (Oxoid, U.S.A.) for A.
hydrophilia and E. coli, tryptic soy agar (Oxoid, U.S.A.) for P. alcaligenes, and Baird-Parker agar
(Becton, Dickinson and Company [BD], U.S.A.) for S. aureus. Each dilution was performed in
triplicates. Colonies were counted to determine the CFU counts per milliliter of culture.
DNA extraction was performed using an in-house protocol detailed in Appendix III.
3.2.2.2 Development of plasmid standards
Plasmid standards were developed for bacteria which were difficult to obtain or culture
(Bacteroidetes thetaiotaomicron, Legionella pneumophila and Methanobrevibacter smithii). The
targeted gene was checked using NCBI Basic Local Alignment Search Tool (BLAST) program
(Altschul et al. 1990), and subsequently sent for synthesis (Integrated DNA Technologies,
U.S.A.). E. coli DH5α competent cells were transformed with the 1:1000-diluted synthesized
plasmid and plated on Luria Bertani (LB) agar supplemented with ampicillin as selection medium
incubated at 37°C overnight (Appendix II). Colonies were picked and grown in LB-A broth at
37°C overnight in 10ml culture tubes (Greiner bio-one, Belgium). Cultures were harvested by
transferring into 2ml microcentrifuge tubes (Axygen, Corning, U.S.A.) and subsequently pelleted
down. Plasmid extraction was performed using the E.Z.N.A.® Plasmid Miniprep Kit (Omega
Bio-Tek, U.S.A.) according to manufacturer‟s instructions. The purified plasmid was quantified
by the Nanodrop™ 2000 Spectrophotometer (Thermo Scientific, U.S.A.). Plasmid copy number
was estimated using an online calculator (Staroscik, 2004). The extracted plasmids were stored at
-20°C until further use.
37
3.2.2.3 Validation of primer set using end-point PCR
Selected primers for this study target common (Enterobacteriaceae and Enterococcus) and
alternative (M. smithii and B. thetaiotaomicron) indicators of fecal pollution, as well as markers
of potential pathogens which are known to be present in environmental water samples (Table 3.2).
Primers were tested for specificity by submitting and checking the sequences using the Probe
Match program in the RDP website (Cole et al., 2008). Primers were further tested for specificity
using end-point PCR on the total genomic DNA of the following organisms: A. hydrophilia, E.
faecalis, E. coli, P. alcaligenes and S. aureus. Each 10-µl PCR reaction consisted of the following
components: 0.05µl TaKaRa Ex Taq (5 units/µl), 1µl 10X Ex Taq Buffer, 0.4µl dNTP Mixture
(2.5mM each), 0.4µl each of forward primer TX9 (10µM) and reverse primer 1391R (10µM), 1µl
template DNA (1ng/µl) and 6.75µl milliQ water. PCR reaction was accompanied with a negative
control replacing template DNA with 1µl milliQ water. Thermocycling of each primer set
proceeded as follows: one cycle at 95°C for 15s, 30 cycles of denaturation at 94°C for 20s,
appropriate annealing temperature (Table 3.2) for 20s and extension at 72°C for 50s, and lastly
one cycle of final extension at 72°C for 10min. PCR products were checked for size and
specificity using a diagnostic 2% (w/v) agarose gel, for amplicon sizes less than 100bp, or a 1%
(w/v) agarose gel, for amplicon sizes above 100bp (Table 3.2), and confirmed to have only a
single band present at the target group (Figure 3.3).
38
Figure 3.3: Example of gel used to check PCR product size and primer specificity. Image shows amplification results of various
bacteria groups using Leg16S primer pair, showing only a single band present in Lane 2 (L: L. pneumophila). Lanes 3-8 (Ec: E. coli,
Ef: E. faecalis, S: S. aureus, A: A. hydrophilia, P: P. alcaligenes, V: Vibrio parahaemolyticus. Lane 8 represents negative control (-ve).
Imaging faults in the machine could cause white areas to be visualised in some gels (such as that found in '-ve'); they are identified as
artifacts based on shape (usually round or irregular) and size (usually a distance from expected band position).
39
Table 3.2: List of gene-targeted primers used in this study.
Target group (gene)
Pathogens
Aeromonas (16S rRNA)
Legionella (16S rRNA)
Mycobacterium (Ag85)
Pseudomonas (16S rRNA)
Escherichia coli (uidA)
Staphylococcus (16S rRNA)
Indicators
B. thetaoitaomicron
(α-mannanase)
Enterobacteriaceae (16S rRNA)
Enterococcus (23s rRNA)
M. smithii (nifH)
Primer name
Primer sequence (5’-3’)
Aer66f
Aer613r
Leg16S1-A
Leg16S2-A
BB-1
BB-3
PSD7F
PSD7R
uidA1318F
uidA1698R
g-Staph-F
g-Staph-R
Bac-alpha-man-F
Bac-alpha-man-R
En-lsu-3f
En-lsu-3r
ECST784F
ENC854R
Mnif-H-F
Mnif-H-R
Annealing
temp (°C)
PCR
product size
(bp)
Reference
GCGGCAGCGGGAAAGTAG
GCTTTCACATCTAACTTATCCAAC
AGGGTTGATAGGTTAAGAGC
CCAACAGCTAGTTGACATCG
ATCAACACCCCGGCGTTCGAG
CGGCAGCTCGCTGGTCAGGA
CAAAACTACTGAGCTAGAGTACG
TAAGATCTCAAGGATCCCAACGGCT
CCGATCACCTGTGTCAATGT
GTTACCGCCAACGCGCAATA
TTTGGGCTACACACGTGCTACAATGGACAA
AACAACTTTATGGGATTTGCWTGA
61
~500
Yu et al. 2005
57
386
Joly et al. 2006
60
162
Fauville-Dufaux et al. 1992
60
215
Matsuda et al. 2009
60
~400
Bower et al. 2005
60
79
Matsuda et al. 2009
CATCGTTCGTCAGCAGTAACA
CCAAGAAAAAGGGACAGTGG
TGCCGTAACTTCGGGAGAAGGCA
TCAAGGACCAGTGTTCAGTGTC
AGAAATTCCAAACGAACTTG
CAGTGCTCTACCTCCATCATT
GAAAGCGGAGGTCCTGAA
ACTGAAAAACCTCCGCAAAC
60
63
Yampara-Iquise et al. 2008
60
428
Matsuda et al. 2009
60
~100
Frahm and Obst 2003
57
151
Johnston et al. 2010
40
3.2.2.4 Validation of primer set using real-time PCR
Before proceeding with absolute quantification of target DNA in samples, each primer set was
tested for efficiency and sensitivity using qPCR. Standards were subjected to 10-fold dilutions
and amplified by qPCR in duplicates under appropriate annealing temperature (Table 3.1). Meltcurve analysis as well as gel electrophoresis was used to validate the purity of PCR product. A
standard curve was generated for each primer set based on Cycle Threshold (CT) values.
Primer efficiency (%) was calculated according to the following formula:
−1
𝐸 = (10 𝑚 ) − 1 × 100
where m refers to the slope of the standard curve.
The sensitivity of primers were tested by determining the limit of detection (LOD). This was
achieved by performing a dilution series, and obtaining the highest CT value at which the data
point remains at the log-linear range of the standard curve.
Amplification efficiency and limits of detection of each primer set are shown in Table 3.3. All
primer pairs were verified to have good amplification efficiency (ranging between 90-110%).
Sensitivity was shown to be slightly lower for primer pairs amplifying Staphylococcus,
Enterobacteriaceae and Enterococcus. Considering that these bacteria groups are found in high
densities in gut microbiota (Staphylococcus: 105 cells/g; Enterobacteriaceae and Enterococcus
106 to 109 cells/g), contamination of fecal material in water sources should introduce substantial
amounts; as such primer sensitivity was not deemed as limiting and we proceeded to work with
identified primer pairs (Matsuda et al., 2009).
41
Table 3.3: Validation of amplification efficiency and lowest limit of detection of primer set.
Bacteria group (gene)
Pathogens
Aeromonas (16S rRNA)
Legionella (16S rRNA)
Mycobacterium (Ag85)
Pseudomonas (16S rRNA)
Escherichia coli (uidA)
Staphylococcus (16S)
Indicators
B. thetaoitaomicron
(α-mannanase)
Enterobacteriaceae (16S rRNA)
Enterococcus (23s rRNA)
M. smithii (nifH)
Primer name
Amplification
efficiency (%)
Limit of detection
(No. gene targets/µl)
Aer66f
Aer613r
Leg16S1-A
Leg16S2-A
BB-1
BB-3
PSD7F
PSD7R
uidA1318F
uidA1698R
g-Staph-F
g-Staph-R
90
2
94
5
96
8
99
2
98
4
94
158
Bac-alpha-man-F
Bac-alpha-man-R
En-lsu-3f
En-lsu-3r
ECST784F
ENC854R
Mnif-H-F
Mnif-H-R
97
10
107
33
106
78
96
3
3.2.2.5 Real-time PCR of samples
Real-time PCR was performed in 96-well optical plates (Scientific Specialities, Inc., U.S.A.)
using an ABI PRISM 7500 sequence detection system (Applied Biosystems, U.S.A.). Each 20-µl
reaction was composed of the following reagents: 10µl Maxima SYBR Green/ROX qPCR Master
Mix (2X) (Thermo Scientific, U.S.A.), 0.4µl each of forward and reverse primer (10µM each),
2µl template DNA and 7.2µl milliQ water. Each run consisted of samples run in triplicates, serial
dilutions of standard DNA in triplicates, as well as a single non-template control (NTC).
Thermocycling conditions were as follows: one cycle at 50°C for 2min, one cycle of denaturation
at 95°C for 10min, 40 cycles of denaturation at 95°C for 20s, appropriate annealing temperature
(Table 3.2) for 20s and extension at 72°C for 50s, followed by melt-curve analysis to distinguish
the target DNA from other non-specific PCR products. Samples were progressively denatured by
42
increasing temperatures from 75°C to 95°C in 0.2°C increments, with detection of fluorescence
dye emission during each temperature increment, to generate melt curve profiles.
3.2.2.6 Data analysis
Cycle thresholds (CT values) were automatically determined by the ABI PRISM 7500 operating
system. The standard curve was derived from the serial dilutions of standards from each qPCR
run. Quantification was performed by comparing the average CT value of each sample against the
standard curve. Data analysis was performed using SPSS® Statistics 20 (IBM, U.S.A.).
Spearman‟s correlational test for non-parametric data was used to determine the level of
correlation between bacteria and water parameters. The significance level was set at 95%
confidence level.
3.2.3 Differentiation between live/dead bacteria
3.2.3.1 Culture strain
E. coli (ATCC 11775) was used for all optimization experiments involving PMA and EMA. E.
coli was cultured in sterile 15ml Falcon tubes (Fisher Scientific, U.S.A.) containing 10ml of
nutrient broth (Oxoid, U.S.A.) at 37°C overnight prior to experimentation.
3.2.3.2 Preparation of dead cells
E. coli were heat-killed by heating the cells to 100°C in a boiling water bath for 5min. Lack of
viability was confirmed by plating 100µl of heat-killed cells on nutrient agar (Oxoid, U.S.A.) and
incubated at 37°C overnight.
3.2.3.3 Comparing PMA and EMA pre-treatment
Following the manufacturer's instructions of using 20mM of PMA (Biotium, U.S.A) and the
recommendations of Nocker and Camper (2006) of using 5mg/ml of EMA as working
43
concentrations, both were dissolved with nuclease-free water to their respective concentrations,
filter-sterilised using a 0.45µm filter and aliquoted into a foil-wrapped microcentrifuge tube for
storage at -20°C. E. coli was grown overnight and a portion heat-killed prior to spiking different
concentrations of 'live' and 'dead' bacteria into ten sterile bottles each containing 100ml of
autoclaved milliQ water. The spiked samples were filtered aseptically through a 0.22µm
nitrocellulose membrane, and treated with either EMA or PMA. Treated membranes were placed
aseptically on a sterile 45mm transparent petri dishes. They were first incubated in the dark at
room temperature for five minutes, followed by exposure to a 500W halogen light source for an
additional five minutes. To prevent degradation of sample due to heat generated from the light
source, the samples were kept on ice during light exposure. After treatment, the membranes were
stored in RapidWater™ Bead Tubes (MO BIO, U.S.A.) at -20°C until ready for DNA extraction.
DNA extraction was performed using RapidWater™ DNA Isolation Kits (MO BIO, U.S.A.)
according to manufacturer‟s instructions. The extracted sample was quantified using qPCR
targeting the E. coli uidA gene.
3.2.3.4 Determining and validating optimum PMA concentration
As per the previous experiment, PMA was dissolved with nuclease-free water to a final
concentration of 1.779mM, filter-sterilised using a 0.45µm filter and aliquoted into a foilwrapped microcentrifuge tube for storage at -20°C. Different PMA concentrations (10µM, 20µM,
30µM, 40µM, 50µM) were added into individually foil-wrapped tubes containing 1ml nuclease
free water. Heat-treated E. coli was mixed with live cultures in defined ratios, with live cells
representing 0%, 25%, 50%, 75% and 100% of the total volume. These mixtures were spiked into
50ml autoclaved milliQ water, prior to filtering through a 0.22µm nitrocellulose membrane. PMA
treatment, DNA extraction and qPCR analysis was performed as described in the previous section.
In addition, the amount of viable bacteria was also determined through serial dilution followed by
spread plating on nutrient agar. All samples were performed in duplicates.
44
To validate the chosen PMA concentration, the same experimental set-up was repeated using
environmental matrix in replacement of milliQ water. Specifically, water was collected from two
different interactive fountains (VC and BJ) within a 1-week period, filtered under aseptic
conditions through a 0.22µm nitrocellulose filter and stored at 4°C. The water was combined and
mixed to homogenisation before autoclaving at 121°C for 15min, and subjected to UV treatment
for 15min. All other aspects of the experiment remain the same.
3.2.4 PCR inhibition
From the literature survey, the easiest way to remove the effect of inhibitory substances is
through dilution of the environmental samples. As such, point samples obtained from BJ, IMM
and VC were filtered and extracted according to the procedures described above. The extracted
samples were diluted 10X and 100X prior to quantification of the Enterobacteriaceae 16S rRNA
gene by qPCR. This gene was chosen because the Enterobacteriaceae group of bacteria is known
to be highly prevalent in environmental samples and the expected high numbers allows for
visualisation of any inhibitory effects more easily (Gavini et al., 1985). Quantification was
performed by comparing against a standard curve using known E. coli concentrations and any
differences in counts obtained between different dilutions were calculated using statistical tools.
3.2.5 Field tests
3.2.5.1 Pathogen detection
Samples were collected from each of the six targeted recreational water facilities (BJ, CS, IMM,
SB, SC, VC). The sampling timeframe was kept within a period of two months to reduce the
confounding effect of seasonal changes. Membrane filtration and DNA extraction was performed
as reported previously. Based on existing literature, six potential pathogens (Aeromonas sp., E.
coli, Legionella sp., Mycobacterium sp., Pseudomonas sp. and Staphylococcus sp.) were
45
shortlisted for detection and enumeration by qPCR, using standards and selective primers as
reported in Section 3.2.2.
3.2.5.2 Evaluating pathogen-indicator relationship
Samples were collected from each of the six targeted recreational water facilities (BJ, CS, IMM,
SB, SC, VC) from January 2012 to April 2013. Membrane filtration, DNA extraction and
chemical testing was performed as previously described. Real-time PCR was used to detect and
quantify the targeted indicators (Enterobacteriaceae, Enterococcus, B. thetaiotaomicron, M.
smithii) and pathogens (Aeromonas, Legionella, Mycobacterium, Pseudomonas). Correlations
between indicator and pathogen were determined by calculating Kendall's tau correlation
coefficient for non-parametric data. Possible indicator-pathogen correlations were further
examined by multiple regression analysis with the indicator as a possible predictor of each
pathogen, controlling for the effects of water parameters including temperature, TDS, salinity, pH,
conductivity, total and free chlorine.
3.3
Results
3.3.1 Comparing the effectiveness of PMA and EMA pre-treatment
Both PMA and EMA have been reported to be effective in differentiating between viable and
non-viable cells and have been successfully applied in combination with qPCR to selectively
quantify viable cells in mixed environmental samples. However studies have also reported that
while these DNA intercalating dyes significantly suppresses counts obtained from dead cells, it
also penetrates live cells and results in the generation of false negative signals in the presence of
live cells (Nocker and Camper, 2006).
In this study, PMA and EMA were evaluated for their potential application in differentiating
between mixed cultures of live and dead E. coli cells. It was found that PMA-treated set-ups
46
showed a signal suppression of more than 6 log scales in the presence of dead cells (Figure 3.4:
P2), demonstrating its effectiveness in reducing the counts obtained by non-viable bacteria. This
is in comparison to EMA-treated set-ups where dead cell signal suppression was less than 4 log
scales and amplification was observed even in the absence of both live and dead cells (Figure 3.4:
E2). Accuracy of quantification of live cells was similar for PMA-treated and EMA-treated setups, which showed a signal suppression of approximately 1-2 log scales in comparison to the
calculated amount of spiked live cells in both cases (Figure 3.4).
Gene targets/100ml
Comparison between
PMA and EMA treatments
1E+09
10000000
10000000
1000000
100000
10000
1000
100
10
1
Dead
Live
qPCR count
P1 P2 P3 P4 P5 E1 E2 E3 E4 E5
Figure 3.4: Comparison between PMA and EMA treatment of viable and heat-killed E. coli mixtures. P1-5 refers to 5 set-ups treated
with PMA while E1-5 refers to 5 set-ups treated with EMA. 'Dead' and 'Live' refers to calculated amount of spiked live and dead cells
respectively while 'qPCR count' is the count obtained from the CT value after qPCR quantification.
3.3.2 PMA optimisation
Optimisation of PMA concentration was performed for both live and dead cultures at a
concentration of 2.8x105 CFU/100ml (confirmed by plate counts), which more closely
corresponds to that found in environmental water samples. Figure 3.5A shows that in live cells,
increased concentrations of PMA up to 20µM resulted in a reduction of bacteria count by about
3-fold as reported from the PMA-qPCR method, before the effect starts to plateau. This is
47
supported by data published by Bae and Wuertz (2009) that there was significant variability in
cell counts at low feces concentration, and suggests a relationship between PMA concentration
and greater likelihood of PMA penetration into undamaged cells when there is lower target
numbers for PMA-qPCR. In heat-killed cells, PMA treatment resulted in a substantial decrease of
bacteria count irregardless of PMA concentration, possibly indicating that a lower PMA
concentration is desirable when working with diluted samples.
Figure 3.5B shows the level of discrimination between variable ratios of live to dead cell cultures
at different PMA concentrations, in comparison to standard culture. Low PMA concentrations
(10µM) were not able to suppress signals contributed by dead bacteria, while high PMA
concentrations (40-50µM) showed evidence of reduced viable bacteria cell count, especially at
higher live cell concentration. In fact, only a PMA concentration of 30µM closely matched the
count that is obtained from standard culture methods, as there was very little deviation in the
higher live cell concentration (50% and 75% live cells) and only a 2-fold decrease at a lower live
cell concentration (25% live cells).
A
Live versus Dead bacteria counts
1.00E+07
CFU/100ml
1.00E+06
1.00E+05
Live
1.00E+04
Dead
1.00E+03
1.00E+02
1.00E+01
1.00E+00
Culture
PMA
10uM
PMA
20uM
PMA
30uM
PMA
40uM
PMA
50uM
48
CFU/100ml
B
Profile of Live/dead mixtures
2.00E+05
1.80E+05
1.60E+05
1.40E+05
1.20E+05
1.00E+05
8.00E+04
6.00E+04
4.00E+04
2.00E+04
0.00E+00
25% live
50% live
75% live
Culture PMA 10uM
PMA 20uM
PMA 30uM
PMA 40uM
PMA 50uM
Figure 3.5: Chart showing the relationship between different PMA concentrations and enumeration of E. coli spiked in pure water.
qPCR of the Enterobacteriaceae specific 16S rRNA gene as well as the standard culture method was performed for A) live and dead
cells, B) Defined live/dead cell mixtures.
3.3.3 Validation of PMA pre-treatment in environmental water matrix
It has been suggested that certain factors found in environmental samples present practical and
theoretical limitations to the application of PMA in the detection of viable cells. For example,
PMA may penetrate viable cells with reversibly damaged membranes, which is likely to be
present in environmental samples. Additionally, turbidity resulting from high levels of suspended
solids or biomass may result in shading and hence inhibition of the PMA-DNA cross-linkage step.
As such, an additional experiment was performed to investigate the effect of factors present in
environmental water samples on the results of the PMA-qPCR analysis method.
Similar to the previous experiment performed on PMA optimisation using MilliQ water (Section
3.3.2), treatment of heat-killed cells using PMA resulted in substantial suppression of bacteria cell
count irregardless of concentration, whereas amplification was observed for the non-treated heatkilled cells (Figure 3.6A), indicating the effectiveness of PMA treatment in removing signals
contributed by dead cells. Counts obtained from the non-treated heat-killed cells were two logfold lower than the amount spiked, suggesting that other factors such as the manner of heat-
49
treatment or the extraction procedures could also have contributed to the lower amounts obtained.
Live-cell counts peak in the mid-ranges of PMA concentration suggesting PMA concentration of
30uM is optimal.
Interestingly, PMA treatment resulted in suppression of qPCR count (approximately two log-fold
units) in environmental samples in comparison to the non-treated control, irregardless of PMA
concentration (Figure 3.6B). These results indicate that certain factors within environmental
samples could interfere with the PMA treatment or downstream DNA extraction procedure thus
leading to anomalous bacterial counts.
A
Live versus Dead bacteria counts
1.00E+07
CFU/100ml
1.00E+06
1.00E+05
1.00E+04
Live
1.00E+03
Dead
1.00E+02
1.00E+01
1.00E+00
Culture PMA PMA PMA PMA PMA
Not
10uM 20uM 30uM 40uM 50uM treated
50
B
Profile of live/dead mixtures
1.00E+07
CFU/100ml
1.00E+06
1.00E+05
1.00E+04
25% live
1.00E+03
50% live
1.00E+02
75% live
1.00E+01
1.00E+00
Culture PMA PMA PMA PMA PMA Not
10uM 20uM 30uM 40uM 50uM treated
Figure 3.6: Chart showing the relationship between different PMA concentrations and enumeration of E. coli spiked in
environmental water matrix. qPCR of the Enterobacteriaceae specific 16S rRNA gene as well as the standard culture method was
performed for A) live and dead cells, B) Defined live/dead cell mixtures.
3.3.4 PCR inhibition
PCR detection methods are essential in this study to detect organisms that are not possible to
culture, however results may be compromised by the presence of inhibitors, including humic acid,
organic matter and clay in environmental water samples. Because the targeted recreational water
sources for this study are mostly sheltered and are less exposed to environmental factors such as
wind and rain, the extent of possible PCR inhibition is not understood.
In this study, the possibility of PCR inhibition in samples collected from interactive fountains was
investigated. A one-way Analysis of Variance test (ANOVA) was used to determine count
difference among different dilutions of DNA extracts obtained from different interactive water
fountain locations. Results showed that there was significant differences only in counts obtained
for IMM: F(2,6) = 20.906, p 1 which explained for around 78%
of the total variance, where PC1 took up 32% and the rest took up between 5 to 15%. The scree
plot showed a sharp change of slope after the PC1 (Figure 4.6), indicating that most of the
variance had been explained by variables in the PC1. Strengths of factor loadings were
determined based the classification of factor loadings by Liu et al. (2003) as 'strong', 'moderate'
and 'weak' corresponding to loading values > 0.75, 0.5-0.75 and 0.3-0.5 respectively. Varimax
76
rotation of the PCs showed moderate to strong positive factor loadings of each bacteria group in
different VF, indicating that these bacteria groups do not cluster and are unlikely to be correlated
with each other (Table 4.3). TDS, salinity, conductivity, total hardness and alkalinity were found
to cluster together in PC1 indicating the presence of an underlying factor (e.g. physical water
quality) governing their correlation. However, these variables only accounted for 32% of the
variance in the data, implying that there were other factors responsible for the difference in
bacterial counts.
77
A
B
Figure 4.5: Temporal patterns of distribution for bacteria markers enumerated using (A) culture and (B) qPCR. Red arrows refer to the date in which 'thorough cleaning' (defined as filter backwash and
dosing of disinfectant) was performed, while green arrows refer to the date in which 'routine cleaning' (defined as clearing of leaf debris and change of fountain water) was done. The blue arrow
indicated a time point meant for 'routine cleaning' which fell on a public holiday.
78
Table 4.2: Regression analyses and correlations of indicator and potential pathogens.
Molecular methods (qPCR)
Enterobacteriaceae
(All time points)
Aeromonas
Legionella
Mycobacterium
Pseudomonas
B
0.498
-1.744
2.097
-0.004
Linear Regression
S.E.
t
0.740
0.674
1.357
-1.286
6.001
0.349
0.025
-0.162
2.321
3.347
-0.377
-0.010
1.203
2.455
5.847
0.014
1.930
1.363
-0.064
-0.712
N.S.
N.S.
N.S.
N.S.
0.444
0.444
0.001
0.056
N.S.
N.S.
N.S.
N.S.
0.025
0.018
0.014
0.019
1.783
0.911
N.S.
N.S.
0.418
0.242
p < 0.005
p < 0.005
p-value
N.S.
N.S.
N.S.
N.S.
Kendall's correlation
p-value
0.402
p < 0.005
0.038
N.S.
0.283
p < 0.05
0.223
N.S.
(Partial time points)
Aeromonas
Legionella
Mycobacterium
Pseudomonas
Culture methods
P. aeruginosa
Coliforms
Enterococcus
Figure 4.6: Scree plot showing a sharp drop after PC1, indicating that most of the variance in the data had been explained by the first
component.
79
Table 4.3: Variable loadings on the first five rotated principle components
Variables
Enterobacteriaceae
Pseudomonas
Aeromonas
Mycobacterium
Legionella
Temperature
TDS
Salinity
pH
Conductivity
Hardness
Total Chlorine
Free Chlorine
Total Alkalinity
VF1
0.094
0.282
0.479
0.446
-0.552
0.214
0.941
0.941
-0.122
0.939
0.850
VF2
-0.370
0.211
0.152
-0.420
0.364
0.828
0.040
0.041
0.325
0.047
-0.164
-0.104
0.182
-0.201
0.648
0.671
-0.579
VF3
0.479
-0.040
0.724
0.004
0.184
0.033
0.218
0.221
-0.335
0.219
0.118
-0.179
-0.251
-0.064
VF4
0.028
0.764
0.004
-0.133
-0.351
0.139
0.151
0.147
-0.752
0.152
0.113
-0.123
-0.199
0.108
VF5
0.023
0.139
0.096
0.594
0.211
0.009
0.018
0.020
0.045
0.014
-0.099
-0.861
0.030
0.321
4.3.3 Marker decay kinetics
Microcosms are often used to investigate environmental processes because they are simulations
of the environmental condition while allowing the researcher to modify and thus study the effect
of individual parameters (Klein et al., 2011; Schulz and Childers, 2011). Typically the data
obtained is based on the measurement of bacteria marker deactivation when subjected to different
forms of treatment, and the results act as an indication of marker behaviour and fate in the
environment. Such comparison of pathogen and indicator decay rates could provide significant
insights into their relationship under the influence of each studied variable.
We suspected that currently tested indicators did not correlate well with pathogen distribution in
the field because of marker persistence issues (differential bacterial die-off/marker decay rates).
As such, a laboratory closed microcosm was set up and monitored over a period of 7 days without
any input of biomass or nutrient source, in order to determine bacterial marker decay rates.
There were two types of survival profiles for pathogens, with Pseudomonas and Aeromonas
showing similar patterns of decay and a decrement of approximately 4 log units (k=1.45 and 1.63
respectively), while Legionella and Mycobacterium exhibited an absence of decay and the former
80
even showed a slight increment of 1 log units (k=-0.25 and 0 respectively). Indicators and
pathogens exhibited different patterns of decay, with Enterobacteriaceae gene target counts
showing an intermediate decay rate (k=0.88) in comparison to the two different pathogen profiles,
while M. smithii appeared to be highly unstable and showed a drastic decline in count to
undetectable levels after 1 day (k=5.28) (Figure 4.7 E-F). Overall, results suggested that there
were variable patterns of decay among different pathogenic markers as well as between indicators
and pathogens. Such differences in marker persistence could be a contributing reason to the
conflicting results of pathogen-indicator relationships found in this study, as well as that reported
in literature (Dorevitch et al., 2010).
81
Figure 4.7: Survival profiles of selected pathogens (A-D) and indicators (E-F). k represents the decay coefficient as calculated by Chick's Law. Error bars have been added to the charts but are too
narrow to be visualised.
.
82
4.4
Discussion
4.4.1 Spatial and temporal variability in bacterial marker distribution
Environmental conditions are often complex and a variety of parameters could possibly introduce
spatial and temporal variation of bacteria marker populations. Such factors include chemical and
physical parameters such as temperature, use of disinfectant or filtering techniques, weather
conditions as well as spatial differences between different contamination source and distribution
pathways. These factors are often ignored in the development of indicators for fecal and non-fecal
contamination despite the fact that they determine the reliability of any indicator-pathogen
relationship observed.
There were no observed difference in indicator and pathogenic marker counts irregardless of
whether they were sampled from the nozzle, reservoir water or the filter region, suggesting that
bacterial populations were well distributed through the entire water system and there was no
significant localisation of any single microbial group. This showed that the measured bacteria
groups were likely to possess similar transport mechanisms within the water system, which is one
of the attributes of an ideal indicator (Section 2.2.2). Interestingly, the results also highlighted the
possibility of the ineffectiveness of the sand filter at removing these indicators and pathogens
from the water, or their constant introduction into the water supply either within the water system
or from the surrounding environment. Considering that most of the measured bacteria were
potentially biofilm-forming (Aeromonas, Pseudomonas, Mycobacterium) or could persist and
regrow in sediments (Enterobacteriacea), it was likely that the contamination source originated
from the water system, however the exact location is difficult to pinpoint without the aid of
microbial source tracking techniques. Knowledge of such data will be useful from a regulatory
standpoint but was less relevant for our purposes.
83
Temporal differences in bacterial marker distribution were present, however did not follow any
significant trend corresponding to the disinfection and cleaning cycle, possibly explaining for the
lack of correlation and predictive power of measured indicators towards pathogens. The absence
of any relationship between the measured indicator and pathogenic markers was further
confirmed through FA/PCA which showed that the measured indicator and pathogen variables
were unable to be clustered and hence correlated together. The overall conclusion obtained from
analysing results of bacteria marker distribution data over temporal scales using simple
correlation, multiple regression and FA/PCA overturned that based on point sampling (Section
3.3.6). Results suggested that in our study, there was an absence of both physical and
microbiological indicators that can act as reliable markers of pathogen occurrence in field settings
when temporal fluctuations of bacterial population are considered, and the association between
Enterobacteriaceae and Pseudomonas/Aeromonas as well as M. smithii/Legionella as observed
from the single-point study was likely due to chance.
To our knowledge, this is the first study that compares indicator-pathogen relationships using the
same markers in both single-point and temporal studies, as such there is no literature to support
the results that we have obtained. However it is reasonable to postulate that environmental
variables such as sunlight and temperature affect bacterial physiology and growth kinetics
differentially (different levels of persistence), and the combined effect is more significant than
that contributed by routine filter backwash, cleaning and disinfection procedures.
4.4.2 Bacterial marker survival and persistence
A laboratory microcosm decay study was established to document the survival profiles and
measure the persistence of indicators and pathogenic markers under conditions mimicking that of
the Bugis Junction water fountain. Pathogens generally have different survival profiles as
indicators, implying that bacterial physiological response and growth is affected differentially
84
under the same conditions of temperature and light/dark cycle. In particular, the rapid decay of
the M. smithii marker is probably a reflection of the bacterium's inability to tolerate aerobic
environments, making it particularly unsuitable to indicate the presence of environmental
pathogens that are aerobic and persist for extended periods in the water column such as those
measured in this study. Enterobacteriaceae, though being shown to possess greater persistence
than Pseudomonas and Aeromonas and less than Legionella and Mycobacterium, is also
unsuitable as differential persistent rates results in uncoupling of the indicator-pathogen
relationship and is likely a major contributing reason to their lack of correlation observed in the
previous study.
It is worrying that in our study Legionella and Mycobacterium are shown to remain viable and
persist for extended periods of time (more than seven days). Both pathogens are highly resilient
organisms that have been shown to colonise and persist in a variety of drinking water supplies
even after treatment and disinfection, and their pathogenicity have been documented well in
humans (Covert et al., 1999; Perola et al., 2003; Borella et al., 2005; Hilborn et al., 2006).
Furthermore, both pathogens have been shown to enter and resuscitate from the viable-but-nonculturable (VBNC) state which impairs the ability to detect them using normal culture techniques
currently employed by monitoring agencies. Their continued presence in recreational water
fountains, and their extended viability and persistence in the water body represents a potential
health risk as yet understudied by regulatory bodies.
Although this study has shown differential persistence of bacterial indicators and pathogens in
recreational water samples, it has not determined the environmental variables which are
responsible. This is especially so for pathogens shown to possess high persistence as
determination of the variables that affect/impede their numbers could lead to more efficient
monitoring or even the development of measures that could suppress natural bacteria populations.
Future work could include laboratory microcosm studies on the isolated effects of sunlight,
85
temperature, UV and predation on marker decay kinetics. Furthermore, the persistence of
Legionella and Mycobacterium has not been well documented as it extends past the length of this
study; it is suggested that future work should include a preliminary study to determine the
timeframe required for these bacterial markers to decay prior to commencing any microcosm
studies. As both organisms are biofilm-forming, it is also possible that selective localisation to
PVP piping in aquatic water systems could protect them from sunlight and other environmental
influences, as such accounting for differential persistence in comparison to indicator organisms;
further research could also include the collection and analysis of swab samples for biofilm
detection.
Existing fecal indicators have been shown to be unsuitable for the prediction of the common
pathogens present in recreational waters from aquatic facilities, however currently there are no
microbiological indicators for non-fecal indicators available. Given that qPCR techniques are
only able to quantify markers of known sequences selectively, screening and discovering novel
indicators suitable for this purpose may prove to be a difficult and almost impossible task using
this method. In fact, technological improvements have now provided us with the tools to detect
and quantify pathogens directly, as such eliminating problems associated with establishing an
indicator-pathogen relationship entirely. Chapter 5 will look into direct pathogen detection
methods using sequencing techniques in selected interactive water fountain samples.
4.5
Conclusion
Tested bacterial indicators and pathogenic markers have been shown to exhibit no statistical
significance in spatial distribution showing that bacterial counts are homogeneous within the
water system. However bacterial counts fluctuate independently of the effects of cleaning and
disinfection schedules, implying either the differential effects they have on bacterial physiological
response or the effect of other uncontrolled variables (possibly of environmental origin) on
86
bacterial count. As a result, no significant, strong and reliable correlations were observed between
indicators and pathogens of concern. This lack of correlation could be due to the differential
persistence of pathogens and indicators in the water column, with pathogens persisting for
extended periods in comparison to indicators. The inability of fecal indicators to predict the
presence of pathogens commonly found in interactive water fountains highlight the need for
alternative measures of water quality in recreational water systems.
87
Chapter 5: Direct pathogen detection and discovery using sequencing
methods
5.1
Introduction
The indicator approach to signal the presence of pathogens was first developed in water quality
monitoring because traditional culture methods did not possess the sensitivity to detect and
identify low concentrations of pathogens directly from water samples. With the advent of
molecular tools, it is now possible to detect a wide range of pathogens in water, and discover
novel and potential pathogens including those that were previously difficult or impossible to
cultivate. These molecular tools include DNA microarrays, real-time PCR and sequencing
techniques, where the majority of methods developed focused on the use of large and wellcurated 16S rRNA databases to identify putative pathogens. DNA microarrays, while being able
to simultaneously interrogate thousands of genes in a single assay, requires sequence information
and does not possess the detection sensitivity required for detecting extremely dilute
concentrations of pathogens commonly found in water samples. On the other hand, while realtime PCR possesses the sensitivity required for direct pathogen detection, it is also limited by
sequence information as well as by the range of pathogens to be studied in a given assay, and is
therefore useful mostly for routine monitoring of targeted pathogens.
Chapters 3 and 4 have described work on testing indicator/pathogen relationships for selected
bacterial groups, and concluded that the relationship did not hold up in field tests conducted on a
recreational water facility. Furthermore, we have found that a number of microbial pathogens and
indicators thought to be commonly found in water bodies were not present in our samples. It was
recognised that there is much to learn about the true diversity of microorganisms, including that
of the pathogens present in recreational waters (NRC, 2004; Pond, 2005). There is hence a
necessity to look at alternative means of accessing water quality from an approach that allows the
88
researcher to investigate the entire spectrum of pathogens present in the water sample.
Sequencing techniques including Sanger sequencing and high-throughput NGS present a solution
to this problem. Traditional Sanger sequencing involves the cloning and sequencing of full or
partial-length 16S rRNA gene in plasmid vectors and the subsequent construction of a clone
library. NGS involves massive sequencing of short 16S rRNA reads, which can then be classified
and identified according to taxonomy. Each method has its advantages and limitations as
reviewed in Section 2.3.3.
This section will focus on pathogen identification and discovery using both the Sanger
sequencing and NGS approach. From each environmental sample, a defined region of the 16S
rRNA gene will be amplified, sequenced and classified based on a total bacteria 16S rRNA
reference database. The diversity and abundance of each sample will be compared and analyzed
according to a reference pathogenic bacteria list.
Sample preparation for 454-sequencing was performed by a fellow colleague, Dr Koh Yiling,
Eileen; all other work described in this section are of author's own.
5.2
Materials and methods
5.2.1 Establishment of 16S rRNA clone library
A clone library was established as detailed in the subsequent subsections, for the following
locations: BJ, IMM, SC, VC. Each location was sampled twice, and membrane filtration and
DNA extraction was conducted as detailed in Section 3.2. Approximately 100 clones from each
environment were screened, sequenced and analysed to form the clone library. A summary of the
methodology is shown in Figure 5.1.
89
16s rRNA (PCR)
amplification
and purification
DNA ligation
Transformation
Verification of
insert
Plasmid
extraction
Picking and
growing of
white colonies
RFLP screening
Sequencing
Phylogenetics
analysis
Figure 5.1: Flowchart summarizing the steps involved in the establishment of each clone library.
5.2.1.1 PCR amplification
Partial length 16S rRNA genes were amplified in a standard PCR reaction using the universal
primers
TX9
(GGATTAGAWACCCBGGTAGTC)
and
1391R
(CCTATCCCCTGTGTGCCTTGGCAGTCTCAG), which amplified the V5-V8 variable region
of the 16S rRNA gene (Figure 5.2). Reagents were obtained from TaKaRa Bio Inc. (Japan)
except for primers and milliQ water. Each 10-µl PCR reaction consisted of the following
components: 0.05µl TaKaRa Ex Taq (5 units/µl), 1µl 10X Ex Taq Buffer, 0.4µl dNTP Mixture
(2.5mM each), 0.4µl forward primer TX9 (10µM), 0.4µl reverse primer 1391R (10µM), 1µl
template DNA (1ng/µl) and 6.75µl milliQ water. PCR reaction was accompanied with a negative
control replacing template DNA with 1µl milliQ water. Thermocycling proceeded with 94°C for
3min, followed by 30 cycles of 94°C for 20s, 52.7°C for 20s and 72°C for 45s, and final
extension at 72°C for 3min. Size and purity of PCR product was evaluated on a diagnostic 1%
(w/v) agarose gel. PCR fragment was purified using High Pure PCR Product Purification kit
(Roche Applied Science, U.S.A.) according to manufacturer‟s instructions.
90
Figure 5.2: Illustration of the 16S rRNA gene. Shaded areas represent V1-V8 variable regions respectively. TX9 and 1391R primers
flank V5-V8 variable regions and amplify a region of approximately 600bp. Image adapted from Patin et al. (2013).
5.2.1.2 DNA ligation, transformation and plasmid extraction
All reagents necessary for performing the ligation reaction was purchased from Promega
(pGEM® -T Easy Vector Systems, Promega, U.S.A.) PCR products were non-directionally
ligated into the multiple cloning region of pGEM® -T Easy vectors by incubating the following
ligation mix in a 16°C water bath overnight: 5µl 2X ligation buffer, T4 DNA ligase, 1µl
pGEM® -T Easy (50ng/µl), 1µl T4 DNA ligase (3 Weiss units/µl) and 3µl template DNA.
Ligation reaction was accompanied with a positive control replacing template DNA with 1µl
Control Insert DNA and 2µl milliQ water.
Preparation of reagents required for transformation, as well as the transformation procedure was
carried out according to Appendix II. Ligation products were transformed into E. coli DH5α
competent cells. Transformants were plated onto LB agar with ampicillin/IPTG/X-Gal (LBXI-A)
plates for blue-white screening and incubated at 37°C overnight.
For each sample (IMM, BJ, SC, VC), 100 white colonies were randomly selected, picked and
grown in LB-ampicillin medium (Appendix II) at 37°C overnight using 10ml culture tubes
(Greiner bio-one, Belgium). Cultures were harvested by decanting into 2ml microcentrifuge tubes
(Axygen, Corning, U.S.A.), pelleted down and stored at -20°C. Plasmid extraction was then
performed using the E.Z.N.A.® Plasmid Miniprep Kit (Omega Bio-Tek, U.S.A.) according to
manufacturer‟s instructions.
91
5.2.1.3 Verification of insert
Presence of insert within each plasmid extract was confirmed through restriction-enzyme (RE)
digestion using EcoRI. All reagents required for RE digest were purchased from Promega
(U.S.A.). The 5-µl reaction mix consisted of the following components: 0.5µl Buffer H 10X
Buffer, 0.1µl EcoRI (12 units/µl), 0.1µl acetylated bovine serum albumin (BSA) (10µg/µl), 2.0µl
plasmid DNA, 2.3µl milliQ water. RE digests were incubated at 37°C overnight. The presence of
an approximately 600bp insert was verified by running the entire 10µl reaction mixture on a 1%
agarose gel.
5.2.1.4 Restriction fragment length polymorphism (RFLP) screening
Each plasmid extract was completely digested in double-digest reactions using TaqI and MspI
restriction enzymes (Thermo Scientific, U.S.A.). Digestion using MspI (0.5µl 10X Buffer Tango,
0.1µl TaqI (10 units/µl), 2µl plasmid DNA, 2.4µl milliQ water) was carried out at 37°C for 1hr,
followed by digestion using TaqI (additional 0.1µl TaqI into each tube) at 65°C for 1hr. DNA
band patterns were resolved by electrophoresing the whole mixture on a 2% (w/v) agarose gel.
Clones were grouped based on similarity of profile (Figure 5.3). From each cluster of plasmid
extracts with similar profiles, a random sample was sent off to a commercial sequencing facility
(AITbiotech, Singapore) for DNA sequencing of the 16S rRNA gene insert.
92
Figure 5.3: Example of different RFLP patterns obtained from double digest of 12 clones within the Science Centre (SC) clone library.
Six unique banding patterns (A-F) were obtained for this particular sample. M: Marker lane.
5.2.1.5 Bioinformatics analysis
The raw 16S rRNA gene sequence reads from the environmental water samples were visually
checked, then trimmed to remove vector and primer sequences. They were then compared with
sequences in the Ribosomal Database Program (RDP) database by using the Seqmatch online
analysis tool (Cole et al., 2008) to determine the phylogenetics affiliation. The results provided a
guide to determining which 16S rRNA gene sequences were chosen for phylogenetics analysis.
Both the environmental and reference sequences were subjected to multiple sequence alignment
using MUSCLE software (Edgar, 2004). The aligned sequences were used to generate a
phylogenetic tree by employing the Maximum Likelihood (ML) approach using MEGA5 with the
Kimura 2-parameter model and default settings for the other parameters (Tamura et al., 2011).
Nodal support was estimated using the bootstrap method conducted with 1000 repetitions.
Phylogenetic trees were visualised using TreeGraph2 software (Stöver and Müller, 2010).
5.2.2 454-sequencing
The same samples analysed using the clone library approach were selected for high-throughput
454-sequencing.
93
5.2.2.1 PCR amplification and purification
Sample preparation was according to instructions provided by the Waikato DNA Sequencing
Facility (WDSF, http://bio.waikato.ac.nz/sequence/) at the University of Waikato, Hamilton, New
Zealand.
Universal
primers
TX9
(GGATTAGAWACCCBGGTAGTC)
and
1391R
(CCTATCCCCTGTGTGCCTTGGCAGTCTCAG), which amplified the V5-V8 variable region
of the 16S rRNA gene were used for the PCR amplification step. Each 30-µl PCR reaction
consisted of the following components: 0.3µl TaKaRa PrimeStar High-fidelity Enzyme (2.5U),
6.0µl 5X Ex Taq Buffer, 3.0µl dNTP Mixture (2.5mM each), 1.2µl forward primer TX9 (10µM),
1.2µl reverse primer 1391R (10µM), 10ng/µl template DNA and remaining volume made up with
milliQ water. Thermocycling proceeded with 94°C for 3min, followed by 24 cycles of 94°C for
20s, 52°C for 20s and 72°C for 45s, and final extension at 72°C for 3min. The PCR product
obtained (~605bp) was visualised using a 2% (w/v) TAE gel, and the band excised and purified
using High Pure PCR Product Purification kit (Roche Applied Science, U.S.A.) according to
manufacturer‟s instructions. A second round of PCR was performed under the same PCR
conditions for 10 cycles using primers adapted with barcode sequences.
5.2.2.2 Sample clean-up, quantification and quality check
Clean-up of the PCR product was performed using Agencourt AMPure XP beads (Beckman
Coulter, U.S.A.) following manufacturer's instructions. DNA quantification was performed using
Quant-iT™ PicoGreen (Invitrogen, U.S.A.) using the protocol described in Appendix IV. Quality
verification was performed using a diagnostic 2% (w/v) TAE gel showing a single band (with no
primer dimers) corresponding to the expected size. The final mixture was sent out to 1st BASE
(Singapore) for pyrosequencing using the Roche 454 GS-FLX platform.
94
5.2.2.3 Sequence analysis and phylogenetics assignment
The open-source program Mothur (Schloss et al., 2009) was used to perform all bioinformatics
analysis. Sequences were processed by removing sequences containing more than 1 ambiguous
base followed by trimming off barcodes and adaptor sequences. Sequences were aligned using the
Silva reference alignment (http://www.mothur.org/wiki/Silva_reference_alignment) followed by
screening and filtering of the alignment to remove gaps, missing data and produce a fixed
alignment length. Sequencing error was further reduced by performing pre-clustering of sequence
data to merge sequence counts within 2bp of a more abundant sequence. Chimera checking was
performed using the chimera.uchime command, and sequences affiliated with chloroplasts,
mitochondria, eukaryotes and unknowns (sequences that cannot be classified at the Kingdom
level) were removed.
A distance matrix based on the processed sequences was generated with a cutoff of 0.15,
followed by clustering into operational taxonomical units (OTUs) and determination of the
majority consensus taxonomy. Alpha-diversity of each sample was calculated by generating
collector's curve of the Chao1 and Inverse Simpson richness estimator and rarefaction curves.
5.2.2.4 Sequence identity calculation
To compare sequences with those of known pathogenic bacteria (Table 5.1), representative
sequences were retrieved from Genbank and combined with sequences from this study into a fasta
file. The web-based version of Clustal (hosted by The European Bioinformatics Institute,
http://www.ebi.ac.uk/Tools/msa/clustalo/) was used to generate sequence alignments using the
default settings, as well as to calculate the identities between the sequences in the alignment. The
number of reads corresponding to each pathogenic bacteria sequence was represented by a
heatmap generated using Microsoft Excel.
95
Table 5.1: Human pathogenic bacteria, disease and accession number of reference sequences.
Genus
Pathogenic species
Disease
Accession No.
Aeromonas
Aeromonas veronii
Aeromonas hydrophilia
Arcobacter butzleri
Septicemia
Gastroenteritis
Diarrhea
Abdominal pain
Cutaneous anthrax
Pulmonary anthrax
Gastrointestinal anthrax
Nausea, vomiting and diarrhea
Whooping cough
Lyme disease
Brucellosis
Acute enteritis
Community-acquired respiratory
infection
Nongonococcal urethritis
Lymphogranuloma venereum
Trachoma
Inclusion conjunctivitis of the
newborn
Psittacosis
Botulism
Pseudomembranous colitis
Gas gangrene
Acute food poisoning
Anaerobic cellulitis
Tetanus
Diphtheria
Opportunistic infections
DQ029351.1
AB610604.1
FJ968634.1
Arcobacter
Bacillus
Bacillus anthracis
Bacillus cereus
Bordetella
Borrelia
Brucella
Campylobacter
Chlamydia
Bordetella pertussis
Borrelia burgdorferi
Brucella abortus
Campylobacter jejuni
Chlamydia trachomatis
Chlamydophilia
Clostridium
Chlamydophilia psittaci
Clostridium botulinum
Clostridium difficile
Clostridium perfringens
Clostridium tetani
Corynebacterium
Enterobacter
Escherichia
Corynebacterium diphtheriae
Enterobacter aerogenes
Enterobacter cloacae
Enterococcus faecalis
Enterococcus faecium
Escherichia coli
Francisella
Haemophilus
Francisella tularensis
Haemophilus influenzae
Helicobacter
Helicobacter pylori
Klebsiella
Klebsiella pneumoniae
Enterococcus
Nosocomial infections
Urinary tract infections
Diarrhea
Meningitis in infants
Traveller's diarrhea
Diarrhea in infants
Hemorrhagic colitis
Hemolytic-uremic syndrome
Tularemia
Bacterial meningitis
Upper respiratory tract infections
Pneumonia, bronchitis
Peptic ulcer
Risk factor for gastric carcinoma
and gastric B-cell lymphoma
Pneumonia
Urinary tract infections
Septicemia
Ankylosing spondylytis
Soft tissue infections
AY643481.1
AJ629413.1
AF366576.1
AF477988.1
EF192471.1
AY628389.1
DQ019301.1
AB285329.1
X68315.1
X73450.1
DQ196135.1
X74770.1
X84248.1
AB004750.1
Y17665.1
AB154827.1
AY692451.1
EU118103.1
AY968238.1
AY613478.1
AY593986.1
U33121.1
96
Legionella
Legionella pneumophila
Leptospira
Listeria
Mycobacterium
Leptospira interrogans
Listeria monocytogenes
Mycobacterium leprae
Mycobacterium tuberculosis
Mycobacterium ulcerans
Mycoplasma pneumoniae
Neisseria gonorrhoeae
Neisseria meningtidis
Mycoplasma
Neisseria
Pseudomonas
Rickettsia
Salmonella
Pseudomonas aeruginosa
Rickettsia rickettsii
Salmonella enterica
Serratia
Shigella
Staphylococcus
Serratia marcescens
Shigella sonnei
Staphylococcus aureus
Staphylococcus epidermidis
Staphylococcus saprophyticus
Streptococcus
Streptococcus agalactiae
Streptococcus pneumoniae
Streptococcus pyogenes
Treponema
Treponema pallidum
Vibrio
Yersinia
Vibrio cholerae
Yersinia pestis
Legionnaire's Disease
Pontiac fever
Leptospirosis
Listeriosis
Leprosy
Tuberculosis
Mycoplasma pneumonia
Gonorrhea
Ophthalmia neonatorum
Septic arthritis
Meningococcal disease
Waterhouse-Friderichsen
syndrome
Pseudomonas infections
Rocky mountain spotted fever
Typhoid fever type salmonellosis
(dysentery, colitis)
gastroenteritis and enterocolitis
Nosocosmial infections
Bacillary dysentery
Staphylococcus infections
Infections
of
implanted
prostheses
Cystitis in women
Meningitis and septicemia in
infants
Endometritis
in
postpartum
women
Opportunistic infections with
septicemia and pneumonia
Scarlet fever
Rheumatic fever
Necrotizing fasciitis
Syphilis
Congenital syphilis
Cholera
Bubonic plaque
Pneumonia plaque
AF129523.1
AY995728.1
AJ535697.1
X53999.1
X52917.1
X58954.1
AF132741.1
AM921674.1
AY735364.1
AY548952.1
DQ150682.1
EF579646.1
M59160.1
EF032687.1
EF463060.1
AY699287.1
L37596.1
AB112407.1
AJ617796.1
NR_028598.1
M88726.1
AY494843.1
AJ232223.1
Data obtained from Ye and Zhang (2011).
5.3
Results
5.3.1 Phylogenetic characterisation using clone libraries
Clone libraries of DNA samples obtained from BJ, IMM, SC and VC were established to identify
the diversity of bacterial communities found within, and visualised by phylogenetic trees
corresponding to samples from each location. Sequences from the clone library revealed that
97
bacterial diversity were mostly homogeneous regardless of location, with most clones clustering
near or within Alpha-proteobacteria, Beta-proteobacteria, Gamma-proteobacteria, Bacteroidetes
or Actinobacteria (Figure 5.4). Proportions of unique sequences clustering within or near each
taxon differed depending on location, but generally Proteobacteria were most well-represented,
appearing in all locations sampled (Figure 5.5).
There were several unclassified sequences within the IMM, SC and VC library, possibly because
the assignment was based on the sequences in RDP curated database mostly consisting of
cultured species, which could have omitted certain environmental isolates that were uncultured
(Figure 5.4). Through a BLAST search, two unclassified clades corresponded to Domain Bacteria
and were likely to be either Verrucomicrobia (clade A) or Proteobacteria (clade C). Clade B
identified strongly with freshwater Eukaryotae while some species of clade D was suggested to be
phototropic but the taxonomic status (prokaryotic or eukaryotic) could not be determined. In
particular, there was a significant proportion of bacteria with sequences classifying into Clade B
and D within the SC and VC libraries (clade B: 41.5% and 20.0% respectively; clade D: 21.5%
and 16.7% respectively).
98
A
Figure 5.4: Phylogenetic tree of sequences obtained from water samples collected from the following locations: (A) Bugis Junction, (B) IMM, (C), Science Centre and
(D) VivoCity. Clones were created using amplification products from the 16S rRNA gene. Representatives were selected based on top hits from BLAST and clustered
using a maximum likelihood approach. An archaea 16S rRNA gene from Haloarcula marismortui was used to root the trees. Bootstrap values are shown on each
branch.
99
Figure 5.4 continued.
B
100
Figure 5.4 continued
C
101
Figure 5.4 continued
D
102
100%
90%
80%
Undetermined
70%
Firmicutes
60%
Cyanobacterium
50%
Actinobacterium
40%
Bacteroidetes
30%
Gamma-proteobacterium
Beta-proteobacterium
20%
Alpha-proteobacterium
10%
0%
BJ
IMM
SC
VC
Figure 5.5: Proportions of unique sequences found in each taxon.
5.3.2 Taxonomic assignment of sequences from 454-sequencing
In total 87089 sequences were obtained from all four samples in a single 454-pyrosequencing run, where
15230 sequences were unique. After quality checking, alignment and taxonomy assignment, the number
of sequences dropped to 57221 with 15127 from BJ, 22917 from IMM, 13853 from SC and 5324 from
VC.
Taxonomic assignment of sequences was performed at the phylum/class level by obtaining the top 50
OTUs and plotting the phylum/class against the number of reads. The bacterial community composition
within most of the locations was characterised predominantly by Beta-proteobacteria, Alpha and Gammaproteobacteria as well as Bacteroidetes (Figure 5.6). The data also detected a number of rare phyla
(Acidobacteria, Chlamydia, Clostridia, Delta-proteobacteria and Verrucomicrobiae) which was
overlooked by the classic Sanger sequencing approach. On the other hand, the bacterial population within
Science Centre water samples was markedly different from the other three locations with a large
103
proportion of unclassified sequences (80% versus 3-15% respectively). In addition, there was very few
Alpha (0.7% versus 2-8%), Beta (0.3% versus 40-60%) and Gamma-proteobacteria (0.08% versus 1530%) in Science centre bacteria population as compared to that from other locations.
104
A
B
C
D
Figure 5.6: Taxonomic classification of bacteria at the phylum/class level within (A) Bugis Junction, (B) IMM, (C) Science Centre and (D) VivoCity.
105
5.3.3 Within-sample diversity analysis
Collector's curves were generated to quantify the alpha-diversity (within-sample diversity). Chao1's
estimate curves showed that indices of diversity and evenness differed significantly among locations. BJ
and IMM samples had relatively even species richness with the presence of some rare species (as shown
by the steep and continued increase in slope), while SC and VC samples was dominated by only a few
species and repeated sampling only yielded few new species as shown by the leveling of the curves
(Figure 5.7A). This could be attributed to the SC sample being dominated mostly by unclassified
sequences (10995 out of 13853 reads) while VC sample was dominated by beta-proteobacteria classified
up to the family level (3142 out of 5324 reads classified to Oxalobacteraceae). Plotting the Inverse
Simpson's index against the number of sequences sampled showed that unequal sampling did not strongly
influence the diversity estimate (Figure 5.7B).
Rarefaction analysis revealed that BJ and IMM samples exceeded 1000 OTUs while SC and VC libraries
barely approached 200 OTUs, indicative of the greater species diversity found in the former two samples
(Figure 5.8). Individual libraries also appeared to vary in richness in concordance with collector's curve
results. Rarefaction curves corresponding to BJ, IMM and VC samples suggested that these locations
seemed not to have been sampled to completion, which is indicative of insufficient sequencing coverage.
106
A
B
Figure 5.7: Collector's curves obtained for samples from BJ, IMM, SC and VC using the (A) Chao1 and (B) Inverse Simpson diversity estimator.
107
Figure 5.8: Rarefaction analysis of observed richness in bacterial communities sampled from BJ, IMM, SC and VC.
108
5.3.4 Comparison against pathogen database
To find out the diversity of pathogenic populations in the samples, sequences from this run were
compared with representative 16S rRNA gene sequences from GenBank. In total 3772 sequences,
accounting for 6.59% of total sequences, were found to correspond to known pathogenic bacteria with a
large proportion of them (5.49% of total sequences) having identities of over 99% (Table 5.2).
Out of the 34 genera that were targeted, 14 occurred in our water samples. Pseudomonas and
Mycobacterium were present in all four locations in high abundance while Neisseria, Bacillus,
Aeromonas, Chlamydia and Staphylococcus were found in BJ and IMM (Figure 5.9). Exposed recycled
water systems (BJ and IMM) were associated with greater diversity as compared to sheltered location (SC)
and open wading pool (VC). Among the four locations, BJ was observed to possess the greatest
pathogenic diversity with 13 different pathogenic genera, where Pseudomonas was the dominant
population accounting for 96.6% of pathogenic bacteria present in the sample. Pathogenic genera were
more evenly distributed in IMM samples; in addition it was the only sample that harboured Rickettsia.
Even though the nature of the water facility is different in SC and VC, the profile of harboured pathogenic
genera were quite similar, suggesting the existence of factors that select for predominantly Pseudomonas
and Mycobacteria in SC and VC.
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Table 5.2: Identity and amount of sequences corresponding to potentially pathogenic bacteria.
Identity to known pathogen
sequences
>99%
97-99%
80% unclassified sequences). In fact, most metagenomics
analysis of environmental samples have uncovered significant proportions of unclassified microbial
diversity, of which a fraction is reported to overlap with the rare biosphere where there is little
understanding of their ecological and metabolic roles (Lynch et al., 2012). Phylogenetics analysis of
the clone libraries recovered a number of novel clades of which comparison with top BLAST hit
111
results suggested among others, the existence of eukaryotic-like sequences (clade B). Although these
results cannot be corroborated with those from the 454-sequencing libraries, it is reasonable to
postulate the existence of freshwater eukaryotic microorganisms (e.g. unicellular algae and protozoa)
in our samples as their presence in aquatic habitats have been reported in literature (Medinger et al.,
2010; Kermarrec et al., 2013). However, the eukaryotic ribosome is not as well characterised as the
prokaryotic ribosome, as a result sequence representation in public databases is not as extensive,
making identification difficult.
Despite the presence of similar bacteria phyla within all the sampled interactive water fountains, the
relative abundances of dominant taxa were markedly different in the Science Center library as
compared to the other target locations, with the SC library showing an obvious paucity of identified
reads. Due to the small sample size of this study, it could not be determined whether this anomalous
profile represents an outlier, or a true difference between water obtained from SC and from malls (BJ,
IMM, VC). Nonetheless, it is postulated that this difference in community structure could be real as
results from Chapter 3 (Figure 3.8A), which illustrated microbial counts obtained from repeated
sampling, also showed an absence of most bacterial markers within the SC samples. Considering that
the water supply to the Science Center water playground is the same as that supplied to the other
recreational water facilities, differences in community structure could only be explained by
environmental influences specific to this particular fountain. There are a myriad of putative
explanations e.g. chlorine dosing, localised weather conditions, bather population; however all of
these factors are shared by other measured recreational sites and any single factor is unlikely to result
in the obvious disparity. In fact, the clone library approach suggested the majority of reads in the
unclassified sector identified to eukaryotic-like and phototrophic (possibly algal) sequences, making it
reasonable to wonder whether it was due to overgrowth of eukaryotic populations within this
particular location. Eukaryotic/prokaryotic interactions in aquatic environments are substantially
documented in literature and it is possible that competition or predator/prey relationships could have
result in the observed community structure (Sherr and Sherr, 2002; Pernthaler, 2005). The potential
presence of eukaryotic sequences should be considered when attempting to accurately characterise the
112
microbial community in samples obtained from similar environments. On the other hand, taking the
study in this direction implies changes in methodology as the 16S rRNA marker has limited
phylogenetic resolution for eukaryotes and alternative phylogenetic markers such as 18s or 28s rRNA
should be explored (Pruesse et al., 2007). Furthermore, the DNA extraction method used in this study
is optimised for bacterial cells and may not work as efficiently on eukaryotes which could possess cell
walls.
5.4.2 Pathogen discovery and detection
Using 454-sequencing and comparison against a database of known pathogens, almost half of the
pathogenic genera targeted (14 out of 34) were found in our samples, where only three (Pseudomonas,
Mycobacterium, Aeromonas) had been previously targeted using real-time PCR. In fact, several of the
highlighted pathogenic groups are instead associated with human exposure (Neisseria, Streptococcus)
or exist as intracellular parasites (Chlamydia, Rickettsia). These pathogens are not known to be
waterborne, nor are they listed on the USEPA or WHO watchlists as microbial contaminants to be
monitored within our drinking and recreational water supplies. As such, it is intriguing to discover
these pathogenic genera in our water samples, and we believe that the risk of exposure to them have
so far been understudied. Furthermore, the ubiquitous presence of Pseudomonas and Mycobacterium
in our samples is testament of their abundance in the environment and resistance to a variety of
environmental hazards including disinfection procedures. While it is not appropriate to suggest that all
tested recreational water fountains have been compromised, their existence should raise some
concerns as they could grow to present a health risk when provided with the correct set of conditions.
It also suggests that current methods of disinfection and cleaning are insufficient in keeping our
recreational water facilities free of these potentially pathogenic organisms.
On the other hand, the limitations of this study should be considered in light of the results: 1) PMA
treatment was not performed as the protocol has not been optimised for 454-sequencing, hence no
inferences can be made about the viability of these pathogenic groups; 2) Not all species within a
single pathogenic genera are pathogenic, hence presence of these pathogenic genera does not
113
necessarily translate to a higher risk of disease; 3) 454-sequencing is a semi-quantitative approach and
the abundance of each pathogenic group can only be compared in relation to each other; no inferences
can be made about the true number of pathogenic organisms present within the samples. This is
especially in consideration of the fact that 454-sequencing has a lower sensitivity threshold in
comparison to qPCR (possibly explaining for the absence of Aeromonas in the VC and SC libraries
when 454-sequencing was performed). Accurate pathogen counts are important as the minimum
infective dose should be accounted for when determining microbial risk; 4) the small sample size
limits the generalisability of this study to only the few interactive fountains that were tested; different
pathogen compositions and trends may emerge with an increased sample size.
Despite being a preliminary method which requires further modification and optimisation, the
approach we have used in this study is valuable in discovering and detecting pathogens directly from
recreational water facilities, furthering our knowledge on the bacterial and pathogenic diversity
present. However, we believe that there is still value in developing indicators to signal the presence of
potential pathogens in our water supplies. This is because the high start-up cost and technical
expertise required makes running NGS on a routine basis impractical for monitoring purposes, and it
is not feasible to develop large numbers of pathogenic markers to target all identified pathogens, even
by using multiplex procedures. On the other hand, we would like to suggest a departure from the
traditional indicator-pathogen paradigm, as we believe that NGS technology could be used as
screening tools to select for potential candidate indicator organisms based on a number of
characteristics (e.g. resistance to disinfectants). These indicators could be normal bacteria, but could
also be pathogens (e.g. Pseudomonas and Mycobacterium), as such removing the need to establish
any indicator-pathogen relationships. In this case their presence will highlight problems in water
quality management e.g. inadequate disinfection procedures, and not the prediction of any particular
pathogen. Subsequently for routine monitoring purposes, real-time PCR should still be used to
quantify these discovered indicator markers, and only in the presence of a hit, should direct pathogen
detection be performed using a similar approach to our study in order to elucidate the causative
pathogen identity.
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5.5
Conclusion
A preliminary overview of bacterial diversity using clone library construction and 454-sequencing
uncovered the existence of a number of dominant phyla including Proteobacteria and Bacteroidetes
within our samples, suggesting that the bacterial community composition of recreational water
samples is similar to that found in most natural environments. Pathogenic diversity is also high in two
of the four water samples tested, and several genera has been associated with human transmission and
are of clinical relevance. The prevalence of Pseudomonas and Mycobacterium in recreational water
samples could signal a potential health risk that has previously gone undetected; such data is useful
from a regulatory standpoint when conducting risk assessments. The methods described in this study
has been used to perform a preliminary characterisation of both the bacterial and pathogenic diversity
present in recreational water samples, however more optimisation work is required to confirm the
presence of viable, pathogenic organisms.
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Chapter 6: Conclusions
Microbial indicators have been widely used to indicate the occurrence of pathogens in contaminated
water supplies. With technological advancements and increased understanding of microbiology at the
molecular level, we are now able to measure existing indicators as well as develop novel indicators
using faster and cheaper methods. However, there are still numerous issues that need to be
investigated in order to rapidly and accurately identify recreational water that has been contaminated
with pathogens; these issues include the detection and differentiation of viable bacteria from dead
ones, problems in accurately enumerating cells using molecular techniques in the presence of
inhibitory compounds, problems in establishing reliable indicator-pathogen relationships due to the
need of both indicator and pathogen to possess similar persistence, fate and transport levels, etc.
Despite knowing these limitations coupled with a lack of understanding of their suitability in
recreational waters, regulatory bodies worldwide are still applying the indicator approach to monitor
microbiological water quality.
In Chapter 3, we have investigated and validated the method attributes of using molecular techniques
in the development and use of indicator markers for recreational water monitoring. We have
discovered that simple dilution is sufficient to reduce the effect of PCR inhibitors in our samples. We
have determined an optimum PMA concentration as a pre-treatment to efficiently reduce the
amplification of dead cells, however at the extent of suppressing some live cell counts in
environmental matrices. It could pose problems in detection sensitivity when developing qPCR assays;
however should be of less consequence in routine monitoring of indicators which should be present in
large amounts within water samples.
In Chapter 4, we have looked at the biological attributes of existing indicators and hence determined
their applicability in the context of monitoring recreational waters from aquatic facilities. We have
discovered that there seems to be an absence of most existing fecal indicator bacteria and associated
fecal pathogens in recreational water systems. Instead, the pathogens which are detected are
associated with the surrounding environment and could possibly be endemic within the water system.
116
These pathogens are highly resistant to environmental hazards including disinfection and cleaning
procedures, and a number occur in all the locations that were analysed. They also exhibit prolonged
persistence despite the lack of external nutrient input. The remaining fecal indicator bacteria were not
able to predict these pathogens accurately even though they exhibit similar transport kinetics within
the water system. This uncoupling of indicator-pathogen relationship not only likely reflects different
source inputs, but could also be due to differential persistence levels.
Considering the advances in molecular technology and the subsequent development of cost-effective
solutions to identify bacterial markers directly, we have studied the feasibility of direct pathogen
detection using sequencing techniques in Chapter 5. We have found that pathogen identification in
recreational water samples using NGS is possible, and have in the process discovered several other
potentially pathogenic genera that were previously untargeted in recreational water monitoring. We
have also found that while the bacterial community composition is roughly consistent across most
samples with a number of dominant phyla, the presence and distribution of pathogenic genera is
uneven; however we are unsure of the factors that contribute to this effect. The results of this study is
preliminary and should be considered together with other complex issues such as the need for
investigations to confirm health risk, as well as source identification and mitigation issues within the
entire monitoring framework.
The current indicator system is indeed flawed; however there is still value in holding on to the
indicator approach as direct pathogen detection using sequencing technology currently do not possess
the speed, logistical feasibility and sensitivity required in routine screening and monitoring procedures.
In fact, direct pathogen measurement could be undertaken as a confirmatory procedure only when
screening indicators persist at high levels. NGS is a promising technique in the development of better
indicators that correlates with pathogens in terms of contamination sources and survival or transport
characteristics, however this field is still in its infancy and most research conducted thus far are of an
exploratory nature. Furthermore, there is currently little research performed on strategic sampling and
concentration techniques optimised for NGS protocols. Nonetheless, we are confident that the
adaptation of NGS technology is a feasible approach both in terms of direct pathogen detection and
117
possibly also for indicator development. We foresee that it will change the current concept of the
indicator-pathogen system, as well as revolutionise the entire field of water quality monitoring.
118
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Appendix I
KCl-HCl acid buffer (10X):
A. 2.0M KCl (14.9g in 100ml distilled water)
B. 2.0M HCl (16.7ml conc. HCl in 100ml distilled water)
Mix 18 parts of (A) with 1 part of (B) to prepare 10X acid buffer. Store at room temperature.
Acid treatment procedure:
1) In a 2.0ml micro-centrifuge tube, add 10X acid buffer to water sample in a 1:10 ratio
(25µl buffer to 250µl sample) and mix by vortexing.
2) Incubate the acidified suspension at room temperature for 15min, before plating onto
BCYE and BCYE-GVPC agar.
132
Appendix II
Transformation
Luria-Bertani (LB) broth:
5g
Yeast extract
10g
Tryptone
10g
NaCl
1L
Deionized water
Autoclaved at 121°C for 15mins.
LB agar:
Additional inclusion of 15g granulated agar in 1L LB broth and autoclaved at 121°C for 15mins.
LB-A agar:
A. 100mg/ml Ampicillin (100mg in 1ml distilled water, filter-sterilised)
To every 200ml LB agar, autoclaved and cooled to 55°C, add 200µl (A) into the agar. Swirl to
mix and pour into petri dishes. Plates can be stored up to 1 week.
LB agar with ampicillin/IPTG/X-Gal (LBXI-A):
A. 100mg/ml Ampicillin (100mg in 1ml distilled water, filter-sterilised)
B. 100mg/ml isopropyl-1-thio-β-D-galactopyranoside (IPTG) (100mg in 1ml distilled water,
filter-sterilised)
C. 50mg/ml 5-bromo-4-chloro-3-inodlyl-β-D-galactopyranoside (X-gal) (50mg in 1ml
dimethyl sulfoxide [DMSO])
133
To every 200ml LB agar, autoclaved and cooled to 55°C, add 200µl (A), 200µl (B) and 100µl (C)
into the agar. Swirl to mix and pour into petri dishes. Plates can be stored up to 1 week.
Procedure:
1) To 50µl E. coli DH5α competent cells in a 15ml Falcon tube, add 1µl of ligation reaction.
Incubate on ice for 20min.
2) Heat shock the tubes for 45s using a water bath set to 42°C. Return the tubes to ice for
2min.
3) Add 900µl LB broth and incubate at 37°C on a shaking incubator for 1hr.
4) Centrifuge tube and remove 800µl supernatant. Resuspend transformants in remaining
supernatant. Plate 100µl of transformants on LBXI-A plates and incubate plates
overnight at 37°C.
Colony growing:
LB broth with ampicillin:
To each culture tube containing 3ml LB broth, autoclaved and cooled to 4°C, add 6µl of
100mg/ml filter-sterilised Ampicillin.
Procedure:
1) Using a sterile autoclaved wooden toothpick (available at departmental stores), touch the
tip of the toothpick to a white colony.
2) Drop the toothpick into culture tube containing 3ml LB-ampicillin broth. The tip of the
toothpick in contact with the white colony should be fully immersed in the broth.
3) Incubate the culture tube at 37°C on a shaking incubator overnight.
134
Appendix III
Genomic DNA extraction
Reagents:
-
1.5ml screw-capped tubes containing 0.3g autoclaved glass beads (Ø 0.1mm)
-
Tris-SDS (prepared by mixing 250ml of [200mM Tris-HCl, 80mM EDTA adjusted to pH
9.0] and 50ml of 10% SDS)
-
Chlorofoam/isoamyl alcohol/ethanol (prepared in the ratio of 80:4:16)
-
3M Sodium acetate adjusted to pH 5.2
-
Isopropanol (100%)
-
Ethanol (70%)
-
Tris-EDTA (TE) buffer adjusted to pH 8.0
-
1ml of culture (containing 108 CFU/ml) to be extracted
Procedure:
1) Pellet the cells by centrifugation and decant the supernatant.
2) Add 600µl Tris-SDS and resuspend the cells. Transfer the suspension into a 1.5ml screwcapped tube with pre-added glass beads.
3) Disrupt the bacteria cells by shaking the tube vigorously using a Mini Beadbeater
(BioSpec Products, U.S.A.) for 30s. Centrifuge at 4°C for 5min.
4) Transfer the supernatant into a new 1.5ml screw-capped tube. Add an equal volume of
chlorofoam/isoamyl alcohol/ethanol. Vortex at high speed for 45s. Centrifuge at 4°C for
5min.
135
5) Transfer 500µl of the top aqueous portion into a new 2.0 microcentrifuge tube. Add 50µl
of sodium acetate, followed by 600µl of isopropanol. Mix by inversion. Centrifuge at 4°C
for 5min.
6) Decant the supernatant. Add 1ml of ethanol (70%). Centrifuge at 4°C for 5min.
7) Decant the supernatant. Air-dry the DNA pellet at room temperature overnight.
8) Resuspend the dried pellet with 200µl TE buffer.
9) Aliquot the DNA extract into 10µl volumes and store at -20°C until use.
136
Appendix IV
Standards preparation
The standard curve consisted of 100ng/ml, 50ng/ml, 25ng/ml, 12.5ng/ml, 6.25ng/ml, 3.13ng/ml,
1.56ng/ml, 0625pg/ml, 312.5pg/ml and 0ng/ml standards. DNA stock (100µg/ml) was first
diluted to working concentration (2µg/ml), which was in turn diluted down to form the first
standard (100ng/ml) using 1X TE buffer as diluent. Subsequent standards were obtained by 2-fold
serial dilutions using 1X TE buffer as diluent.
Sample analysis
1X Picogreen was prepared from the stock Picogreen reagent through a 200X dilution using 1X
TE buffer. Samples were also diluted 100X using 1X TE buffer. 100µl of each DNA template
(standard or sample) was loaded into each well of a 96-well flat bottom transparent UV
microplate (Corning, U.S.A.) pre-loaded with 100µl 1X Picogreen solution. Each
standard/sample was analyzed in duplicates.
Measurement
DNA fluorescence was measured using a fluorescent microplate reader at 480nm excitation and
520nm emission wavelengths. Fluorescence value of the reagent blank was subtracted from each
of the samples. The standard curve was generated by plotting fluorescence against standard
concentration, and sample concentration interpolated from the best-fit line. The assay was
repeated if the R2 value of the standard curve was lower than 0.98.
137
[...]... correlation and predictive powers of FIB against pathogens in environmental waters in spatial and temporal studies, and hence determine the applicability of current indicators in Singapore recreational waters, and 3) To develop alternative methods of assessing recreational water quality, should the current indicators be found to be unsuitable 1.5 Scope of study Because the field of recreational water microbiology... increase in the popularity of recreational activities that involve contact with water In Singapore, government initiatives have resulted in a redesign of our waterways to incorporate various lifestyle attractions including kayaking and windsurfing activities (PUB, 2013) There is also a trend in the increasing use of indoor water recreation such as commercial spa pools, Jacuzzi and interactive water playgrounds... pneumonia in hospital settings (Dembry et al., 1998), and has been documented to cause cerebrospinal infections and bacterimia (Fick, 1992) In healthy individuals, P aeruginosa frequently causes ear and skin infections, and has also been linked to infections of the urinary-tract and gastro-intestinal tract P aeruginosa-induced infections are notoriously difficult to treat, associated with peculiar clinical... pools, interactive fountains) and untreated (reservoir) waters In 2007 and 2008, a total of 134 outbreaks associated with recreational water was reported, resulting in at least 13,699 cases of illnesses, in which 40% of outbreaks comprised of dermatological illnesses, acute respiratory conditions as well as combined illnesses The main etiological agents in these cases were of bacterial origin, which included... marine waters (Charoenca and Fujioka, 1993, 1995) 2.2 Indicator approach As awareness increased about disease transmission by water, there was a growing realisation of the importance of monitoring water supplies in order to protect public health This section describes the history of recreational water monitoring practices including an explanation of the indicator approach and the guidelines that Singapore... Alternative indicators The vast majority of alternative indicators proposed have focused on markers for fecal contamination, and they include fecal anaerobes, chemical compounds and viral indicators Of the three categories, only the first two pertain to the detection of bacterial pathogens which is of interest in this study, while viral indicators are mainly used as surrogate indicators for enteric viruses As... transmitted by use of recreational water and this association has not been investigated There is much that is not understood about the microbial risks present in recreational water settings Therefore, from a research standpoint, it is important to investigate what is the nature of 1 the microbial community, including pathogens, that is present in our recreational waters It is also integral to develop... Worldwide, recreational water quality criteria are based on the measurement of fecal indicator bacteria using culture techniques However various limitations of using traditional fecal indicators in monitoring bathing water quality and predicting disease outbreak exist; these include low levels of correlation with pathogens, low detection sensitivity, ability to multiply outside the water column, as well as inability... evidence linking exposure to environmental MAC to development of skin and soft tissue infections, and even pneumonia (Collins et al., 1984; Shelton et al., 1999) In most cases, disease outbreaks can be attributed to a high bather load, lack of disinfection, or inadequate maintenance of filters and pipes 2.1.2.5 Pseudomonas aeruginosa The pseudomonads are a group of Gram-negative, curved rods classified into... point-sampling approach A more intensive sampling for spatial and temporal variability of bacterial markers were also conducted within a single recreational water facility Using correlation analysis, linear regression and FA/PCA, it was discovered that Enterobacteriaceae and M smithii were good predictors of pathogen presence in study sites subjected to point-sampling, but none of the measured indicators ... in determining microbial risks of recreational waters from aquatic facilities in Singapore Despite widespread hype of the potential of using molecular techniques in water quality monitoring, they... Attributes of an ideal indicator Since the beginnings of water quality research, many indicators have been proposed but all possess shortcomings in one way or another In 2004, several attributes of an... effects of waterborne disease outbreaks in coastal and inland recreational waters, including treated (spas, pools, interactive fountains) and untreated (reservoir) waters In 2007 and 2008, a total of
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