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DEVELOPMENT OF NOVEL COMBINATORIAL METHODS
FOR GENOTYPING THE COMMON FOODBORNE
PATHOGEN CAMPYLOBACTER JEJUNI
Erin Peta Price
Bachelor of Applied Science (Honours I), QUT 2003
CRC for Diagnostics
School of Life Sciences, Institute of Health and Biomedical Innovation
Queensland University of Technology
Brisbane, Australia
A thesis submitted for the degree of Doctor of Philosophy of the Queensland
University of Technology
2007
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STATEMENT OF ORIGINAL AUTHORSHIP
The work presented in this thesis has not been previously submitted to meet
requirements for an award at this or any other higher education institution. To the
best of my knowledge and belief, this thesis contains no material previously published
or written by another person except where due reference is made.
Signed:
Erin Peta Price B. App. Sci. (Hons)
Date:
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ABSTRACT
Campylobacter jejuni is the commonest cause of bacterial foodborne gastroenteritis in
industrialised countries. Despite its significance, it remains unclear how C. jejuni is
disseminated in the environment, whether particular strains are more pathogenic
than others, and by what routes this bacterium is transmitted to humans. One major
factor hampering this knowledge is the lack of a standardised method for
fingerprinting C. jejuni. Therefore, the overall aim of this project was to develop
systematic and novel genotyping methods for C. jejuni.
Chapter Three describes the use of single nucleotide polymorphisms (SNPs) derived
from the multilocus sequence typing (MLST) database of C. jejuni and the closely
related Campylobacter coli for genotyping these pathogens. The MLST database
contains DNA sequence data for over 4000 strains, making it the largest comparative
database available for these organisms. Using the in-house software package
“Minimum SNPs”, seven SNPs were identified from the C. jejuni/C. coli MLST database
that gave a Simpson’s Index of Diversity (D), or resolving power, of 0.98. An allele-
specific real-time PCR method was developed and tested on 154 Australian C. jejuni
and C. coli isolates. The major advantage of the seven SNPs over MLST is that they
are cheaper, faster and simpler to interrogate than the sequence-based MLST
method. When the SNP profiles were combined with sequencing of the rapidly
evolving flaA short variable region (flaA SVR) locus, the genotype distributions were
comparable to those obtained by MLST-flaA SVR.
Recent technological advances have facilitated the characterisation of entire bacterial
genomes using comparative genome hybridisation (CGH) microarrays. Chapter Four
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of this thesis explores the large volume of CGH data generated for C. jejuni and eight
binary genes (genes present in some strains but absent in others) were identified that
provided complete discrimination of 20 epidemiologically unrelated strains of C.
jejuni. Real-time PCR assays were developed for the eight binary genes and tested on
the Australian isolates. The results from this study showed that the SNP-binary assay
provided a sufficient replacement for the more laborious MLST-flaA SVR sequencing
method.
The clustered regularly interspaced short palindromic repeat (CRISPR) region is
comprised of tandem repeats, with one half of the repeat region highly conserved and
the other half highly diverse in sequence. Recent advances in real-time PCR enabled
the interrogation of these repeat regions in C. jejuni using high-resolution melt
differentiation of PCR products. It was found that the CRISPR loci discriminated
epidemiologically distinct isolates that were indistinguishable by the other typing
methods (Chapter Five). Importantly, the combinatorial SNP-binary-CRISPR assay
provided resolution comparable to the current ‘gold standard’ genotyping
methodology, pulsed-field gel electrophoresis.
Chapter Six describes a novel third module of “Minimum SNPs”, ‘Not-N’, to identify
genetic targets diagnostic for strain populations of interest from the remaining
population. The applicability of Not-N was tested using bacterial and viral sequence
databases. Due to the weakly clonal population structure of C. jejuni and C. coli, Not-
N was inefficient at identifying small numbers of SNPs for the major MLST clonal
complexes. In contrast, Not-N completely discriminated the 13 major subtypes of
hepatitis C virus using 15 SNPs, and identified binary gene targets superior to those
previously found for phylogenetic clades of C. jejuni, Yersinia enterocolitica and
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Clostridium difficile, demonstrating the utility of this additional module of “Minimum
SNPs”.
Taken together, the presented work demonstrates the potentially far-reaching
applications of novel and systematic genotyping assays to characterise bacterial
pathogens with high accuracy and discriminatory power. This project has exploited
known genetic diversity of C. jejuni to develop highly targeted assays that are akin to
the resolution of the current ‘gold standard’ typing methods. By targeting
differentially evolving genetic markers, an epidemiologically relevant, high-resolution
fingerprint of the isolate in question can be determined at a fraction of the time,
effort and cost of current genotyping procedures. The outcomes from this study will
pave the way for improved diagnostics for many clinically significant pathogens as the
concept of hierarchal combinatorial genotyping gains momentum amongst infectious
disease specialists and public health-related agencies.
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LIST OF KEYWORDS
Campylobacter jejuni; Campylobacter coli; genotyping; single-nucleotide
polymorphism; SNP; binary gene; CRISPR; HRM; high-resolution melt; Minimum
SNPs; Not-N; bacteria; real-time PCR; comparative genome hybridisation; CGH;
microarray; software; hepatitis C virus; HCV.
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LIST OF PUBLICATIONS AND MANUSCRIPTS
The following publications and manuscripts have been prepared in conjunction with this thesis.
• Price, E. P., V. Thiruvenkataswamy, L. Mickan, L. Unicomb, R. E. Rios, F.
Huygens and P. M. Giffard. 2006. Genotyping of Campylobacter jejuni using seven
Single Nucleotide Polymorphisms in combination with flaA Short Variable Region
sequencing. Journal of Medical Microbiology 55: 1061-1070 (Impact Factor (2005):
2.32).
• Price, E. P., F. Huygens and P. M. Giffard. 2006. Fingerprinting of Campylobacter
jejuni by using Resolution-Optimized Binary Gene Targets derived from Comparative
Genome Hybridization Studies. Applied and Environmental Microbiology 72: 7793-7803
(Impact Factor (2005): 3.82).
• Price, E. P., H. Smith, F. Huygens and P. M. Giffard. 2007. High-resolution DNA
Melt Curve Analysis of the Clustered, Regularly Interspaced Short-Palindromic-Repeat
Locus of Campylobacter jejuni. Applied and Environmental Microbiology 73: 3431-3436
(Impact Factor (2005): 3.82).
• Price, E. P., J. Bamber, V. Thiruvenkataswamy, F. Huygens and P. M. Giffard.
2007. Computer-aided identification of polymorphism sets diagnostic for groups of
bacterial and viral genetic variants. BMC Bioinformatics 8: 278. (Impact Factor (2005):
4.96).
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THESIS-ASSOCIATED ABSTRACTS AND PRESENTATIONS
• Price, E. P., F. Huygens and P. M. Giffard. High resolution fingerprinting of
Campylobacter jejuni using a small number of binary gene targets derived from
comparative genome hybridisation studies. Australian Society for Microbiology Annual
Scientific Meeting – Becton Dickinson QLD finalist (July 2006 – oral presentation).
• Price, E. P., F. Huygens, L. Unicomb, J. Ferguson and P. M. Giffard. C. jejuni
genotyping using single-nucleotide polymorphisms derived from multilocus sequence
typing databases. 13th International Workshop on Campylobacter, Helicobacter, and
Related Organisms (CHRO) (September 2005 – oral and poster presentations).
Other publications by the author are listed.
• Merchant, S., E. P. Price, P. Blackall, J. Templeton, F. Huygens and P. M.
Giffard. Characterisation of chicken Campylobacter jejuni isolates using resolution-
optimised single nucleotide polymorphisms and binary gene markers. Manuscript in
preparation.
• Stephens, A. J., F. Huygens, J. Inman-Bamber, E. P. Price, G. R. Nimmo, J.
Schooneveldt, W. Munckhof and P. M. Giffard. 2006. Methicillin-resistant
Staphylococcus aureus genotyping using a small set of polymorphisms. Journal of
Medical Microbiology. 55: 43-51.
• Robertson, G. A., V. Thiruvenkataswamy, H. Shilling, E. P. Price, F. Huygens, F.
A. Henskens and P. M. Giffard. 2004. Identification and interrogation of highly
informative single-nucleotide polymorphism sets defined by multilocus sequence typing
databases. Journal of Medical Microbiology. 53: 35-45.
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• Giffard, P. M., F. A. Henskens, F. Huygens, E. P. Price, G. A. P. Robertson, H. J.
Shilling and V. Thiruvenkataswamy. (Inventors). Assessing data sets.
PCT/AU03/00320; WO2003/079241. Priority date: 18-03-02.
• Giffard, P. M., F. Huygens, E. P. Price, A. J. Stephens and J. Inman-Bamber.
(Inventors). Patent (Provisional): A Diagnostic Method. 2006905879. Filed 23-10-06.
• Inventorship unfinalised. Patent (Provisional): A Genotyping Method. 2007900172.
Filed 15-01-07.
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ACKNOWLEDGEMENTS
I gratefully acknowledge my supervisors, Associate Professor Phil Giffard and Dr Flavia
Huygens, for their continued encouragement, guidance, enthusiasm and advice during my
Honours and PhD projects. Phil in particular has been a great mentor and I feel very fortunate
to have worked with such an amazing scientific mind – it really has been a pleasure to be his
student. I am very appreciative for his quick turn-over of my endless manuscript drafts. Thanks
also to my collaborators Pat Blackall, Jillian Templeton, Jan-Maree Hewitson (the Department of
Primary Industries and Fisheries), Helen Smith (Queensland Health Scientific Services),
Jacqueline Schooneveldt (Queensland Health Pathology Services), Lance Mickan and Leanne
Unicomb (OzFoodNet).
I would like to thank my family, Judith, Peter, Jodie, Meagan and Emma, for their continued
emotional support and understanding over the past three-and-a-half years, and for only ever
encouraging my pursuits. Huge thanks goes to QUT, IHBI, and the CRC for Diagnostics for
allowing me to undertake this project and for providing my scholarship, travel and research
funding. I’d also like to acknowledge the support of the other students in my research group
(Alex Stephens, Shreema Merchant, Tegan Harris and Erin Honsa) for their scientific advice and
for providing a change of scenery, particularly during the writing-up process. To the other
students in the CMB, and particularly within the CRC for Diagnostics (Chris Swagell, Shea
Carter and Levi Carroll), I would like to extend a big thank-you for making the times in the lab
more enjoyable and for providing me with great troubleshooting advice.
A special thank-you goes to Derek, who was my partner-in-crime throughout this entire
journey and who provided endless support, love and also many interesting scientific
discussions. Without his support this journey may not have eventuated. Lastly I shouldn’t
forget my dog, Jinx, who unwittingly provided much-needed stress relief and welcome
distraction throughout the duration of my PhD.
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LIST OF ABBREVIATIONS
AFLP Amplified fragment length polymorphism AS Allele-specific Bp base pair/s CAP Capsular polysaccharide biosynthesis (locus) CC Clonal complex
CGH Comparative genome hybridisation CJIE Campylobacter jejuni-integrated element/s
CRISPR Clustered regularly interspaced short palindromic repeat (locus) D Simpson’s index of diversity
DNA Deoxyribonucleic acid DR Direct repeat
flaA SVR Flagellin A short variable region FM Flagellar modification (locus)
GBS Guillain-Barré syndrome HRM High-resolution melt Kb kilobase/s
LOS Lipooligosaccharide (locus) MLEE Multilocus enzyme electrophoresis MLST Multilocus sequence typing ORF Open reading frame PCR Polymerase chain reaction PFGE Pulsed-field gel electrophoresis PR Plasticity region
RAPD Random amplified polymorphic deoxyribonucleic acid R/M Restriction/modification (locus) SNP Single-nucleotide polymorphism ST Sequence type Tm Melting temperature
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TABLE OF CONTENTS
Page
TITLE PAGE i
CERTIFICATE RECOMMENDING ACCEPTANCE iii
STATEMENT OF ORIGINAL AUTHORSHIP v
ABSTRACT vii
LIST OF KEYWORDS xi
LIST OF PUBLICATIONS AND MANUSCRIPTS xiii
ACKNOWLEDGEMENTS xvii
LIST OF ABBREVIATIONS xix
TABLE OF CONTENTS xxi
CHAPTER ONE. INTRODUCTION 1
1.1 A description of the scientific problem investigated 2
1.2 The overall objectives of the study 4
1.3 The specific aims of the study 4
1.4 An account of scientific progress linking the papers 5
1.5 References 10
CHAPTER TWO. LITERATURE REVIEW 17
2.1 Introduction 18
2.2 Epidemiology of human Campylobacter-related disease 18
2.2.1 Incidence of campylobacteriosis 18
2.2.2 Distribution of C. jejuni and C. coli in food and the environment 20
2.3 Clinical aspects of Campylobacter infection 23
2.3.1 Guillain-Barré syndrome 24
2.4 Genomes of Campylobacter species 25
2.5 Currently used methods for typing C. jejuni and C. coli 31
2.5.1 Phenotypic methods 32
2.5.1a Serotyping 32
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2.5.1b Phage typing 33
2.5.1c Hippuricase speciation 33
2.5.1d Multilocus enzyme electrophoresis (MLEE) 34
2.5.2 Genotyping methods 35
2.5.2a fla typing 35
2.5.2b Pulsed-field gel electrophoresis (PFGE) 37
2.5.2c Random amplified polymorphic DNA-PCR (RAPD-PCR) 39
2.5.2d Amplified fragment length polymorphism (AFLP) 40
2.5.2e Multilocus sequence typing (MLST) 40
2.5.2f Single-nucleotide polymorphism (SNP) profiling 43
2.5.2g
Clustered regularly interspaced short palindromic
repeat (CRISPR) typing 45
2.5.2h DNA microarrays 47
2.6 Real-time PCR-based methodologies 54
2.6.1 Introduction 54
2.6.2 Probe-based methodologies 57
2.6.2a TaqMan® probes 57
2.6.2b Molecular beacons 60
2.6.3 Generic chemistries 62
2.6.4 Allele-specific PCR (AS PCR) 64
2.6.5 Fluorescently labelled primers 66
2.6.6 Melting temperature (Tm) shift primers 69
2.7 Emerging genotyping technologies 71
2.7.1 High-resolution melt (HRM) analysis 71
2.7.2 Lab-on-a-chip (LOaC) devices 73
2.8 Hepatitis C virus (HCV) 76
2.8.1 Introduction 76
2.8.2 Currently adopted HCV genotyping methodologies 79
2.9 References 83
CHAPTER THREE. RESULTS 107
Statement of Joint Authorship 108
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Genotyping of Campylobacter jejuni using seven Single Nucleotide Polymorphisms in
combination with flaA Short Variable Region sequencing 110
CHAPTER FOUR. RESULTS 121
Statement of Joint Authorship 122
Fingerprinting of Campylobacter jejuni by using resolution-optimized binary gene
targets derived from Comparative Genome Hybridization studies 124
CHAPTER FIVE. RESULTS 135
Statement of Joint Authorship 136
High-resolution DNA melt curve analysis of the Clustered, Regularly Interspaced
Short-Palindromic-Repeat locus of Campylobacter jejuni 138
Supplementary data 144
CHAPTER SIX. RESULTS 145
Statement of Joint Authorship 146
Computer-aided identification of polymorphism sets diagnostic for groups of
bacterial and viral genetic variants 148
Supplementary data 156
CHAPTER SEVEN. GENERAL DISCUSSION 159
7.1 Discussion 160
7.2 Conclusions and future directions 173
7.3 Major findings of this thesis 175
7.4 References 177
APPENDIX 183
Summary of C. jejuni and C. coli genotyping results 183
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Chapter 1: Introduction
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CHAPTER ONE
INTRODUCTION
Chapter 1: Introduction
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1.1 A Description of the Scientific Problem Investigated
Campylobacter jejuni, and less frequently Campylobacter coli, account for a
substantial portion of all human gastroenteritis cases caused by foodborne sources
in Australia, with an estimated 1% of the population infected annually [1]. Whilst
rarely fatal, campylobacteriosis is a debilitating gastrointestinal illness typified by
fever, abdominal cramps and bloody diarrhoea [2] and therefore unsurprisingly has
considerable economic and social consequences [3]. Intriguingly, campylobacters
are unlikely foodborne pathogens as they possess fastidious growth requirements,
are sensitive to environmental stresses such as dessication, osmotic stress, and
even oxygen, and as yet do not appear to harbour strain-specific virulence or
pathogenic determinants [4]. These traits are in contrast to those of their well-
characterised foodborne pathogen counterparts, such as Escherichia coli and
Salmonella spp. [5]. There is intense interest in unravelling the mechanisms that C.
jejuni and C. coli employ to adapt to the inhospitable environments required for
their survival and dissemination. There also exists an incomplete understanding of
the contribution that different sources (e.g. environmental versus foodborne
reservoirs) play in the transmission of Campylobacter spp. to humans, and whether
particular strains are more likely to cause human gastrointestinal disease than
others [6-8].
A fundamental step in advancing our comprehension of Campylobacter transmission
and disease potential is to characterise variants within the species (i.e. strains)
based on genetic differences or similarities between strains; this is termed
genotyping. A number of genotyping methods have been developed for C. jejuni
and C. coli; however, no standard methodology currently exists due to factors such
Chapter 1: Introduction
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as assay expense, labour-intensity, time-consumption, low resolution or complex
data analysis [7, 9]. The method chosen also depends on the requirements of the
end-user. In diagnostic laboratories, the large number of Campylobacter isolates
encountered demands rapid, high-throughput and cost-effective typing methods
that often possess low resolution, whereas higher discrimination is of prime
importance for epidemiological or research purposes where expense or
laboriousness are generally less important considerations [7].
An ideal genotyping strategy for C. jejuni and C. coli would be low-cost, simple,
rapid, highly discriminatory and based on a single assay platform, which could
therefore be used for a wide range of applications. Recent advances in array
technology [10] and high-throughput DNA sequencing [11, 12] have resulted in the
compilation of databases that contain a vast pool of comparative genetic data.
Using appropriate bioinformatics tools, these data can be analysed and a small
number of highly informative genetic targets identified. Such targets can then form
the basis of streamlined genotyping assays that are designed to give the required
information using the minimal number of genetic targets [13]. To date, no studies
have employed such an approach to the genotyping of C. jejuni and C. coli. If such
methods could be developed, their application in diagnosis, source tracing and host
specificity of not only C. jejuni and C. coli, but also for other clinically or
environmentally significant pathogens, is far-reaching.
Chapter 1: Introduction
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1.2 The Overall Objectives of the Study
The overall objective of this study was to investigate, develop and test novel
genotyping methods for the common foodborne pathogens, C. jejuni and C. coli.
The methods were designed using the real-time PCR platform. The central
hypothesis of this study was that these methods would rival or surpass the current
genotyping ‘gold standard’, pulsed-field gel electrophoresis (PFGE), and would
achieve this performance in a convenient and low-cost manner using a small
number of highly informative genetic targets. The approaches used were: to identify
and interrogate highly informative single-nucleotide polymorphisms (SNPs) from the
slowly evolving housekeeping genes of C. jejuni/C. coli using the in-house
“Minimum SNPs” sofware; to identify and interrogate highly informative binary
genes (genes present in some strains but absent in others) from C. jejuni
comparative genome hybridisation (CGH) data, again using “Minimum SNPs”; to
increase resolution of the SNP-binary gene profiles in investigating both sporadic
and outbreak campylobacteriosis cases by inclusion of the hypervariable CRISPR
locus; and to apply a novel algorithm of “Minimum SNPs” for the identification of
informative genetic targets that diagnose, with high confidence, defined populations
of bacterial or viral strains.
1.3 The Specific Aims of the Study
The aims of this study were:
Chapter 1: Introduction
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1. To develop a SNP real-time PCR-based assay as a rapid and cost-effective
alternative to the multilocus sequence typing (MLST) genotyping method for
C. jejuni and C. coli (Chapter Three).
2. To increase the resolving power of SNP typing (Aim 1) by identifying a
subset of binary genes from CGH studies of C. jejuni (Chapter Four).
3. To incorporate a melting temperature (Tm)-based real-time PCR assay for
the rapidly evolving CRISPR locus of C. jejuni. It was hypothesised that
addition of a hypervariable locus to the existing SNP and binary gene
approach would provide resolution comparable to or surpassing that of PFGE
(Chapter Five).
4. To test the performance of a novel third module of the “Minimum SNPs”
software, ‘Not-N’, in identifying informative genotyping targets. It was
hypothesised that Not-N could identify small numbers of genetic markers
that would provide diagnostic targets for differentiating clinically or
epidemiologically important groups of bacterial or viral strains, including C.
jejuni and C. coli, from the remaining population (Chapter Six).
1.4 An Account of Progress Linking the Scientific Papers
This project has primarily pursued the development of novel real-time PCR-based
genotyping methods that are based on the interrogation of highly informative
targets derived from large comparative databases. It was our hypothesis that small
numbers of resolution-optimised targets could be systematically extracted from the
Chapter 1: Introduction
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large comparative databases (in particular, those maintained for MLST and CGH)
and that the targets could be rapidly and efficiently interrogated on a uniform
diagnostic platform. Real-time PCR was selected for this purpose because of the
increasing availability of this platform in many diagnostic and research laboratories,
its ability to interrogate different classes of polymorphisms (specifically SNPs,
binary markers and the hypervariable repetitive regions), the flexibility of assay
chemistries, the affordability of real-time apparatus, and its closed-tube format,
which minimises amplicon contamination [14, 15].
Since its inception [16], the volume of comparative gene data generated by MLST
has steadily increased, and there now exist MLST schemes for over 40 bacterial
pathogens [17]. The primary advantage of MLST over gel-based methods, such as
PFGE and AFLP, lies in the portability and accuracy of sequence data [16]. However,
MLST remains an impractical approach for routine surveillance involving large
numbers of isolates and for diagnostic laboratories due to the labour-intensity and
cost of DNA sequencing [18]. More recently, comparative genomic hybridisation
(CGH) methods that compare the entire gene complement of multiple bacterial
strains against a reference strain have been developed using microarray technology
[10, 19], and are also increasing in popularity. Like MLST, however, CGH is a costly,
labour-intensive and time-consuming procedure that is currently impractical for
routine surveillance or high-throughput studies.
In line with our hypothesis, Chapter Three of the thesis describes the development
of a real-time PCR-based single-nucleotide polymorphism (SNP) assay for C. jejuni
and C. coli. The SNPs were identified from within the C. jejuni/C. coli MLST
database using the Simpson’s Index of Diversity (D) [20] module of “Minimum
Chapter 1: Introduction
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SNPs” [13]. Previous studies utilising “Minimum SNPs” on the Neisseria meningitidis
and Staphylococcus aureus MLST databases showed that a high degree of
discrimination (between 0.95 and 0.98) was obtained with approximately seven
high-D SNPs [13, 21]. It was therefore hypothesised that the D function of
“Minimum SNPs” would be similarly successful in identifying high-D SNPs within the
C. jejuni/C. coli MLST database. Chapter Three details a high-D seven-member SNP
set that, in combination with flaA short variable region sequencing, provided strain
fingerprints analogous to the more laborious MLST-flaA SVR method. This finding
was significant as MLST-flaA SVR is becoming increasingly employed as a
replacement to PFGE, particularly for characterising outbreaks of campylobacteriosis
[22-24].
Despite the substantial labour reduction of MLST-flaA SVR, DNA sequencing was still
an assay requirement for the SNP-flaA SVR approach, and therefore this assay did
not meet our performance requirement of being entirely carried out on the real-time
PCR platform. Therefore, Chapter Four of this thesis details a second real-time PCR-
based genotyping method for C. jejuni and C. coli, developed with a view to
replacing flaA SVR sequencing. The D function of “Minimum SNPs” systematically
identified eight highly informative binary gene targets from within the then-
available CGH and genome sequence data for C. jejuni, comprising
presence/absence information for approximately 33,000 genes/isolates [4, 25-28].
The SNP-binary approach was used to characterise a large collection of Australian C.
jejuni and C. coli isolates. The results from this chapter demonstrated the
comparable performance of the combinatorial SNP-binary assay with SNP-flaA SVR
and MLST-flaA SVR.
Chapter 1: Introduction
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Both the MLST-flaA SVR and SNP-binary gene approaches were unable to reach the
resolving power of PFGE when examining the Australian C. jejuni and C. coli isolate
collections obtained during this project. Therefore, Chapter Five of this thesis
examined the rapidly-evolving clustered regularly interspaced short palindromic
repeat (CRISPR) locus of C. jejuni and C. coli as an add-on to the existing SNP-
binary assay, with a view to attaining the resolving power of PFGE. CRISPRs are a
class of sequence repeats that are widespread in bacterial and archaeal genomes
[29] and are predominantly characterised by DNA sequencing [30]. As the aim of
the preceding genotyping methods was to circumvent the requirement for
sequencing, interrogation of this locus on the real-time PCR platform was sought.
Recent developments in real-time PCR temperature and optical capabilities have
provided the capacity to differentiate amplicons based on small sequence
differences [31-33].
A novel and highly reproducible method for interrogating the CRISPRs of C. jejuni
using high-resolution melt (HRM) analysis on the real-time PCR apparatus is
detailed in Chapter Five. This chapter is the first in the published literature to
demonstrate the applicability of HRM on DNA polymorphisms other than individual
SNPs. The few other HRM studies in the literature have focussed on the intercalating
dyes LC Green and SYTO® 9 for HRM analysis due to their apparent superiority over
SYBR® Green I [31, 33]. This study was also the first to directly compare SYTO® 9
and SYBR® Green I chemistries for HRM and to demonstrate that SYBR® Green I is
a more robust and reproducible chemistry. The SNP-binary gene assay in concert
with CRISPR HRM analysis was as discriminatory as PFGE for assessing both
sporadic and outbreak campylobacteriosis epidemiology, and demonstrated for the
Chapter 1: Introduction
- 9 -
first time the power and applicability of combinatorial genotyping approaches for
systematic and high-resolution C. jejuni and C. coli fingerprinting.
One feature absent from “Minimum SNPs” and from similar bioinformatics programs
[34, 35] was the ability to identify genetic targets that discriminate with high
confidence a bacterial population of interest from the remaining population of a
species, such as for the differentiation of bacteria with increased virulence
properties from their avirulent counterparts. Chapter Six of the thesis discusses the
development and application of an innovative module of the “Minimum SNPs”
software, termed Not-N, to achieve this purpose. The performance of Not-N for
discriminating the major clonal complexes (CCs) of C. jejuni and C. coli was
investigated in an attempt to improve the CC-specific SNPs identified by other
researchers [36-38]. Not-N performed poorly in identifying lineage-specific SNPs for
both C. jejuni/C. coli and Staphylococcus aureus CCs. However, the Not-N module
was efficient at discriminating between the various subtypes of the clinically
important hepatitis C virus (HCV) [39], as well as in identifying clade-specific genes
from CGH data for C. jejuni, Clostridium difficile and Yersinia enterocolitica [40-42].
The genotyping targets identified by Not-N analysis are superior in performance to
those identified by other researchers, and in the case of HCV, which have formed
the basis of commercial assays commonly used in diagnostic laboratories [43-48].
It is anticipated that these superior Not-N markers will eventually replace those in
current use.
Chapter 1: Introduction
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1.5 References
1. Hall, G. and the OzFoodNet Working Group. 2004. How much gastroenteritis in
Australia is due to food? Estimating the incidence of foodborne gastroenteritis in
Australia. National Centre for Epidemiology and Population Health (NCEPH) Working
Paper No. 51, Canberra: National Centre for Epidemiology and Population Health.
Available at: http://nceph.anu.edu.au/Publications/Working_Papers/WP51.pdf [last
accessed 31-03-07].
2. Ketley, J. M. 1997. Pathogenesis of enteric infection by Campylobacter. Microbiology
143: (Pt 1):5-21.
3. Abelson, P., Potter Forbes, M. and Hall, G. 2006. The annual cost of foodborne
illness in Australia. Australian Government Department of Health and Ageing,
Canberra.
4. Parkhill, J., Wren, B. W., Mungall, K., Ketley, J. M., Churcher, C., Basham, D.,
Chillingworth, T., Davies, R. M., Feltwell, T., Holroyd, S., Jagels, K.,
Karlyshev, A. V., Moule, S., Pallen, M. J., Penn, C. W., Quail, M. A.,
Rajandream, M. A., Rutherford, K. M., van Vliet, A. H., Whitehead, S. and
Barrell, B. G. 2000. The genome sequence of the food-borne pathogen
Campylobacter jejuni reveals hypervariable sequences. Nature. 403: 665-668.
5. Park, S. F. 2002. The physiology of Campylobacter species and its relevance to their
role as foodborne pathogens. Int J Food Microbiol. 74: 177-188.
6. Tauxe, R. V. 1992. Epidemiology of Campylobacter jejuni infections in the United
States and other industrialized nations. In I. Nachamkin, M. J. Blaser, and L. S.
Tompkins (ed.) Campylobacter jejuni: current status and future trends. American
Society for Microbiology. Washington D. C. pp. 9-19.
7. Wassenaar, T. M. and Newell, D. G. 2000. Genotyping of Campylobacter spp. Appl
Environ Microbiol. 66: 1-9.
Chapter 1: Introduction
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8. Manning, G., Dowson, C. G., Bagnall, M. C., Ahmed, I. H., West, M. and
Newell, D. G. 2003. Multilocus sequence typing for comparison of veterinary and
human isolates of Campylobacter jejuni. Appl Environ Microbiol. 69: 6370-6379.
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14. Monis, P. T., Giglio, S. and Saint, C. P. 2005. Comparison of SYTO9 and SYBR
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17. Chan, M-.S., Maiden, M. C. and Spratt, B. G. 2001. Database-driven multi locus
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18. Djordjevic, S. P., Unicomb, L. E., Adamson, P. J., Mickan, L., Rios, R. and the
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by restriction fragment length polymorphism analyses of the flaA gene. J Clin
Microbiol. 45: 102-108.
19. Fukiya, S., Mizoguchi, H., Tobe, T. and Mori, H. 2004. Extensive genomic
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20. Hunter, P. R. and Gaston, M. A. 1988. Numerical index of the discriminatory ability
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21. Stephens, A. J., Huygens, F., Inman-Bamber, J., Price, E. P., Nimmo, G. R.,
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Staphylococcus aureus genotyping using a small set of polymorphisms. J Med
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22. Sails, A. D., Swaminathan, B. and Fields, P. I. 2003. Utility of multilocus
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23. Mellmann, A., Mosters, J., Bartelt, E., Roggentin, P., Ammon, A., Friedrich, A.
W., Karch, H. and Harmsen, D. 2004. Sequence-based typing of flaB is a more
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24. Clark, C. G., Bryden, L., Cuff, W. R., Johnson, P. L., Jamieson, F., Ciebin, B.
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25. Pearson, B. M., Pin, C., Wright, J., I’Anson, K., Humphrey, T. and Wells, J. M.
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26. Taboada, E. N., Acedillo, R. R., Carrillo, C. D., Findlay, W. A., Medeiros, D. T.,
Mykytczuk, O. L., Roberts, M. J., Valencia, C. A., Farber, J. M. and Nash, J. H.
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27. Poly, F., Threadgill, D. and Stintzi, A. 2004. Identification of Campylobacter jejuni
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28. Fouts, D. E., Mongodin, E. F., Mandrell, R. E., Miller, W. G., Rasko, D. A.,
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2005. Major structural differences and novel putative virulence mechanisms from the
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31. Wittwer, C. T., Reed, G. H., Gundry, C. N., Vandersteen, J. G. and Pryor, R. J.
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32. White, H. and Potts, G. 2006. Mutation scanning by high resolution melt analysis.
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34. Carlson, C. S., Eberle, M. A., Rieder, M. J., Yi, Q., Kruglyak, L. and Nickerson,
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polymorphisms for association analyses using linkage disequilibrium. Am J Hum
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35. Filliol, I., Motiwala, A. S., Cavatore, M., Qi, W., Hazbon, M. H., Bobadilla del
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M. I., Leon, C. I., Crabtree, J., Angiuoli, S., Eisenach, K. D., Durmaz, R.,
Joloba, M. L., Rendon, A., Sifuentes-Osornio, J., Ponce de Leon, A., Cave, M.
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Mycobacterium tuberculosis based on single nucleotide polymorphism (SNP) analysis:
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systems, and recommendations for a minimal standard SNP set. J Bacteriol. 188:
759-772.
36. Best, E. L., Fox, A. J., Frost, J. A. and Bolton, F. J. 2004. Identification of
Campylobacter jejuni multilocus sequence type ST-21 clonal complex by single-
nucleotide polymorphism analysis. J Clin Microbiol. 42: 2836-2839.
37. Best, E. L., Fox, A. J., Frost, J. A. and Bolton, F. J. 2005. Real-time single-
nucleotide polymorphism profiling using TaqMan technology for rapid recognition of
Campylobacter jejuni clonal complexes. J Med Microbiol. 54: 919-925.
38. Best, E. L., Fox, A. J., Owen, R. J., Cheesbrough, J. and Bolton, F. J. 2006.
Specific detection of Campylobacter jejuni from faeces using single nucleotide
polymorphisms. Epidemiol Infect. 17: 1-8.
39. Simmonds, P., Bukh, J., Combet, C., Deleage, G., Enomoto, N., Feinstone, S.,
Halfon, P., Inchauspe, G., Kuiken, C., Maertens, G., Mizokami, M., Murphy, D.
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40. Champion, O. L., Gaunt, M. W., Gundogdu, O., Elmi, A., Witney, A. A., Hinds,
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borne pathogen Campylobacter jejuni reveals genetic markers predictive of infection
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41. Stabler, R. A., Gerding, D. N, Songer, J. G., Drudy, D, Brazier, J. S., Trinh, H.
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Clostridium difficiles reveals clade specificity and microevolution of hypervirulent
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Multiplex real-time reverse transcription-PCR assay for determination of hepatitis C
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Chapter 2: Literature Review
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CHAPTER TWO
LITERATURE REVIEW
Chapter 2: Literature Review
- 18 -
2.1 Introduction
Campylobacter-related diarrhoeal illness (campylobacteriosis) caused by infection
with pathogenic Campylobacter species is the leading cause of foodborne bacterial
gastroenteritis in industrialised countries, with an estimated 1% of the population
affected every year. Since the recognition of Campylobacter species as a significant
cause of foodborne disease in the early 1970s [1], the incidence of
campylobacteriosis has steadily increased [2] as a result of a number of factors,
such as increased awareness and improved detection and reporting of
Campylobacter infections [3]. Intriguingly, most cases of campylobacteriosis appear
to be sporadic as there is often no identifiable epidemiological link, and outbreaks
are infrequently identified [4]. A substantial degree of effort has been placed on
better understanding the ecology, occurrence and pathogenicity of this unique
bacterium; however, there still remain many ‘unknowns’ and genotyping is a
fundamental component for bridging these knowledge gaps. As such, this review
focuses on the epidemiology, clinical background and genetics of Campylobacter
spp., current typing methodologies available for fingerprinting of the most common
campylobacters, C. jejuni and C. coli, and emergent genotyping technologies. An
exhaustive review of all background material has not been attempted; rather,
relevant subjects are covered in detail.
Chapter 2: Literature Review
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2.2 Epidemiology of human Campylobacter-related disease
2.2.1 Incidence of campylobacteriosis
Over half a million cases of foodborne infectious disease occur annually in Australia,
costing an estimated $1.2 billion in labour losses and treatment expenses [5]. Of
the nineteen presently recognised Campylobacter species, twelve have been
implicated in human disease and two species, C. jejuni and C. coli, account for
greater than 95% of Campylobacter infections in humans [2, 6]. The remaining 5%
of Campylobacter-related disease is attributed to at least six other species; C. lari,
C. upsaliensis, C. fetus, C. sputorum, C. concisus and C. curvus [7]. C. jejuni alone
accounts for around 80-90% of human Campylobacter infections, and is the leading
cause of bacterial foodborne disease in many industrialised countries including
Australia, the United States, the United Kingdom, and the Netherlands [8-11]. Of
the 26,000 notified cases of bacterial foodborne illness reported in Australia in
2005, 64% were attributed to Campylobacter spp., whereas 33% of cases were
reported as Salmonella spp. [8]. These statistics are consistent with other
industrialised nations, where the incidence of campylobacteriosis far outweighs
other bacterial foodborne infections, such as Salmonella, Shigella and E. coli [10,
13, 14].
Indeed, the number of reported cases of campylobacteriosis in Australia is thought
to be a gross underestimate of the true incidence. As most cases of
campylobacteriosis are self-limiting, only an estimated 3 to 10% of people with a
Campylobacter infection in the community visit a doctor and have a positive stool
sample reported [15]. Additionally, New South Wales legislation does not require
Chapter 2: Literature Review
- 20 -
notification of Campylobacter infection except in outbreak situations [8]. In England
and Wales, over 46,000 cases of campylobacteriosis were reported in 2005 [12].
However, the true annual incidence of Campylobacter infection is estimated to be
about 1% of the British population [16]. Approximately 2.4 million cases of
campylobacteriosis are estimated to occur annually in the United States alone,
resulting in up to 800 deaths as a result of severe disease outcomes [17].
There are marked differences in the global epidemiology and clinical manifestation
of campylobacteriosis. Whilst the disease is approximately evenly distributed
amongst all age groups in industrialised countries, individuals in developing regions
over the age of two are rarely affected. It is thought that the high rate of
environmental exposure in developing regions due to the endemic nature of
Campylobacter species results in the development of protective immunity to
subsequent illness [18]. Exposure to C. jejuni and C. coli in industrialised countries,
on the other hand, is relatively limited, with protective immunity generally only
developed in individuals within high-exposure occupations, such as poultry abattoir
workers [19]. The manifestation of campylobacteriosis also differs between
industrialised and developing countries, with inflammatory disease leading to bloody
diarrhoea common in the former, and non-inflammatory illness accompanied by
dysentery-like illness prevalent in the latter [20].
2.2.2 Distribution of C. jejuni and C. coli in food and the environment
The campylobacters are Gram-negative, spirally curved rods that cultivate optimally
under microaerophilic conditions (5% O2) [2]. A subset of pathogenic
Campylobacter species (C. jejuni, C. coli, C. lari and C. upsaliensis) grow at 41 to
Chapter 2: Literature Review
- 21 -
42oC, unlike other campylobacters, which are inhibited at this elevated temperature
[21], hence C. jejuni and C. coli are termed “thermophilic campylobacters”. Despite
being significant causes of foodborne gastroenteritis in humans, the physiology of
thermophilic campylobacters suggests that they are unusual foodborne pathogens
due to their sensitivity to environmental stresses and fastidious growth
requirements. In particular, C. jejuni and C. coli are unable to multiply at lowered
temperatures, such as those present during food processing and storage. Further,
growth is inhibited by temperatures above 47oC, as well as freezing, desiccation,
osmotic stress and low pH [2]. Although C. jejuni and C. coli are unable to multiply
under hostile conditions, these organisms can persist and survive for long periods of
time in a variety of environments. Coupled with a very low infective dose (as little
as 500 colony forming units) [18] these organisms have many routes by which they
can gain access to a susceptible human host.
A number of different sources have been associated with the transfer of C. jejuni
and C. coli to humans but it is widely accepted that transmission occurs
predominantly through zoonotic spread of thermophilic campylobacters from natural
reservoirs to ingested products [6]. Whilst outbreaks of campylobacteriosis are rare,
several outbreak sources have been recognised, such as contact with and
consumption of raw or undercooked poultry, contact with domestic pets or
occupational contact with animals, and consumption of contaminated raw milk or
water, including surface and potable water [22-27]. On the contrary, comparatively
little is known about sources of infection associated with individually acquired
(sporadic) campylobacteriosis as often no identifiable epidemiological link between
cases can be made. The difficulty in tracing seemingly sporadic disease may be
attributed to the delayed onset of symptoms, the presence of multiple strains in
Chapter 2: Literature Review
- 22 -
individual instances of infection or the underreported nature of campylobacteriosis
[28].
The gastrointestinal and urogenital mucosa of many wild and domesticated warm-
blooded animals, such as poultry, sheep, cattle, pigs, goats and domestic pets can
act as a habitat and reservoir for C. jejuni and C. coli, where these organisms are
known to colonise asymptomatically [6, 28]. Whilst it remains unknown why C.
jejuni causes disease in humans but is asymptomatic in other animals, it has been
hypothesised that Campylobacter spp. may sense, adapt and respond to
fluctuations between 42oC (such as the temperature of the avian gut) and 37oC
(such as a human host), triggering a change from commensalism to pathogenesis
[29]. As C. jejuni is widespread in many warm-blooded animals used in food
production, this organism is frequently recoverable from poultry and meat products
sold for human consumption. One study conducted in Canada showed that 62% of
poultry products offered for retail sale were found to be contaminated with
Campylobacter spp. [30]. Due to the fastidious nature of this organism, ingestion of
raw or undercooked poultry either through direct consumption or through cross-
contamination with other foods is thought to be the major route of C. jejuni
transmission to humans [31]. Non-animal sources may also contribute to disease in
humans, probably as a result of animal faecal contamination [6]. Campylobacters
are widely distributed in the environment such as in source (recreational) waters,
soil, farm slurry, manure, broiler environments and beach sand, suggesting that
these niches may also act as sources of transmission to the human host [32].
Chapter 2: Literature Review
- 23 -
2.3 Clinical aspects of Campylobacter infection
Both host susceptibility and strain pathogenicity are thought to play a role in the
clinical outcome of C. jejuni infection, although these mechanisms have not been
well characterised. The physiology, ecology and pathogenesis of C. jejuni remain
poorly understood as this organism does not conform to the model paradigms
established for other foodborne pathogens, such as Escherichia coli and Bacillus
cereus [2, 33]. Infection with C. jejuni results in a wide spectrum of disease
outcomes. Typically, symptoms range from mild gastroenteritis to more severe
dysentery-like illness. Chronic sequelae can also occur, particularly in
immunocompromised patients, leading to invasive complications such as meningitis,
urinary tract infections or the autoimmune-mediated demyelinating neuropathies,
Guillain-Barré and Miller Fisher syndromes (GBS and MFS), which can be potentially
fatal [16, 33]. Clinically, campylobacteriosis is indistinguishable from the acute
diarrhoeal illness seen in salmonellosis and shigellosis [34]. Most cases of infection
are acute and self-limiting, with severe gastroenteritis, fever and abdominal pain
usually lasting between one to five days. Generally, antibiotics are not administered
in cases of acute enteritis, as symptoms lessen by the time bacterial diagnosis is
made, although an estimated 10% of cases do not resolve and therefore require
medical intervention. Antimicrobial therapy, such as erythromycin or
fluoroquinolones, may be employed to treat patients presenting with high fever,
bloody diarrhoea, or more than eight stools in a day [9, 34].
Chapter 2: Literature Review
- 24 -
2.3.1 Guillain-Barré Syndrome (GBS)
GBS is an acute, post infectious autoimmune-mediated disorder of the peripheral
nervous system, and is the most common cause of acute paralysis in children and
adults [33]. Around 40% of acute paralysis cases caused by GBS have been linked
to uncomplicated C. jejuni enteritis, with infection occurring up to three weeks prior
to the onset of neurological symptoms [33, 35]. In support of this link, C. jejuni has
been isolated from between 15% to 30% of patients presenting with GBS [9, 33].
Approximately 20% of patients with GBS suffer permanent disability, and around
5% die, even with respiratory care [9]. Although Campylobacter-related infections
are quite common in the general population, the risk of developing GBS or, less
commonly, MFS following infection is comparatively low [36]. The worldwide
incidence of GBS is 1.3 cases per 100,000 individuals [37]. However, the risk of
developing GBS following Campylobacter infection is around 100 per 100,000 cases
[9, 33].
Development of GBS is sporadic, and multiple epidemiologically-related cases have
not been identified, suggesting that host susceptibility plays an important role in
disease outcome. The pathogenesis of post-infectious GBS has been linked to the
development of serum anti-ganglioside antibodies in response to C. jejuni infection
[35] which are present in 30% of GBS patients but generally absent in non-GBS
patients [38]. These antibodies are thought to target the gangliosides localised in
peripheral nervous tissue, mediating an immune attack. Although ganglioside
mimicry may be an important factor in the pathogenesis of GBS [39], the exact
mechanisms triggering the autoimmune response remain to be elucidated. Some
studies have indicated that particular serotypes and genes involved in sialylation of
Chapter 2: Literature Review
- 25 -
the lipooligosaccharide (LOS) may be linked to development of GBS, although none
have a proven role [33, 40, 41].
2.4 Genomes of Campylobacter species
Despite their ubiquitous prevalence and clinical significance, understanding of the
genetics, physiology and pathogenesis of the thermophilic campylobacters is still
comparatively limited. Unlike other foodborne bacterial pathogens, such as
Salmonella, Shigella and E. coli, strain characterisation of C. jejuni and C. coli has
not revealed definitive source attribution [6]. To better understand the attributes
that render thermophilic campylobacters such successful foodborne pathogens, the
genome sequence of the human C. jejuni isolate, NCTC 11168, was completed in
2000 [42]. The genome sequences of C. jejuni RM1221, isolated from chicken meat,
and more recently of 81-176, a highly invasive strain of C. jejuni isolated from an
outbreak involving raw milk and that is used by many laboratories, have since been
completed and annotated [43, 44]. The recent completion of the shotgun sequences
for C. coli, C. lari and C. upsaliensis have further provided valuable insights into the
degree of intra- and inter-species genome diversity of Campylobacter spp. [43].
The genome sequence of NCTC 11168 is 1.64 megabase pairs (Mbp) in length,
contains a G+C content of 30.5%, and a predicted 1,643 open reading frames
(ORFs); the C. jejuni RM1221 genome is 1.77 Mbp in length, has a G+C content of
30.3% and encodes 1838 ORFs, and the C. jejuni 81-176 genome is 1.61 Mbp, has
a G+C content of 30.6%, and encodes 1779 genes [45]. In comparison, the
average genome of E. coli, Salmonella spp. and Shigella flexneri is 4.84Mbp in
length, has a G+C content of 51.2%, and comprises 4555 ORFs [46]. Due to the
Chapter 2: Literature Review
- 26 -
small size of the C. jejuni genome, it is unsurprising that a very high proportion
(94%) is estimated to encode for proteins, making the ORF density one of the
highest known [42, 43].
Although there is little organisation of C. jejuni genes into operons or clusters [42],
comparison of the RM1221 and 81-176 genomes with NCTC 11168 showed that the
three genomes are largely syntenic [43, 44] (Figure 1). Exceptions to this synteny
in RM1221 include the insertion of four genomic islands (termed Campylobacter
jejuni-integrated elements (CJIEs)) that are absent in NCTC 11168 and which
contribute to the larger size of the RM1221 genome. CJIEs 1, 2 and 4 share
homology with Mu bacteriophage and phage-related proteins, whereas CJIE3 is
thought to be an integrated plasmid due to its homology to the C. coli RM2228
pCC178 megaplasmid and Helicobacter hepaticus ATCC 51449 HHGI1 genomic
island [43].
Whilst most C. jejuni studies have focused upon the genes encoded by NCTC
11168, the recent genome sequence of RM1221 has shed light on the increasing
genetic diversity of this organism and researchers are beginning to take advantage
of this knowledge. Parker and colleagues [47] developed a PCR-based method for
detection of the four CJIEs identified in RM1221 in 67 C. jejuni and 12 C. coli
isolates to determine the prevalence of these elements. 55% of the C. jejuni and
58% of the C. coli isolates were positive for at least one of the four CJIEs, and 27%
of C. jejuni were positive for two or more elements. The C. coli isolates were
positive for CJIE 1 and 3 only, whereas the C. jejuni isolates were positive for all
four elements. Further analysis of the genes within the CJIEs showed that they
displayed a modular or mosaic pattern, with many genes absent or highly divergent
Chapter 2: Literature Review
- 27 -
Figure 1. Comparison of NCTC 11168, RM1221 and 81-176 Campylobacter jejuni genomes. The
three genomes are largely syntenic with the exception of the C. jejuni integrated elements (CJIEs; purple
lines) found in strain RM1221, the composition of genes residing within the plasticity regions (PRs; black
circles) and the extra genetic material integrated at ‘hot spots’ (HS; red circles). Blue circles indicate
gene insertion; pink circles indicate gene deletion. Asterisks indicate putative PRs identified from the
literature. Based on references 42-47 in conjunction with the CampyDB website [45].
compared with RM1221 [47]. The Mu-like bacteriophage encoded by CJIE 1 was
located essentially randomly throughout the 19 C. jejuni-positive strains, suggesting
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that CJIE 1 may contribute to genetic diversity or pathogenicity in C. jejuni by
acting as a vehicle for movement of genetic material or by removing gene function
via insertional inactivation [43, 44].
The smaller size of the 81-176 genome is primarily due to a reduction in genes
encoding the lipooligosaccharide (LOS), capsular polysaccharide (CAP) biosynthesis,
flagellar modification (FM) and restriction/modification (R/M) loci [44]. These loci
have been shown to exhibit a high degree of divergence in multiple C. jejuni strains
[48-50]. 81-176 contains a number of loci that are absent in RM1221 and NCTC
11168, such as additional respiration, potassium uptake, and R/M pathways, which
are thought to contribute to the increased pathogenicity of this strain [44]. Also
identified in 81-176 is a 6kb insertion element that exhibits features of the RM1221
CJIE 3 integrated plasmid. Similarly to CJIE 3, two proteins within the 81-176
element share homology with the C. coli pCC178 megaplasmid [44]. 81-176
harbours two plasmids proposed to contribute to its pathogenicity, pVir and pTet,
whereas NCTC 11168 and RM1221 lack plasmids [51, 52].
The ability of pathogen populations to generate genetic diversity may increase
adaptation and hence survival in hostile environments [53]. Based on multilocus
sequence typing (MLST; discussed in section 5.25) data, C. jejuni exhibits a weakly
clonal population structure, consisting of genetically diverse strains and a limited
number of seemingly clonal lineages [54, 55]. While the presence of integrated
plasmids and active bacteriophages can contribute to the generation of genetic
diversity in C. jejuni, it is unlikely that these elements are solely responsible for the
high level of diversity in this species, particularly as these elements are not
ubiquitously distributed. Frequently, multiple strains of C. jejuni are isolated from
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the same host and many strains are naturally competent for uptake of foreign DNA
[53]. C. jejuni is thought to generate extensive genetic diversity through frequent
intra- and inter-species homologous recombination, as a result of the simultaneous
presence of multiple strains at a distinct niche and mechanisms that allow DNA
transfer and subsequent integration into the chromosome [53, 54]. In support of
this, frequent homologous recombination between genes was first described for the
virulence-associated flagellin genes of C. jejuni (see fla typing, section 5.2.1) [56].
The active role of homologous recombination in the generation of C. jejuni genetic
diversity was confirmed in vitro by assessing the level of genetic exchange in two
non-essential genes, hipO and htrA [53]. This study identified the frequent
occurrence of genetic rearrangements, as a result of both interstrain horizontal
genetic transfer and intragenomic alterations, both in vitro and during in vivo
infection of chickens. Such rearrangements occurred in the absence of selective
(immunological) environmental pressure, confirming the observations made by
Dingle and co-workers [54]. Analysis of the NCTC 11168 and 81-176 genomes did
not reveal the presence of functional insertion sequence (IS) elements, transposons
or phage-associated sequences (prophages) [42, 48], suggesting that genetic
diversity in these strains is predominantly generated by homologous recombination,
potentially in combination with as yet unidentified mechanisms.
An interesting feature, scattered within the largely syntenic genomes of C. jejuni,
are regions of high gene variability termed plasticity regions (PRs) (Table 1).
Initially seven PRs, containing between 11 and 45 genes, were identified in the
NCTC 11168 genome based upon comparative genome hybridisation (CGH) studies
(discussed in section 5.2.8.1) [49]. The number of PRs was expanded to 16 and
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subsequently 18 as more C. jejuni CGH data was generated [47, 50], and is likely
to increase further as more is uncovered about the heterogeneity of this organism.
The PRs do not differ greatly in G+C content compared with the bulk of the
genome, and there is no evidence of association with mobile elements [49]. The
exact role of the PRs in contributing to C. jejuni genetic diversity has yet to be
elucidated. It remains unknown how PRs arise, how fast they evolve, whether they
are associated with virulence or host specificity and whether their diversity has
been driven by specific mechanisms [44, 49, 50].
Table 1. Plasticity regions and hot spots for insertion of horizontally acquired genetic material
identified in the Campylobacter jejuni genomes
Plasticity region
Gene* start Gene* end Function/gene
1 Cj0030 Cj0036 Type II restriction/modification 2 Cj0055c Cj0059c Unknown
3 Cj0177 Cj0182 Putative iron transport; biopolymer transport;
tonB; exbB1; exbD1
4 Cj0294 Cj0310c Pantothenate and biotin biosynthesis;
molybdenum ABC transporter 5 Cj0421c Cj0425 Unknown 6 Cj0480c Cj0490 Unknown; uxaA 7 Cj0561c Cj0571 Unknown 8 Cj0625 Cj0629 Hydrogenase; hypA; hypD; hypE 9 Cj0727 Cj0755 Type III restriction/modification 10 Cj0967 Cj0975 Unknown 11 Cj1135 Cj1151c Lipooligosaccharide (LOS)
12 Cj1293 Cj1343 Flagellar modification (FM); O-linked glycosylation
locus 13 Cj1414c Cj1449c Capsular biosynthesis (CAP) 14 Cj1543c Cj1563c Type I restriction/modification; Unknown 15 Cj1677 Cj1679 Unknown 16 Cj1717c Cj1729c leuA; leuB; leuC; unknown 17 Cj0258 Cj0263 pyrC; putative zinc transport 18 Cj0857c Cj0860 Unknown
Hot Spot Gene* start Gene* end Function/gene 1 Cj0501 --- --- 2 Cj0564 Cj0570 --- 3 Cj0747 Cj0760 --- 4 Cj0936 Cj0937 Potential C. jejuni integrated element (CJIE) 5 Cj1518 Cj1529c --- 6 Cj1585 --- --- 7 Cj1687 Cj1688 ---
*Genes named according to C. jejuni strain NCTC 11168. Adapted from references 44, 47 and 50.
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In addition to PRs, comparison of the 81-176 genome with RM1221 and NCTC
11168 uncovered potential hot spots for the insertion of horizontally acquired
genetic material (Table 1 and Figure 1) [44]. Some of these hot spots are within
PRs; 81-176, RM1221 and NCTC 11168 all harbour strain-specific DNA segments
that are bound by ORFs Cj0564 and Cj0570, located within PR7. 81-176 also
contains unique genes that are bounded by ORFs Cj1687 and Cj1688, including a
permease pseudogene and a putative peptidase, whereas NCTC 11168 and RM1221
do not contain additional genes at this region. PCR amplification of this hot spot in
fifteen clinical C. jejuni strains demonstrated that two isolates yielded fragments of
the same size as 81-176, whereas four strains contained an additional 2.5kb
fragment at this locus. DNA sequencing of the additional genetic material from the
two strains uncovered putative ATP-binding proteins previously unidentified in C.
jejuni. These findings indicate that hot spots in C. jejuni represent loci of high
genetic variability between C. jejuni isolates and demonstrate that additional
genetic material exists that may confer specific properties on different strains [44].
2.5 Currently used methods for typing C. jejuni and C. coli
For many foodborne pathogens, such as Salmonella, Shigella and E. coli, typing is
used mostly to identify sources of outbreaks [6]. However, as the majority of
campylobacteriosis cases are considered sporadic and due to the sheer number of
isolates encountered in clinical laboratories, C. jejuni and C. coli typing methods
have generally been employed to characterise either outbreak strains or for small-
scale retrospective epidemiological studies [28, 57-60]. Typing of C. jejuni and C.
coli isolates is essential for tracing infection sources and routes of transmission to
humans, for identifying and monitoring problematic strains and for assessing the
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level of public health intervention required to effectively control the spread of
disease [11]. There are many typing methods currently used for C. jejuni
characterisation, based on phenotypic or genotypic differences between strains, and
as such only the most commonly used or pertinent methods in relation to this
project will be discussed in greater detail.
2.5.1 Phenotypic methods
2.5.1a Serotyping
Serotyping was the first typing method used to characterise C. jejuni beyond the
species level. Two serotyping schemes were developed in the 1980s for C. jejuni
characterisation and have often been used in conjunction with other typing
methods. The Penner scheme [61] is based on soluble heat-stable (O) antigens,
whereas the Lior scheme detects variation in the heat-labile antigens [62, 63]. Of
the two, the Penner scheme has been more extensively developed and as such is
more commonly used in reference laboratories [64]. The Penner scheme defines
more than 60 serotypes in C. jejuni and C. coli, and variation in serotype is thought
to be conferred by the 42.6kb CAP locus [41, 48]. Used alone, serotyping lacks
discrimination and suffers many limitations, particularly in terms of cross-reactivity
[64]. One CGH C. jejuni study showed extensive genomic diversity amongst
serotype O:2 C. jejuni strains, and a lack of correlation between isolate serotype
and relatedness of strains [48]. Other problems associated with Penner serotyping
include difficulty in standardising the antiserum preparation and the expense of
antisera. As a result, new serotypes remain non-typeable [65]. Whilst serotyping
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has been used extensively, it is most beneficial when combined with other typing
methods for C. jejuni characterisation due to its low resolving power [66, 67].
2.5.1b Phage typing
Given the low resolving power of serotyping, phage typing has been employed as an
extension to serotyping to further characterise C. jejuni and C. coli, and there are
currently 76 recognised phage types [68]. This method makes use of a set of
virulent phages that may or may not have specificity for cell-surface receptors on
the bacterial host. If the bacteriophage is able to attach and infect, cell lysis will
result, which can be seen as plaque formation on Petri dish cultures [69]. The major
limitations of phage typing, similarly to serotyping, include the occurrence of non-
typeable strains and problems with cross-reactivity. Further, large panels of
specialised reagents and a high level of skill are required to perform phage typing,
limiting the use of this method to reference laboratories [70]. Consequently, phage
typing has largely been replaced by more rapid, sensitive and cost-effective
genotyping methods.
2.5.1c Hippuricase speciation
The hippuricase biochemical test has been extensively used to differentiate C. jejuni
from C. coli and C. lari [71]. The basis behind the test lies in the specific capacity
for C. jejuni to hydrolyse hippuric acid using N-benzoylglycine amidohydrolase
(hippuricase), an enzyme encoded by the hipO gene [72]. The hippuricase test has
an approximately 90% success rate. Both false-negative atypical C. jejuni strains
harbouring a truncated or lowly expressed hipO gene [73, 74] and non-C. jejuni
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false-positives have been documented [71]. As with most phenotypic-based
methods, the hippuricase test has been converted to PCR-based methods of
speciation with higher success rates [72, 75, 76].
2.5.1d Multilocus Enzyme Electrophoresis (MLEE)
MLEE is a typing method that has been extensively applied to long-term
epidemiological studies of many bacterial pathogens, including C. jejuni. MLEE
detects predominantly neutral variation in the amino acids of housekeeping
metabolic enzymes [77]. Approximately 20 housekeeping enzymes, encoded by
genes that are widely spread in the bacterial genome, are simultaneously examined
by MLEE [70, 77]. Housekeeping enzymes are used as they are under low selective
pressure for variability; amino acid variations that negatively affect the activity of
the enzyme and hence fitness of subsequent generations are selected against [78].
Strain variability is based on altered electrophoretic mobility due to amino acid
changes at any of the housekeeping loci [77].
MLEE has been used to study the clonal framework of C. jejuni [79], as well as to
assess the congruence between MLEE and other typing methods, such as pulsed-
field gel electrophoresis (PFGE) and MLST (sections 5.2.2 and 5.2.5) [70]. However,
MLEE has not been extensively adopted in characterising C. jejuni for a number of
reasons. Firstly, MLEE is an indirect typing method, as it examines the
electrophoretic mobilities of enzymes rather than indexing the direct molecular
basis of variation, and thus characterisation of strains can be subjective, particularly
when comparing profiles between laboratories. Further, this method is time
consuming, expensive and technically demanding, with the requirement for live
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cultures and the examination of a large number of loci [77], rendering MLEE
unsuitable for routine typing of C. jejuni. For these reasons, MLEE has been largely
superceded by MLST, which is essentially a sequence-based version of MLEE (see
section 5.2.5).
2.5.2 Genotypic methods
2.5.2a fla typing
Motility of C. jejuni is imparted by its possession of a polar flagellum at one or both
ends of the cell. The flagella are composed of many structural flagellin protein
subunits encoded by the highly homologous flaA and flaB genes. flaA and flaB share
92% homology, are approximately 1.7kb in length and are tandemly arranged in
the Campylobacter genome [80]. The flaA and flaB genes exhibit approximately
95% sequence variation between isolates, providing the basis of fla typing schemes
[80, 81]. Conventionally, fla typing involves PCR amplification of the entire flaA or
flaB gene followed by digestion with restriction enzymes. PCR amplicons are
subsequently subjected to restriction enzyme digestion, resulting in PCR-restriction
fragment length polymorphism (PCR-RFLP) profiles following gel electrophoresis
[63, 81].
fla typing using PCR-RFLP is quite discriminatory and provides greater strain
information than serotyping, and as such is a useful tool for epidemiological studies
[63]. However, due to lack of standardisation of primer sequences, endonucleases
and protocols, strain profiles generated by fla typing can vary widely between
laboratories [82]. These technical difficulties can be overcome by nucleotide
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sequencing of a 321 bp fragment of the flaA short variable region (SVR). There are
over 900 flaA SVR nucleotide sequences in the flaA SVR database [83]. However,
whilst sequencing overcomes the technical difficulties associated with PCR-RFLP
analysis of the fla genes, a higher cost and lower availability of equipment restrict
its widespread use. In addition, flaA SVR sequencing is generally less discriminatory
than flaA typing using PCR-RFLP [84, 85]. Single-strand conformation
polymorphism and denaturing gradient gel electrophoresis have also been
developed as cheaper alternatives for characterising the fla genes in C. jejuni but
are not in common use [86].
There is strong evidence that the flaA and flaB genes of C. jejuni and C. coli
undergo high levels of inter- and intra-genomic recombination as a mechanism to
evade host immunological responses [87]. This heterogeneity limits the usefulness
of fla typing as a sole typing method, particularly for long-term epidemiological
investigations, as this region does not stay stable over time and represents only a
single genetic locus [70, 88]. To overcome this, flaA SVR sequencing is frequently
used in combination with other typing methods, including MLST, to gain higher
resolution when assessing the epidemiological relatedness of isolates [28, 58].
MLST and flaA SVR sequencing were used in one study to characterise 47 isolates
from twelve outbreaks, and were shown to have comparable resolution to PFGE, the
current gold standard for C. jejuni typing (discussed in section 5.2.2 below) [58].
Other researchers have utilised PCR-RFLP of the fla genes in conjunction with PFGE
to gain high discrimination between isolates [89, 90]. Most studies that have
employed fla typing have indicated the value and high discriminatory ability of this
locus when used in combination with other genetic loci.
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2.5.2b Pulsed-Field Gel Electophoresis (PFGE)
PFGE, or macrorestriction profiling, was developed in 1984 as a way to separate
digested yeast chromosomal DNA [91], and has since evolved into a commonly
used, high resolution, whole genome typing methodology for a number of bacterial
pathogens. PFGE utilises infrequently cutting restriction enzymes to digest the
bacterial chromosome. The resultant DNA digest, which contains between five and
fifteen fragments depending on the enzyme used and the target sequence, is
subsequently electrophoresed in a pulsed electrical field within an agarose gel
matrix to separate the fragments on the basis of size [92, 93].
PFGE is generally considered the ‘gold standard’ for microbial epidemiological
studies due to the very high discrimination obtainable with this technique [58], but
there are also several limitations associated with this method. Foremost is the
unsuitability of PFGE for long-term and non-outbreak surveillance of C. jejuni
populations as this method is sensitive to small genetic changes, resulting in overly
complex restriction patterns that can obscure existing strain relationships [58, 93].
This phenomenon is exemplified by results of several investigations. One study
found that recombining C. jejuni isolates rapidly diverged from the parental strains
to an extent that the original PFGE pattern could no longer be deduced [53]. De
Boer and colleagues [53] concluded that PFGE was too sensitive for the
determination of genetic relatedness of strains, particularly when examining isolates
from diverse sources.
In a second study, epidemiologically-linked strains from a waterborne outbreak in
Canada differed in their PFGE banding patterns by an insertion of a 40kb fragment,
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conferred by the introduction of a Mu-like prophage [94]. Whilst the relationships
between the outbreak strains could still be determined, the study by Barton et al.
[94] showed that PFGE profiles can evolve rapidly, even between geographically
and temporally related isolates.
Another pitfall of PFGE is the tedious and time-consuming task of preparing the DNA
agarose blocks. Many commonly used enzymes do not readily digest the DNA of
some C. jejuni strains, and pre-treatment of DNA samples with formaldehyde is
sometimes necessary to deactivate DNAse activity in some strains prior to
electrophoresis [95]. Oftentimes, interlaboratory profile comparisons are hampered
by the use of inconsistent experimental protocols. These shortcomings prompted
the Centers for Disease Control and Prevention (CDC) to introduce an initiative
called PulseNet, which enables researchers to electronically compare PFGE patterns
in real-time between laboratories. PulseNet has been used extensively to detect
foodborne disease clusters and to identify common source outbreaks in E. coli
O157:H7, Salmonella, Shigella, Listeria and Campylobacter [96]. PulseNet requires
strict adherence to standardised protocols and labour-intensive normalisation of
electrophoretic patterns [70], and as a consequence has yet to be widely adopted in
many countries outside of the United States, including Australia. Irrespective of
these limitations, PFGE remains a powerful technique for detecting micro-evolution
in isolates that may be indistinguishable using MLST or MLEE [58, 70], and is
commonly used in reference laboratories to monitor Campylobacter outbreaks.
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2.5.2c Random Amplified Polymorphic DNA-PCR (RAPD-PCR)
RAPD-PCR analysis has been used to characterise C. jejuni isolates from a variety of
sources, such as for investigating the extent of genetic variability in strains isolated
from GBS and MFS patients [97-99]. RAPD-PCR uses arbitrary, approximately 10-
mer primers that bind to several regions over the target DNA and which generate
amplicons using conventional PCR. The size and number of amplicons can be
controlled by altering the stringency of the assay, such as annealing temperature or
MgCl2 concentration [79, 100]. Advantages of RAPD-PCR include use of the entire
genome to generate amplified fragments, and no requirement for prior knowledge
of the target DNA sequence, similarly to PFGE and AFLP [101]. RAPD-PCR has high
discriminatory potential and typeability, and is faster and cheaper than PFGE [79,
98].
The main problems associated with RAPD-PCR typing, inherent in all gel-based
methods, include poor reproducibility of assays between laboratories, most probably
due to lack of protocol standardisation, as well as difficulties in complex profile
interpretations, particularly for weak bands. RAPD-PCR analysis, PFGE and AFLP are
based on electrophoretic banding patterns generated by restriction enzyme
digestion and therefore do not provide the molecular basis for variation between
strains, and as such the relatedness of strains is subject to interpretation [67]. The
RAPD-PCR technique has not been widely accepted due to significant reproducibility
issues surrounding this method, with successful reproduction of results highly
dependent on strict adherence to protocols, reagents and even equipment [102].
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2.5.2d Amplified Fragment Length Polymorphism (AFLP)
AFLP is a highly discriminatory method that has been used to characterise a number
of bacterial pathogens, including C. jejuni [87]. AFLP involves the digestion of
genomic DNA, usually with two restriction enzymes, and ligation of specific
oligonucleotide adapters at the restriction sites. The adapters provide primer
binding sites for subsequent PCR amplification. Fluorescently labelled primers are
designed to hybridise to the adaptor sequence, and contain one or more nucleotides
extending beyond the restriction site at the 3’ ends. Under stringent PCR conditions,
only a subset of restriction fragments may be amplified (between 50 and 100), and
subsequently detected and discriminated on the basis of length (between 35 and
500 bp) using an automated fluorescence DNA sequencer [88, 103, 104]. AFLP can
be easily automated, allowing standardisation and high throughput of strains for
epidemiological investigations [88]. This technique is not dependent on prior
sequence knowledge, similarly to PFGE and RAPD-PCR [103]. AFLP and PFGE
provide comparable levels of discrimination as both typing methods interrogate
regions throughout the entire genome, although the exact basis for variation
between strains cannot be elucidated using these methods. Whilst AFLP is
reasonably rapid and easily standardised, the main drawbacks of equipment
expense and complexity of the patterns generated have limited its routine use [80].
2.5.2e Multilocus Sequence Typing (MLST)
MLST was devised in 1998 as a novel approach to bacterial genotyping, and utilised
Neisseria meningitidis as the model organism [77]. Since its inception, the MLST
scheme has been applied to forty bacterial pathogens of public health interest,
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including Staphylococcus aureus, Streptococcus pneumoniae and C. jejuni [54, 105,
106]. MLST involves PCR amplification and subsequent nucleotide sequencing of
internal fragments of conserved housekeeping genes. Analogous to MLEE,
housekeeping loci are chosen as these genes are not subject to immune selection,
and hence provide evolutionarily stable markers for comparing strains over large
time scales or from different geographical regions [77].
In general, seven housekeeping loci that are widely distributed throughout the
genome are sequenced using the MLST scheme. Direct sequencing of seven loci in
MLST provides resolution of strains comparable to the 15 to 20 loci used in MLEE
[77]. Sequence variants at each of the seven housekeeping loci, known as alleles,
are numbered based on previous submissions to the database. Each sequence type
(ST) is defined by a unique seven-digit ‘barcode’ at the seven loci. The combined
MLST database for C. jejuni and C. coli, established in 2001, currently contains over
2500 unique STs [32, 54] (Table 2). All MLST databases are publicly accessible at a
central online database [107].
Table 2. MLST Housekeeping genes of Campylobacter jejuni and Campylobacter coli
Housekeeping locus
Corresponding metabolic enzyme
No. of allelesa
Locus length (bp)
Gene positionb
aspA aspartate ammonia-lyase 187 477 96074..97480 glnA glutamine synthetase 257 477 658331..656901 gltA citrate synthase 219 402 1605251..1603983 glyA serine hydroxymethyltransferase 294 507 367219..368463 pgm phosphoglycerate mutase 367 498 402285..403763 tkt transketolase 301 459 1569190..1571088
uncA ATP synthase α subunit 213 489 111488..112993 aCurrent number of alleles for 2535 STs (as at 11/12/06) [32].
bDetermined from the sequenced NCTC 11168 C. jejuni strain [42].
MLST has proven powerful for the timely monitoring of worldwide trends in C. jejuni
and C. coli populations, and this technique possesses many advantages over other
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genotyping methods, such as PFGE and MLEE [108]. In particular, DNA sequencing
has minimal experimental variation, and therefore the precision and reproducibility
of data is high. Other advantages of MLST include the global accessibility of data
from a continuously expanding database, allowing electronic portability and
interlaboratory comparison of data without the requirement for reference isolates,
unlike PFGE and MLEE [77]. The use of live cultures may be eliminated as MLST can
be applied directly to clinical material or extracted DNA [108].
Applications of MLST include the ability to infer phylogenetic relationships between
isolates, measuring relative rates of mutation and recombination, and identifying
ancestral clones from which other STs have diverged to assist local and global
tracking of problematic clones [77, 109]. Characterisation of 194 [54] and
subsequently 814 [28] C. jejuni isolates using MLST demonstrated population
diversity and a weakly clonal structure in this organism, for which MLST is most
applicable [28]. The population structure of weakly clonal bacteria consists of clonal
complexes (CCs), or lineages, in which the isolates are considered to be derived
from a common ancestor [110].
Dingle and co-workers determined that the CC, as defined by MLST, is an
epidemiologically relevant measure for long-term investigations of C. jejuni
populations [28]. C. jejuni STs are grouped into CCs when two or more isolates
share identical alleles at four or more loci, with lineages named after the putative
founder ST of the complex (also known as the central genotype), e.g. the ST-45
complex [54]. Although of insufficient resolving power for short-term epidemiology,
for which PFGE is the gold standard, MLST provides an attractive method for
longitudinal epidemiological studies of bacterial pathogens such as C. jejuni, due to
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the increasing data generated by this typing scheme. MLST has revealed that some
CCs exhibit host specificity; for example, the ST-443, ST-446, ST-433, ST-460, ST-
573, ST-574, ST-661, ST-1150 and ST-607 CCs are associated with human
infections and poultry, whereas ST-952, ST-1332, ST-1304, ST-1325, ST-1287, ST-
692 and ST-682 consist of environmental isolates that have yet to be identified in a
human host [32].
MLST suffers disadvantages that make routine use in bacterial genotyping
impractical for public health laboratory implementation. Foremost is the time,
labour and cost associated with DNA sequencing, rendering large-scale throughput
difficult without substantial effort and expense [111]. Secondly, MLST requires post-
PCR manipulation of amplicons, which can result in sample contamination. The
entire profile of alleles must be sequenced before the ST identity can be
determined, requiring both sequencing of forward and reverse DNA strands. Many
smaller laboratories may not have sequencing apparatus and therefore sequencing
performed by another laboratory is at a further cost. MLST is amenable to semi-
automation using 96-well format liquid handlers, although this technology is
currently costly and time consuming [70, 112].
2.5.2f Single-nucleotide polymorphism profiling
Single-nucleotide polymorphism (SNP) profiling has been developed as a rapid and
cost-effective alternative to more cumbersome sequence-based genotyping
methods used for characterising bacteria [113]. SNP-based studies of bacteria have
predominantly used the vast amount of comparative sequence data generated from
MLST to identify SNPs that are informative genotyping targets [113-116]. Best and
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co-workers [59, 117] have developed SNP profiling methods for characterising the
CCs of C. jejuni and C. coli based on the real-time PCR platform (discussed in
section 6). Fourteen SNPs were identified that delineated the six major CCs
associated with human infection (ST-21, ST-45, ST-48, ST-61, ST-206 and ST-257),
and were interrogated using fluorescently labelled TaqMan® probes (discussed in
section 6.2.1). SNPs were selected within the most common allele/s of a CC e.g.
allele 1 of the glnA locus, characteristic of the ST-21 CC, and allele 10 of the gltA
locus, characteristic of the ST-45 CC [59, 117].
The advantages of the SNP assay over MLST include fast turn-around-time and
cost-effectiveness, allowing the timely characterisation of strains for public health
investigations [59, 117]. The major pitfall of the Best et al. SNP method is the high
rate of false-negative STs obtained using the fourteen SNPs, which ranges from 17
to 54% of STs. There are currently 42 recognised C. jejuni/C. coli CCs of which 31
have been associated with human infection [32], and therefore the CC-specific SNPs
are limited in their applicability for genotyping the entire species. More SNPs would
need to be incorporated into the assay as emerging CCs are identified or if non-
human CCs were also examined, potentially reducing the cost and time benefits of
the SNP method. Irrespective of these shortcomings, the SNP approach provides
preliminary CC designation in a substantially reduced time, effort and expense
compared with complete MLST characterisation and is an attractive method for
high-throughput genotyping applications.
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2.5.2g Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR) typing
Genome sequencing of several C. jejuni strains has revealed few repeat regions in
this organism. However, one repeat region, the clustered regularly interspaced
short palindromic repeat (CRISPR) region [118], has been identified in both NCTC
11168 and RM1221. CRISPRs are a class of short sequence repeats that have been
found in nearly all archaeal and half the bacterial genomes sequenced to date, and
are the most widely distributed family of repeats in prokaryotic genomes [119]. A
unique characteristic of CRISPRs is the presence of nearly exact direct repeat (DR)
sequences ranging between 21 (in Salmonella typhimurium) and 37 bp (in
Streptococcus pyogenes) in length interspaced by similarly sized, highly diverse
spacer sequences [119, 120]. The DRs, whilst highly conserved within a species,
differ substantially between species and can exist as one of several loci on the
prokaryotic genome [119]. The number of DRs and the composition of the spacer
sequences also vary markedly between strains. In C. jejuni, CRISPRs are located at
one locus and characteristically contain the 34 bp DR motif
TTTTAGTCCCTTTTTAAATTTCTTTATGGTAAAA interspaced by 32 bp spacer sequences
(Figure 2). Approximately 90% of C. jejuni strains contain CRISPRs, ranging from
one DR (sans spacer) to eight repeats [11].
Figure 2. Schematic of the CRISPR locus in Campylobacter jejuni. The CRISPR locus of NCTC
11168 is encoded by Cj1520 and contains five direct repeats (DRs); in RM1221 the CRISPR locus is
located between CJE1693 and CJE1694 and contains four DRs. The CRISPR locus is absent from 81-176.
Consensus Sequence
Consensus Sequence
DR (34 bp) DR DR
Spacer (32 bp) Spacer
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CRISPRs, like other repeat regions, represent an interesting locus for genotyping as
they are thought to evolve at an accelerated pace compared with more stable loci in
the genome, such as the housekeeping genes used in MLST. There is an abundance
of variation residing within CRISPR spacers that can potentially be utilised for
discriminating genotypes that may remain indistinguishable by other methods. The
most comprehensively studied CRISPR locus is in Mycobacterium tuberculosis,
which spawned the development of ‘spoligotyping’ [121], now a commonly used
method for characterising this species. Spoligotyping is a reverse line blot
hybridisation technique that involves PCR amplification of the entire M. tuberculosis
CRISPR locus using a non-labelled and a biotinylated primer. The PCR product is
incubated with multiple synthetic spacer oligonucleotides that are covalently bound
to a membrane. Hybridisation of the labelled amplicon is detected and used to
determine which spacers are present in a strain by either the presence or absence
of bands corresponding to the membrane-bound spacer sequences [121].
An interesting feature of the C. jejuni and C. coli CRISPRs is their small DR number
(between two and eight) but almost limitless spacer diversity; 170 different spacers
were found in 137 Campylobacter strains [11]. In contrast, M. tuberculosis CRISPRs
contain approximately six to 50 DRs but the number of different spacers is limited
to around 70 [122] and, unlike M. tuberculosis, comparatively little CRISPR
characterisation has been performed on C. jejuni and C. coli isolates. For these
reasons spoligotyping is not a feasible method for characterising C. jejuni and C.
coli CRISPRs. Moreover, spoligotyping involves intensive post-PCR manipulations
and suffers from weak hybridisation signals, probably as a consequence of sequence
diversity, which may result in ambiguous profile interpretations [121].
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Only one research group has characterised CRISPRs from C. jejuni and C. coli
isolates. Schouls and colleagues [11] used PCR amplification and DNA sequencing of
the CRISPR locus to compare its performance with AFLP and MLST. All three
methods were shown to be comparably powerful in identifying outbreaks and
displayed similar genetic clustering of isolates. However, one pitfall of the C.
jejuni/C. coli CRISPR study was the low typeability of isolates. Nineteen of the 184
(10%) isolates tested were CRISPR-negative, including two of the four examined C.
coli strains, although the authors acknowledged that the high prevalence of
CRISPR-negatives could be attributable to primer binding site diversity. An
additional 15% of isolates harboured a single DR without a spacer region, whereas
the remaining 75% of isolates contained an average of five repeats. Despite
potential typeability issues, Schouls and co-workers [11] demonstrated that the
combination of MLST with CRISPR sequencing enabled the number of STs to be
expanded from 117 to 158 genotypes upon addition of CRISPRs to ST identity. It
remains to be seen whether CRISPRs will be widely adopted as a genotyping tool for
C. jejuni and C. coli.
2.5.2h DNA Microarrays
There are limited multiple strain genome datasets currently available for C. jejuni.
In the interim, other less intensive comparative genomics methods, such as DNA
microarrays, have been devised and used extensively to characterise C. jejuni
strains [123]. DNA microarray technology enables large-scale, genomic
interrogation of many bacterial pathogens, and has provided considerable insights
into intra-species genetic diversity and microbial evolution [49]. Two applications of
DNA microarrays, complementary DNA (cDNA) expression arrays and comparative
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genome hybridisation (CGH) arrays, have been developed for whole-genome
bacterial characterisation; the latter are relevant to genotyping and will be
discussed in further detail in this review.
There are two CGH approaches that have been adopted for studying intra-C. jejuni
genetic composition; those that are constructed from library clones or PCR products
generated for each open reading frame (ORF) of a strain whose genome has
previously been characterised [48-50], and those constructed from shotgun library
probes of a tester strain that remains uncharacterised by genome sequencing
[124]. Array construction involves the robotic spotting of each clone, probe or PCR
amplicon onto a glass microscope slide (or less commonly, a nylon slide), usually in
duplicate or triplicate, followed by immobilisation of the amplicons and appropriate
washing and drying of the slide. Once constructed, the array is typically
interrogated using (a) fluorescently labelled, restriction enzyme-digested genomic
DNA (gDNA) of the strain from which the array was constructed, which acts as a
common reference for all hybridisations, and (b) differentially fluorescently labelled,
restriction enzyme-digested gDNA of a tester isolate. Following hybridisation the
array is scanned and fluorescence intensities at each spot measured. In most
instances the fluorescent Cy3 and Cy5 dyes (green and red, respectively) are used
for differential labelling [125].
The first C. jejuni CGH array was constructed using the pUC18 clone library
generated from genome sequencing of NCTC 11168 [48], and has been
subsequently used in another study to correlate interstrain diversity with phenotypic
virulence [126]. In the Dorrell et al. study [48], approximately 1,300 core genes
were universally present in the eleven test isolates, with the remaining genes
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(21%) absent or highly divergent in one or more strain/s. Genes within the LOS, FM
and CAP loci, as well as genes encoding R/M systems and iron acquisition, were
found to be divergent or absent in many of the examined strains, accordant with
genome sequencing studies. The pUC18 clone library microarray was crude in
construction and hence it was acknowledged that many false positive signals were
generated, due to the presence of overlapping adjacent genes in many of the clones
[48]. Nevertheless, the pUC18 array was central in pioneering CGH array
construction for examining intra-species diversity of C. jejuni.
To overcome the limitations of the pUC18 clone array, six gene-specific microarrays
have since been independently constructed that predominantly encompass the
~1,600 ORFs of NCTC 11168 [49, 50, 67, 124, 127, 128]. The CGH array
constructed by Leonard and co-workers was the first C. jejuni array constructed
from PCR amplicons for each ORF of NCTC 11168 [67] and also included additional
clones from the putative virulence plasmid of 81-176, pVir [51]. The NCTC 11168-
pVir array was used to assess concordance of CGH of sixteen outbreak C. jejuni
isolates with RAPD-PCR and Penner serotyping profiles [67] and to investigate
potential genetic differences from C. jejuni strains implicated in GBS versus non-
GBS strains [127]. Both CGH studies performed by Leonard and co-workers [67,
127] showed several areas of divergence within the genome between C. jejuni
isolates, such as the LOS, CAP and FM loci, which correlated with divergent regions
observed in the pUC18 library array of Dorrell et al. [48]. An additional locus
encoding genes involved in sugar modification and transport (uxaA) and genes
within pVir were also divergent between strains [67, 127].
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The initial CGH analysis performed by Leonard et al. [67] showed a correlation
between gene complement with RAPD-PCR clusters, but in some isolates a lack of
correlation between the two methods was observed, suggesting recent rapid genetic
exchange that went undetected by RAPD-PCR. However, the validity of using RAPD-
PCR for assessing the relatedness of strains is debatable, for reasons mentioned
previously (discussed in section 5.2.3). The second NCTC 11168-pVir CGH array
study focused on comparing C. jejuni isolates implicated in GBS from non-GBS
strains to identify unique differences within the genome that were associated with
GBS. No association was found between gene content and GBS outcome, a finding
consistent with other studies and which further consolidates the role of both C.
jejuni- and host-specific mechanisms in the development of adverse clinical
sequelae [127, 128]. These CGH studies showed the efficient delineation of
epidemiologically distinct isolates based on gene presence or absence and
confirmed the genetic heterogeneity of C. jejuni, and were integral in paving the
way for further CGH studies.
Pearson and co-workers constructed a similar array to Leonard et al. [67]
comprising only the NCTC 11168 ORFs, which was used to assess the genomic
diversity of 18 C. jejuni isolates from diverse sources [49]. This study identified
1,385 ORFs (84%) that were universally present and predicted to be involved in
vital cell functions, such as energy metabolism, cell division, peptide secretion, and
synthesis of macromolecules [49]. The remaining 269 genes (16%) were absent or
highly divergent in one or more isolates, 136 (50%) of which were localised within
seven plasticity regions (PRs) of NCTC 11168. The gene content of three of these
PRs encoding the CAP, LOS and FM loci were also identified as variable in the pUC18
array [48]. Further regions of variability include the molybdenum transport
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apparatus, the pantothenate biosynthesis genes, uxaA, ABC transporters, putative
acyl carrier proteins for fatty acid biosynthesis, and outer membrane proteins [49].
Taboada and colleagues [50] also constructed an array covering the ORFs of NCTC
11168, which was interrogated using 51 C. jejuni strains from food and clinical
sources. The raw data from the Taboada et al. [50] study was integrated with raw
CGH data from the prior CGH studies [48, 49, 67] to allow uniform meta-analysis of
97 strains from all CGH datasets. Previous CGH studies had been unable to
differentiate between gene divergence and absence, limiting the usefulness of the
array data by overlooking the fundamental differences between these two genetic
states [123]. A unique feature of the CGH study carried out by Taboada and co-
workers [50] was the differentiation of not only gene presence or absence, but gene
divergence also, which was possible by defining appropriate cut-offs for these gene
states based on the amplitude of their array signal. These criteria assigned 350
genes as divergent or absent in multiple strains whereas 249 were uniquely variable
in only a single strain, and nearly half of these 599 divergent genes mapped to PRs.
122 of these genes were further classified as highly divergent or absent, allowing
such genes to be distinguished from moderately divergent genes. Whilst the meta-
analysis data revealed another nine PRs (defined as loci containing three or more
adjacent genes that were absent in two or more strains) in NCTC 11168, CGH
demonstrated that the majority of the C. jejuni genome was stable [50].
A combined NCTC 11168-RM1221 array has recently been developed and used to
examine genomic diversity in 35 epidemiologically distinct C. jejuni isolates [47].
Many aspects of this array have been covered in section 5.2.8.1 and will not be
covered again here. The Parker et al. [47] array comprised 1,530 genes from NCTC
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11168 and 227 unique genes from RM1221, including genes from within the four
CJIEs. Two additional PRs, named 17 and 18, were identified; PR17 spans Cj0258 to
Cj0263 and PR18 encompasses Cj0857c to Cj0860c (see Table 1, page 15). 385 of
the 1,786 (22%) 11168-RM1221 genes were absent or highly divergent in at least
one isolate, concurrent with Taboada and co-workers [50].
The largest C. jejuni CGH study to date was carried out using 111 C. jejuni strains
interrogated on another independent NCTC 11168 array [128]. The tested strains
originated from humans with a range of disease outcomes and from diverse animal
and environmental sources. Champion and co-workers [128] used the array data in
combination with Bayesian-based algorithms to perform comparative phylogenomics
of the isolates. The phylogenomic analysis revealed that the isolates formed two
distinct clades consistent with source; a livestock clade, containing approximately
90% of animal-derived isolates, and a non-livestock clade that harboured
environmental isolates. A cluster of six genes (Cj1321 to Cj1326) within the flagellin
glycosylation locus were strongly associated with the livestock clade and confirmed
to be present in an additional six isolates sampled from chicken. Interestingly, the
human isolates were roughly equally distributed between the two clades, suggesting
that both sources are important reservoirs for C. jejuni transmission to humans.
Similarly to other CGH studies, no association between specific clinical outcomes
(such as GBS) and gene content was identified, although it remains to be
determined whether pan-C. jejuni species arrays may reveal greater insight into the
genetics behind particular clinical manifestations [128].
Despite their invaluable contribution in characterising intra-species genomic
variation, DNA microarrays suffer some recognised disadvantages. One
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disadvantage of CGH arrays is the inability to detect minor genetic changes, such as
SNPs, gene rearrangements or small insertions and deletions, which may lead to
functional differences between strains due to truncation or inactivation of gene
products. Only ORFs are usually included on arrays, with non-coding intergenic
regions containing non-translated RNA and promoter elements excluded [49].
Further, DNA arrays rely on efficient hybridisation between the immobilised gene
and the target DNA, with a negative log2 ratio (ratio of tester strain signal to control
strain signal) obtained in both cases of sufficient sequence diversity (gene
divergence) and gene absence. The inability to efficiently discriminate gene
divergence and absence ignores the implicit evolutionary and biological differences
between these two groups [123]. This phenomenon is exemplified by the apparent
‘absence’ of flaA and flaB in many array studies, despite the universal presence of
these genes [49]. Other factors, such as inconsistent probe length, have been
shown to influence hybridisation kinetics and can lead to incorrect gene
classification. Nevertheless, designation of genes as present or absent, based on log
ratios, can be achieved with high confidence if appropriate thresholds are
implemented as shown in the Taboada et al. [50] CGH array study.
A specific drawback of the C. jejuni DNA microarrays discussed in this review is that
often only the NCTC 11168 ORFs are immobilised, prohibiting the identification and
interrogation of additional genes that may be present in other strains [48, 129].
The arrays of Leonard et al. [67] and Parker et al. [47] have attempted to
circumvent this shortcoming by including additional chromosomal and plasmid-
borne genes. An alternative for identifying C. jejuni genes absent from NCTC 11168
has also been described. Ahmed and co-workers [129] used a subtractive
hybridisation approach to identify genes present in the highly invasive strain 81116
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that were absent in NCTC 11168. Twenty-three clones present in 81116 were
identified as absent in the sequenced NCTC 11168 strain, six of which shared
similarity to R/M systems found in other bacteria and others thought to be
associated with colonisation. Interestingly, another six clones did not share
homology with any bacteria [129].
A similar approach was taken by Poly et al. [124], in which C. jejuni strain ATCC
43431 shotgun sequence CGH array was compared with NCTC 11168 to identify
ORFs unique to ATCC 43431. 130 complete and incomplete ORFs, encoding the
LOS, CAP, R/M systems and integrases were found in ATCC 43431 that were absent
in NCTC 11168. The G+C content of these unique genes was found to be
substantially lower than the NCTC 11168 genome (26% versus 30.6%) suggesting
that the additional ATCC 43431 genes have been acquired by horizontal gene
transfer from another species [124]. The vast number of C. jejuni CGH arrays
described in the literature has highlighted the increasing popularity of these
methods for whole-genome comparisons of bacteria, and future CGH arrays are
likely to contribute even further to our understanding of the genetic diversity in C.
jejuni in the guise of pan-species CGH arrays.
2.6 Real-time PCR-based methodologies
2.6.1 Introduction
High-throughput bacterial genotyping methods that are inexpensive, discriminatory
and rapid are highly sought after by diagnostic and research industries. In this
context, a single-step procedure that allows genotyping information to be obtained
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directly from genomic DNA or clinical material is highly desirable. All of the
previously discussed molecular genotyping methods require end-point
manipulations, such as gel electrophoresis (as in AFLP, RAPD-PCR, PFGE and RFLP)
or amplicon clean-up (such as in MLST and flaA SVR sequencing). Real-time PCR is
based on the principles of conventional PCR but with continuous monitoring of
product accumulation [130]. Real-time PCR provides an ideal single-step, closed-
tube genotyping platform by negating the need for end-point detection and
manipulation of amplicons, reducing contamination issues [131]. Applications of
real-time PCR include disease diagnosis and monitoring of infection loads during
therapy, gene expression quantification, pathogen identification and SNP detection
[132]. It is therefore unsurprising that real-time PCR instruments are rapidly
becoming universal within clinical laboratories for molecular diagnostics
applications.
Real-time PCR can be broken down into two components; a kinetic PCR and a melt-
curve component (Figure 3). All real-time PCR instruments are designed to perform
the kinetic PCR component and most are also equipped with melt-curve capabilities.
In kinetic PCR, the amount of fluorescence is proportional to the amount of
accumulated amplicon produced during thermocycling. As the PCR enters
exponential phase the greatest increase in fluorescence is detected, and it is during
this phase that the PCR curve crosses a predetermined threshold, termed ‘cycles to
threshold’ (CT). The CT is a quantitative measure that can be used to determine the
amount of starting DNA in a sample and to measure the efficiency of a PCR. As the
PCR reaches saturation, the PCR curve begins to asymptote as reagents are
exhausted [133].
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Figure 3. Example of kinetic PCR (top panel) and DNA melt curves (bottom panel) in real-time
PCR. Kinetic PCR measures the exponential increase in fluorescence as a result of increased amplicon
production during thermocycling. Following kinetic PCR, amplicons are melted over increasing
temperature increments to provide a characteristic melt curve profile. The melt curve can also be used to
detect primer dimer and non-specific amplification, which are usually seen as distinct peaks below 75oC
[132].
Using certain real-time PCR chemistries (intercalating dyes and labelled fluorescent
primers) and most real-time PCR instruments, a melt curve of amplicons can be
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generated post-PCR by plotting the negative first-derivative of the melt curve
fluorescence against temperature; in other words, amplicon denaturation is
observed as the rapid loss of fluorescence at its melting temperature (Tm) peak
[134]. Melt curves are analogous to detecting amplicons by gel electrophoresis and
are dependent on the G+C content of the DNA duplex, the absolute order of the
nucleotides and amplicon size [132].
There are two main types of chemistries used in real-time PCR; those specifically
designed for SNP characterisation, and generic chemistries that are capable of
detecting essentially any genetic polymorphism, including SNPs. SNPs are the most
common class of polymorphism and have had substantial applications in
pharmacogenetics, antimicrobial resistance profiling and bacterial genotyping [59,
113, 135, 136]. Keeping these points in mind, this review focuses on the most
commonly used real-time PCR-based methods and detection chemistries,
concentrating on their merits and shortcomings from the perspective of high-
throughput bacterial diagnostics.
2.6.2 Probe-based methodologies
2.6.2a TaqMan® probes
The TaqMan® 5’ exonuclease assay uses competitively binding, dual-labelled
fluorogenic oligonucleotide probes to differentiate between the nucleotide variants
of a SNP [137]. The probe sequences typically differ from each other only at the
position of the SNP. The original TaqMan® probes were between 20-30 bp in length,
but the addition of a 3’ minor groove binding (MGB) domain has improved assay
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efficiency due to their shorter length and increased binding efficiency, and as a
consequence MGB probes are now more prolific than their predecessor. The MGB
probes, generally between 10-15 bp in length, possess a quencher molecule at their
3’ end and a reporter fluorophore at the 5’ end (Figure 4) [138].
Figure 4. Schematic of the TaqMan® 5’ exonuclease assay. (a) A complementary minor groove
binder (MGB) TaqMan® probe binds to DNA template during the annealing step of the PCR. During
extension, the Taq DNA polymerase displaces the probe using its 5’ to 3’ exonuclease activity. Cleavage
of the TaqMan® probe liberates the reporter (R) molecule from the probe and hence its proximity to the
quencher (Q) molecule, resulting in an increase in fluorescence. (b) Mechanism for differentiation of
polymorphisms using TaqMan® probes. Two probes containing different reporter fluorophores are used to
discriminate between polymorphisms. Only the probe that is complementary to the polymorphism in the
tester DNA will bind; a mismatch between probe and template is less stable than the complementary
probe-template complex and dissociates prior to cleavage. FAM and TET are commonly used reporter
fluorophores. Figure adapted from reference 135.
The reporter fluorophores for each probe contain differing emission wavelengths to
allow discrimination between polymorphisms in a multiplex PCR. During the
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annealing-extension step of PCR, the probes competitively anneal only to their
complementary sequence encompassing the SNP of interest. When the probe is
intact, no detectable reporter fluorescence is observed due to the proximity of the
quencher molecule to the reporter fluorophore, as the emission spectrum of the
quencher molecule absorbs the excitation spectrum of the reporter moiety [139].
Once the probe has bound to its exact target sequence, Taq DNA polymerase
utilises its endogenous 5’ to 3’ exonuclease activity to degrade the hybridised probe
from the template strand. Probe degradation releases the quencher molecule from
proximity of the reporter dye with a subsequent increase in fluorescence, which is
measured by the real-time PCR instrument [137].
There are currently four different reporter fluorophores available for TaqMan®
probes; FAM, VIC, TET and NED, allowing up to four targets within a single tube to
be interrogated. Because of the assay robustness and the ability to multiplex,
TaqMan® MGB probes have been used extensively to characterise SNPs for many
applications, such as pharmacogenetics and bacterial genotyping [59, 135]. The
popularity of the TaqMan® system is evident from a recent PubMed search
(performed 08-01-07), which returned nearly 2000 hits on the topic.
However, the TaqMan® assay is not without its shortcomings. The primary
disadvantage of TaqMan® from a routine diagnostics perspective is the expensive
start-up costs of the probes and consumables (probes are approximately AU$500
each for 1000 reactions), particularly if multiple SNPs are examined, although the
cost per assay declines as throughput increases [140]. Tri- and tetra-morphic SNPs
are commonly encountered in bacterial sequences, which although can be
interrogated within a single tube in the TaqMan® system, would require extensive
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optimisation to avoid competitive priming issues [141]. Amplicon specificity cannot
be confirmed by melt-curve analysis, and therefore non-specific amplification and
primer dimer can only be detected by gel electrophoresis [132]. Another drawback
of the TaqMan® assay includes design constraints; the success of the assay is
dependent on conserved sequence surrounding the SNP of interest at which the
probes bind, and therefore may be unsuitable for highly polymorphic regions.
2.6.2b Molecular beacons
Molecular beacons are dual-labelled oligonucleotide probes that, unlike the linear
TaqMan® probes, form stem-and-loop structures when free in solution (Figure 5).
The loop consists of nucleotides complementary to the target sequence of interest,
whereas the stem is formed by annealing of two ‘arm’ sequences surrounding the
loop that are complementary to each other [142]. In contrast to TaqMan® probes,
molecular beacons use a conformational change rather than enzymatic cleavage to
detect hybridisation which results in the subsequent increase in fluorescence. When
in stem-and-loop conformation, the donor and the quencher are in close proximity
and hence the reporter is quenched.
As the molecular beacon binds to its complementary target, the beacon undergoes
a favourable conformational change in which the probe-target hybrid is bound by
more bases than the stem structure of unbound beacon, resulting in detectable
increase in reporter fluorescence as the two moieties are separated from each other
[143]. The hairpin stem renders molecular beacons more specific than the linear
TaqMan® probes as the beacons can only bind to an exactly complementary
sequence.
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Figure 5. Conformation and mechanism of action of molecular beacons for SNP genotyping.
When free in solution, molecular beacons form an energetically favourable stem-and-loop structure. The
‘arms’ of the probe are complementary to each other and harbour terminal reporter and quencher
molecules, resulting in absorption of reporter fluorescence. Molecular beacons will only linearise upon
binding to an exact sequence, allowing precise discrimination of polymorphisms at a SNP. Upon binding
of the beacon to its exact target sequence, the reporter fluorophore is relinquished from close proximity
to the quencher resulting in fluorescence emission. Figure adapted from reference 141.
The high specificity of molecular beacons has been demonstrated with a four-state
SNP, in which a polymorphism could be successfully differentiated from the other
polymorphisms in a single tube without competitive priming issues [144]. This
specificity and low background fluorescence is due to the inability for mismatched
probe-template hybrid formation and the highly quenched nature of the excess
unbound beacons. Moreover, the quencher used in molecular beacons is non-
fluorescent and therefore does not interfere with the emission spectrum of the
reporter fluorophores in multiplexed reactions [143, 144].
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Similarly to TaqMan® probes, molecular beacons are difficult to design and, because
they are dual-labelled, are expensive to produce. On top of the cost of the dual-
labelled probe, non-labelled primers for performing the PCR are also required. All
probe-based methods are unable to detect amplified DNA directly, and therefore the
signal is affected by probe hybridisation efficiency and potential for high background
noise [139]. Due to the rigidity of the probe-target hybrid and the absolute need for
exactly complementary sequence, molecular beacons require stringent design and
are likely unsuitable for target sequences which are highly polymorphic, such as
many bacterial and viral genes.
Other probe-based methods, such as Invader® [145], MGB Eclipse™ probes [146]
and Scorpion® probes [147] have been developed as alternative procedures for
characterising SNPs on the real-time PCR platform, but have not been as widely
adopted as TaqMan® and molecular beacons and are therefore not covered in this
review.
2.6.3 Generic chemistries
Double-stranded DNA (dsDNA)-specific intercalating dyes are commonly used for
real-time PCR applications due to their cost-effectiveness and flexibility. Examples
of dsDNA-specific dyes include ethidium bromide [130], SYBR® Green I [148],
EvaGreen™ [149], LC Green® [150], SYBR® GreenER™ (Invitrogen), SYTO® 9
[151], 2005) and BEBO [152]. Unlike the sequence-specific chemistries described
earlier, dsDNA-specific dyes are considered generic as they indiscriminately bind to
all dsDNA species, irrespective of the DNA sequence. dsDNA-binding dyes operate
by emitting very little or no fluorescence when free in solution. However, during
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PCR thermocycling, dsDNA species accumulate and bind the intercalating dye,
resulting in a large quantum yield increase in fluorescence which is detected by
excitation with the appropriate wavelength of light [132].
SYBR® Green I has been the most widely adopted dsDNA-specific dye, although
there are purported disadvantages with the use of this chemistry. The most
substantive of these limitations is the potential for concentration-dependent
inhibition of the PCR [150, 151]. Moreover, Giglio and co-workers [153]
demonstrated that SYBR® Green I binds preferentially to G+C-rich amplicons during
multiplex PCR. The phenomenon of ‘dye jumping’ during denaturation of amplicons
occurs due to the inability to use SYBR® Green I at saturating concentrations. As
low-G+C or heteroduplex pockets of dsDNA begin to melt, SYBR® Green I molecules
redistribute to unmelted higher G+C or homoduplex regions, potentially masking
small differences in melting behaviour [153, 154] (Figure 6).
Next-generation dsDNA binding dyes, such as SYTO® 9 and LC Green®, have since
been developed that can be used at saturating concentrations (discussed further in
section 7.1). dsDNA dyes are limited in their ability to detect multiple targets within
a single reaction due to their non-specificity, and are therefore unsuitable for
multiplexing. DNA binding dyes may increase the stability of dsDNA species,
increasing non-specific primer binding to PCR artefacts such as primer dimers and
spurious amplification products [155].
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Figure 6. Difference between non-saturating and saturating double-stranded DNA (dsDNA)-
binding dyes. Some dsDNA dyes such SYBR® Green I cannot be used at saturating concentrations due
to their inhibitory effect on PCR. The non-saturating composition of SYBR® Green I molecules in the DNA
duplex results in the redistribution or ‘jumping’ of the dye during denaturation to homoduplexes or
regions of higher G+C content. This phenomenon results in no detectable change in fluorescence signal,
even in the presence of a heteroduplex, such as a SNP. In contrast, saturating dyes such as SYTO® 9 and
LC Green® PLUS+ can discriminate heteroduplexes, increasing assay sensitivity and theoretically
enabling all sequence changes to be detected. Figure adapted from reference 154.
2.6.4 Allele-specific PCR
Allele-specific PCR (AS PCR) is an attractive option for SNP interrogation as this
method is cost-effective and adaptable to highly diverse sequences. ‘Allele’
generally refers to multiple nucleotide differences between sequence variants; for
simplicity, however, the term allele and SNP are used interchangeably herein.
Allele-specific PCR relies on the intrinsic properties of Taq polymerase to
discriminate between polymorphisms [157]. Under ideal conditions, Taq cannot
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extend primers containing a 3’ mismatch as this enzyme lacks 3’ to 5’ exonuclease
activity (Figure 7). However, mismatched primer-template complexes can be
inefficiently extended, albeit at later cycles, providing template for logarithmic
amplification [141, 157]. Mismatch products may not be readily distinguished from
matched reactions at the end point of the PCR assay without extensive optimisation
for each SNP of interest [158]. Real-time monitoring of allele-specific PCR product
accumulation by incorporation of dsDNA-binding dyes or fluorogenic primers
obviates the requirement for extensive optimisation, as differences between
matched and mismatched allele amplification efficiency can be monitored on a per-
cycle basis using CT [133]. The difference in CT between matched and mismatched
reactions, termed ∆CT, can be used to determine the polymorphism at a SNP in real
time [159].
ΔCT
NTC
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Figure 7: Principle of allele-specific (AS) real time PCR for SNP genotyping. The AS primer is
designed with the ultimate 3’ base in alignment with the SNP. Due to a lack of 3’ to 5’ proofreading
ability of Taq DNA polymerase, extension of the primer will only occur efficiently when the 3’ base
complements the template (matched AS primer). In the mismatched AS primer reaction, the 3’ end of
the primer is non-complementary to the template, and extension will not occur efficiently. The difference
in amplification efficiency between matched and mismatched reactions can be quantitatively measured as
the change in cycles to threshold (∆CT).
2.6.5 Fluorescently labelled primers
An alternative to fluorogenic probes and dsDNA binding dyes are fluorescently
labelled primers. Fluorogenic primers, like other chemistries, can be used for gene
detection and SNP interrogation by AS PCR [155]. The first-generation fluorogenic
primers were designed with both reporter and quencher moieties. The primers
contained a 5’ hairpin structure and the quencher and reporter molecules in close
proximity of the stem and hairpin loop. Similarly to the FRET probes such as
TaqMan®, the primers are unable to emit detectable fluorescence when free in
solution. When incorporated into an amplification product, the hairpin structure
becomes linearised; fluorescent signal is generated and measured as the primer is
extended by a DNA polymerase. The incorporated primer then acts as a template
for subsequent PCR cycles (Figure 8) [155].
Unlike probe-based methods, fluorogenic primers result in the incorporation of the
primer into the amplicon, which enables the amplimer to be directly detected [139].
As the primer is integrated into the PCR product, the background interference is
minimal, allowing quantification over a wide dynamic range. The stem-loop
structure is highly stable at annealing temperature and special buffer and
temperature conditions, such as those required for hybridisation-based methods,
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are not necessary. By exclusion of probes, the reaction kinetics and constituents are
simplified and the cost of the assay is reduced [139, 155].
Figure 8. Schematic representation of the first- and second-generation fluorogenic primer
systems. The first- and second-generation fluorogenic primers contain a stem-and-loop structure that
confers little or no reporter fluorescence emission when the primers are free in solution. Upon binding to
a complementary DNA target, the fluorogenic primers act in concert with an unlabelled primer during
PCR to produce labelled amplicons. The subsequent integration of the fluorogenic primer linearises the
stem-and-loop conformation, resulting in restoration of reporter fluorescence. The main differences
between the first- and second-generation primers are in their stem-and-loop position and the number of
attached moieties; the stem-and-loop structure of the second-generation fluorogenic primers results in
self-quenching of the reporter, unlike the first-generation primers, which require the quenching moiety.
Figure adapted from references 138 and 154.
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The second-generation of fluorogenic primers, which are marketed by Invitrogen
under the proprietary LUX (Light Upon Extension) trademark, contain a single
reporter fluorophore close to the 3’ end and lack a quencher moiety. Unlike the
first-generation fluorogenic primers, the 5’ end of the LUX™ primer contains a
blunt-end stem-and-loop of five to seven nucleotides when free in solution. The
blunt-end hairpin primary and secondary conformation essentially quenches the
conjugated 3’ reporter fluorophore, as does the presence of a 3’ G or C at the
ultimate base [155]. The reporter molecule only increases its fluorescence emission
after deconstruction of the stem-and-loop structure during incorporation into a PCR
product. The change to linearised form results in an 8-fold increase in fluorescence;
in comparison, the first-generation fluorescent primers yield a 35-fold increase in
signal-to-background ratio upon linearisation [139, 155].
The main benefits of the single-labelled LUX™ primer over its predecessor include
the increased efficiency and specificity of the reaction and a reduction in assay cost.
Efficiency and specificity are improved in the LUX™ primers as they more closely
mimic generic, unlabelled primers, minimising the potential formation of primer
dimer artifacts and mispriming. The manufacturing cost is reduced as only a single
fluorophore is attached to the LUX™ primer and the purification is less rigorous
compared with dual-labelled primers and probes. Currently LUX™ primers are about
half the price of TaqMan® probes and unlike TaqMan®, are amenable to amplicon
melt curve analysis and size determination using gel electrophoresis [155].
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2.6.6 Melting temperature (Tm) shift primers
The inability to multiplex using dsDNA-binding dyes is the most significant downside
of these chemistries for real-time PCR applications. In contrast to fluorogenic
probes and primers, dsDNA-binding dyes require separate reactions to differentiate
polymorphisms using the AS PCR procedure, resulting in increased expenditure of
reagents and reduced throughput. Melt-curve genotyping using dsDNA-binding dyes
enables the conversion of AS real-time PCR to single-tube format, but requires the
Tm of the two alleles under investigation to be sufficiently different. Using
conventional real-time PCR apparatus, a single base change may not affect the Tm
sufficiently to facilitate discrimination of alleles [140].
One method of bypassing this weakness is the use of a 5’ GC-clamped AS primer in
the real-time PCR assay [140, 158, 160]. This method involves designing
(generally) two AS primers; one complementary to one polymorphism, and the
other complementary to the other polymorphism but with an additional 5’ GC
clamp. A generic common primer for both AS primers is also included in the
reaction. The 5’ GC clamp, between 10 and 15bp in length, is incorporated into the
amplicon during PCR thermocycling and effectively increases the Tm of the primer
by approximately 4oC. The difference in Tm between the amplicons, which can be
exploited using melt curve analysis, allows both AS reactions to be performed in the
same reaction vessel (Figure 9). Additional mismatches are introduced into the AS
primers to further destabilise the 3’ mismatched primer from the target DNA [161].
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Figure 9. Effect of 5’-GC clamp on melt temperature profiles in multiplexed allele-specific (AS)
real-time PCR. The first peak at 79oC indicates amplicons generated from the unmodified AS primer;
the second peak at 85oC indicates the PCR products amplified by the GC-clamped AS primer. The dsDNA-
binding SYBR® Green I chemistry was used to detect amplicons. Figure adapted from reference 140.
The 5’ GC clamp assay combines the flexibility, robustness and cost-effectiveness of
dsDNA-binding chemistry with the multiplexed nature of fluorogenic primers and
probes. Furthermore, the assay can be adapted to almost any sequence and the 5’
GC clamp does not affect annealing of the primer to the target DNA [140].
Nevertheless, a major consideration of Tm shift primers is the need to extensively
optimise the multiplex reaction in order to reduce the influence of competitive
primer binding. If more than one AS reaction is carried out for a SNP within the
same tube, competitive priming provides more opportunities for mispriming by the
mismatched primer, reducing reaction specificity [141]. The effects of this
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phenomenon can be substantially reduced by deliberate incorporation of further
mismatches into the AS primers. An alternative strategy that also avoids mispriming
issues is to interrogate different SNPs in the same reaction vessel; however, precise
assay design would be required to ensure that the additional common primer does
not interfere with amplification efficiency.
2.7 Emerging genotyping technologies
2.7.1 High-resolution melt (HRM) analysis
HRM is an inexpensive, simple and high-throughput methodology that has a wide
spectrum of real-time PCR applications. HRM is based on the principle that a given
DNA sequence has distinct and reproducible dissociation kinetics upon melting, and
involves the precise monitoring of a change in fluorescence when a dsDNA-binding
dye is released from an amplicon as it is denatured by increasing temperature
[154]. Nucleic acids that differ in length, absolute sequence, G+C content and
strand complementarity can be discriminated by their differing thermal
characteristics [134]. HRM requires sophisticated instrumentation that is capable of
extreme thermal resolution, minimal well-to-well variation and high-speed data
capture. As HRM is an emerging technology, only the Rotor-Gene™ 6000 (Corbett
Life Science), HR1™ and 384-well LightScanner™ (both from Idaho Technology)
instruments are currently capable of performing HRM. The Idaho instruments only
perform HRM, whereas the Rotor-Gene™ 6000 has both kinetic PCR and HRM
capabilities [156].
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Formerly, amplicon characterisation using HRM analysis was limited by technical
constraints in sensitive temperature control, available chemistries and data
acquisition and analysis. As an example of the difference between HRM and non-
HRM instruments, the Applied Biosystems 7300 sequence detection system
dissociates amplicons at 0.5oC increments, whereas the Corbett Rotor-Gene™ 6000
can melt at 0.02oC and the Idaho HR1™ at 0.01oC increments [156]. As mentioned
in section 6.3, SYBR® Green I cannot be used at saturating concentrations and is
therefore considered an unsuitable dye for HRM. New-generation dsDNA
intercalating dyes such as SYTO® 9, EvaGreen™ [149], LC Green® [150] and LC
Green® PLUS+ [162] have arisen as candidate dyes for performing HRM as they can
be used at higher concentrations than SYBR® Green I, allowing saturation of the
dsDNA duplex [132, 154]. The sensitivity of HRM and the new-generation dsDNA-
binding dyes allows the discrimination of as little as a single base difference
between amplicons [154] (Figure 10).
One downside of the HRM method is the requirement for stringent protocol
adherence. The shape and Tm of an amplicon can be affected by several
parameters, including MgCl2, buffer, template DNA, primer and dye concentrations,
as well as template DNA quality [156]. Once standardised, however, HRM provides
a highly specific, inexpensive and reproducible methodology [154] that will likely be
increasingly used as an alternative to more expensive labelled probe or primer
chemistries, or potentially even DNA sequencing.
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Figure 10. Example of a high-resolution melt (HRM) curve for SNP genotyping. Using HRM, it is
possible to detect nucleotide difference/s between polymorphisms at a SNP based on their distinct
melting temperature characteristics. Figure adapted from reference 156.
2.7.2 Lab-on-a-chip (LOaC) devices
The desire to rapidly and cost-effectively detect and characterise nucleic acids in the
field environment has driven the development of ‘lab-on-a-chip’ (LOaC) devices.
LOaC apparatus are the fastest growing component of the nanotechnology industry
and are directed at medical or environmental point-of-care diagnostics as an
alternative to time-consuming laboratory testing [163]. LOaCs combine
miniaturisation and microfluidics to analyse minute volumes of biological samples
(nucleic acids or proteins) in a closed, single-step system. Ultimately LOaC devices
will enable automated sample preparation, fluid handling, and analysis and
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detection steps to be performed on a single chip by incorporating mechanical,
electronic, fluidic and optical components [164]. For example, protein (or proteome)
LOaCs are being developed that create protein ‘snapshots’ of an entire cell, allowing
rapid screening for drug candidates, for assessing nutrition requirements, or for
differentiating normal cellular processes from diseased states [165, 166].
There are many different types of LOaC devices described in the literature. DNA
LOaCs include microfabricated electrophoresis devices that can be readily applied to
electrophoresis-based methods, such as RFLP analysis, DNA sequencing and AS PCR
detection, as a cost-effective and rapid replacement for capillary electrophoresis
[167, 168]. Other DNA LOaCs are designed to perform DNA amplification and
detection on the same chip, although sample preparation and analysis are
potentially also achievable. There are several advantages associated with
miniaturised systems including the ability to perform at high-throughput capacities,
the potential for portability, the use of minute volumes, and extreme assay rapidity
[168]. A fundamental component of a rapid LOaC apparatus designed for nucleic
acid amplification is efficient heat transfer. Most LOaC devices have been
constructed from silicon, glass or plastic. Silicon LOaC devices offer the best
thermal efficiency, whilst glass and plastic chips are less expensive [164].
One single-use prototype LOaC device, the In-Check Lab-on-a-chip, contains four
PCR chambers that are each composed of three buried channels. Each reaction
chamber can hold a maximum of 2µL. DNA amplification is performed in microscopic
channels that are buried within a silicon chip by mixing the template DNA under
examination with the appropriate reagents. PCR thermocycling is controlled by a
graphical user interface that allows the user to define reaction conditions and
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monitor the PCR in real-time. Following amplification, PCR products are analysed
using capillary electrophoresis [164]. Next-generation In-Check LOaC chips are
being designed which incorporate both amplification and detection, in which the
sample flows into the detection region of the same chip, the amplicon/s hybridise
with gold pre-loaded DNA fragments, and hybridisation of the amplified sample with
the pre-loaded fragments is subsequently detected optically [164].
Another recently developed LOaC device describes on-chip amplification of the
ubiquitious cadF gene from C. jejuni using real-time PCR [169]. The chip integrates
a thermal system (heater and thermometer) with optical detection, designed to
allow differentiation between two distinct wavelengths. Two previously unreported
dsDNA binding dyes, SYTOX Orange and TO-PRO-3, were used to measure kinetic
amplification of cadF gene on the chip. As a control, the same assays were
performed on a conventional real-time PCR instrument. Interestingly, the chip was
capable of performing melt analysis over a 35-95oC temperature gradient, and the
Tm was shown to be identical to that obtained on the real-time PCR machine. The
rapidity of thermocycling on the chip enabled the total PCR time to be reduced from
90 to 40 minutes. Some notable disadvantages of the C. jejuni cadF chip include the
lower PCR efficiency (approximately 10% lower than conventional real-time PCR),
surface-induced inhibition due to interaction of PCR reagents with the chip surface,
and high background noise [169]. However, these problems are likely to be
overcome as newer-generation LOaC devices are manufactured.
Proteome and DNA LOaC devices allow extremely rapid analysis of sub-microlitre
sample volumes, resulting in low reagent consumption and negligible waste, and as
such offer an attractive avenue for future pathogen detection and genotyping.
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Currently, external hardware is required to transfer and analyse samples [164],
limiting the portability of the LOaC devices. The major challenge in reducing LOaC
technology to routine practice lies in the need to make the technology cost-
effective, workable and user-friendly [132].
2.8 Hepatitis C virus
Chapter Six (Manuscript Four) of this thesis describes the identification of novel
SNPs for Hepatitis C virus (HCV) genotyping. It is therefore relevant to include
background on HCV as well as HCV genotyping methodologies in current use to put
into perspective the findings from Chapter Six.
2.8.1 Introduction
HCV is a positive-sense, single-stranded RNA virus approximately 9.6 kb in length
and is a member of the Hepacivirus genus and the Flaviviridae family [170, 171].
The HCV genome encodes seven structural and non-structural genes, and also
contains 5’- and 3’-non-translated regions (NTRs) (Figure 11).
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Figure 11. Schematic of the HCV genome. C, core; E, envelope; p7, polypeptide 7; NS, non-
structural; NTR, non-translated region; RdRp, RNA dependent RNA polymerase. Adapted from reference
172.
HCV is associated with chronic liver infection, including cirrhosis and hepatocellular
carcinoma, and is estimated to affect over 170 million people worldwide [173].
There are six broad HCV genotypes (1-6) defined by phylogenomic analysis, which
have been further divided into thirteen currently recognised subtypes; 1a, 1b, 2a,
2b, 2c, 3a, 3b, 4a, 4d, 4f, 4t, 5a and 6a [171]. Genotypes 1b and 1a are currently
the commonest found worldwide and comprise three-quarters of all diagnosed HCV
infections, whereas other genotypes such as 5a, 4a and 3b have restricted
geographical distributions [171, 174] (Figure 12).
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Figure 12. Phylogeny and global distribution of hepatitis C virus genotypes. Adapted from
references 171 and 174.
In addition to identifying geographic trends of HCV genotypes, HCV typing has
played an important role in monitoring HCV in chronically infected patients as it
assists in determining prognosis and therapy duration. Genotyping of HCV assists in
determining the treatment delivered to a patient, due to known genotype-specific
Chapter 2: Literature Review
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differences in response to interferon-α-ribavirin treatment [170]. Specifically,
genotypes 1 and 4 are more resistant than genotypes 2 and 3 to interferon-α-based
therapy [171].
2.8.2 Currently adopted HCV genotyping methodologies
Viruses have traditionally been characterised by antigenic characteristics
(serotyping), but similarly to bacteria there has been a shift towards genetic
classification using simpler PCR-based methodologies. Many of the existing HCV
genotyping methods focus on the 5’-NTR due to the relatively conserved nature of
this region [175]. The COBAS Amplicor™ HCV Monitor Test v2.0 (Roche
Diagnostics) is used by many diagnostic laboratories to reverse-transcribe HCV RNA
into cDNA, which allows downstream genotyping applications [176]. One of these
methods, the widely used commercial line probe assay (INNO-LiPA HCV II), is based
on genotype-specific probes from the 5’-NTR that are embedded onto a
nitrocellulose strip [175, 177]. The 5’-NTR amplified by using biotinylated primers
from patient sera is placed onto the strip and allowed to hybridise. Complementary
sequences hybridise to the strip and are colourimetrically detected using
streptavidin, enabling the genotype to be determined. As the INNO-LiPA test is
based on SNP detection by hybridisation, the assay temperature is crucial for
correct genotype identification. The LiPA assay costs approximately US$72 per test,
excluding RNA extraction costs [175].
Another commercial test, the TRUGENE HCV 5’-NC Genotyping kit (Bayer
Diagnostics), is a sequence-based methodology targeting the 5’-NTR. Following
reverse transcription by the COBAS Amplicor™ test, the TRUGENE system employs
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standardised PCR amplification and a proprietary sequencing system, called CLIP
sequencing, of a 244 bp fragment of the 5’-NTR [178]. Substantial capital
investment is required to obtain the specialised TRUGENE equipment, after which
the assay costs approximately US$100 per test [175].
Two real-time PCR methods for HCV have recently emerged; the Abbott HCV RNA
analyte-specific reagent (ASR) test [179] and the COBAS TaqMan®48 HCV test
[180]. The benefits of real-time PCR assays over conventional PCR assays for HCV
genotyping lie in their potential sensitivity, broad linear range of detection, turn-
around-time and decreased labour-intensity [180, 181]. Both tests target the 5’-
NTR for all genotypes with the exception of the Abbott HCV RNA ASR test, in which
1a and 1b are differentiated using SNPs within the NS5B region. Reverse
transcription, PCR amplification and SNP detection are carried out in a single step,
with SNPs interrogated using TaqMan® probes [180, 181]. Other genotyping
methods include RFLP of the 5’NTR, core and NS5 regions [182-184], an Invader®
(SNP interrogation) assay of the 5’-NTR [185], fluorescence-based primer-specific
extension analysis within the 5’-NTR [170] and PCR with genotype-specific primers
targeting the core or NS5 regions [186, 187].
A drawback of targeting the 5’-NTR is that some subtypes, such as 1a and 1b, 1b
and 6a, or 2a and 2c, remain indistinguishable in a small number of cases due to
the high level of conservation of this region [178, 188, 189]. Inherent with all 5’-
NTR tests, there is incomplete correlation of genotype/subtype compared with other
genes, in particular the NS5B gene, which is used to construct HCV phylogenies
[175]. One study showed that the line probe assay gave conflicting results
compared with NS5B; 1.4% of 148 samples at the genotype level were discordant,
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increasing to 14% at the subtype level [190]. The same study showed that the
TRUGENE HCV 5’-NC assay was discordant with NS5B in 2% of samples at the
genotype level, and 8% at the subtype level. Possible reasons for the discrepancies
in genotypes and subtypes include the effect of additional SNPs in the 5’-NTR that
may influence the sensitivity and outcome of the line probe assay. Owing to the
tight secondary and potentially tertiary and quaternary structure of the 5’-NTR,
SNPs may affect stability of the structure and hence the assay sensitivity and
efficiency [174].
Without doubt the most accurate method of HCV characterisation is genome
sequencing. There exist three publicly available HCV databases hosted in France,
Japan, and the United States, with the latter databases primarily containing genome
sequence data [173, 191]. There are currently 188 HCV genomes that have been
completely sequenced and many more partial sequences including the 5’-NTR, core,
E1 and NS5B.
To conclude, there are a suite of HCV genotyping methods that have been
developed and rigorously tested, with genome sequencing the ideal methodology
for unambiguous strain characterisation. However, all the HCV methods described in
this review are costly, laborious, time-consuming, have inadequate specificity, or
possess a combination of these factors. There is therefore a need to refine existing
or develop new HCV diagnostics to address the limitations described above, and the
now-abundant comparative genome data available to researchers will allow
improvement of prior methodologies. Genotyping methods for HCV need to account
for the existence of quasispecies; that is, a population of closely related genomes
found within a single patient that have arisen due to the high heterogeneity of HCV
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[175]. In addition, it has been documented that individual patients can exhibit co-
infections with multiple HCV strains, with one study indicating the occurrence of
mixed infections in 5% of cases [170], as well as marked differences in patient viral
loads [181]. Whilst these issues are beyond the bounds of the current project, they
are important considerations when developing and testing any genotyping method
for HCV.
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Chapter 3. Campylobacter jejuni genotyping using SNPs
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CHAPTER THREE
Genotyping of Campylobacter jejuni using Seven Single-Nucleotide Polymorphisms
in combination with flaA Short Variable Region Sequencing
Erin P. Price1, Venugopal Thiruvenkataswamy1, Lance Mickan2, Leanne Unicomb3,4, Rosa E.
Rios5, Flavia Huygens1 and Philip M. Giffard1.
1. Cooperative Research Centre for Diagnostics
Queensland University of Technology
Brisbane, Australia
2. Institute of Medical and Veterinary Science
Adelaide, Australia
3. OzFoodNet, Hunter New England Population Health
Wallsend, Australia
4. National Centre for Epidemiology and Population Health
Australian National University
Canberra, Australia
5. Microbiological Diagnostic Unit
University of Melbourne
Melbourne, Australia
J Med Microbiol. (2006) 55: 1061-1070.
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Chapter 4. Identifying binary gene targets in Campylobacter jejuni
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CHAPTER FOUR
Fingerprinting of Campylobacter jejuni by using Resolution-Optimized Binary Gene
Targets derived from Comparative Genome Hybridization Studies
Erin P. Price1, Flavia Huygens1 and P. M. Giffard1.
1. Cooperative Research Centre for Diagnostics,
Institute of Health and Biomedical Innovation,
Queensland University of Technology
Brisbane QLD 4059 Australia
Appl Environ Microbiol. (2006) 72: 7793-7803.
Chapter 4. Identifying binary gene targets in Campylobacter jejuni
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Chapter 5: High-resolution melt analysis of C. jejuni CRISPRs
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CHAPTER FIVE
High-resolution DNA melt curve analysis of the clustered, regularly interspaced
short-palindromic-repeat locus of Campylobacter jejuni
Erin P. Price1, Helen Smith2, Flavia Huygens1 and P. M. Giffard1.
1. Cooperative Research Centre for Diagnostics,
Institute of Health and Biomedical Innovation
Queensland University of Technology
Brisbane QLD 4059 Australia
2. Queensland Health Scientific Services
Coopers Plains QLD 4108 Australia
Appl Environ Microbiol. (2007) 73: 3431-3436.
Chapter 5: High-resolution melt analysis of C. jejuni CRISPRs
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Chapter 6: Marker sets diagnostic for groups of genetic variants
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CHAPTER SIX
Computer-aided identification of polymorphism sets diagnostic for groups of
bacterial and viral genetic variants
Erin P. Price1, John Inman-Bamber1, Venugopal Thiruvenkataswamy1, Flavia Huygens1 and
Philip M. Giffard1.
1. Cooperative Research Centre for Diagnostics,
Institute of Health and Biomedical Innovation
Queensland University of Technology
Brisbane QLD 4059 Australia
BMC Bioinformatics (2007) 8:278.
Chapter 6: Marker sets diagnostic for groups of genetic variants
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STATEMENT OF JOINT AUTHORSHIP
The authors listed below have certified that:
1. They meet the criteria for authorship in that they have participated in the conception, execution,
or interpretation, or at least that part of the publication in their field of expertise;
2. they take public responsibility for their part of the publication, except for the responsible author
who accepts overall responsibility for the publication;
3. there are no other authors of the publication according to these criteria;
4. potential conflicts of interest have been disclosed to (a) granting bodies, and (b) the editor or
publisher of BMC Bioinformatics, and;
5. they agree to the use of the publication in the student’s thesis and its publication on the
Australasian Digital Thesis database consistent with any limitations set by publisher
requirements.
Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and
viral genetic variants. BMC Bioinformatics (2007) 8:278.
Contributor Statement of Contribution
Erin P. Price
(candidate)
Wrote the manuscript; contributed to experimental design and research plan
formulation; executed most experiments
Signature: Date:
Venogupal
Thiruvenkataswamy
Programmed “Minimum SNPs” to incorporate the Not-N function; critically revised
manuscript and approved final version of manuscript
John Inman-
Bamber
Contributed to Not-N data acquisition and analysis for Staphylococcus aureus;
critically revised manuscript and approved final version of manuscript
Flavia Huygens
Contributed to conception of research plan and provided feedback on experimental
design and executions; critically revised manuscript and approved final version of
manuscript
Chapter 6: Marker sets diagnostic for groups of genetic variants
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Philip M. Giffard
Formulated research plan; critically reviewed the manuscript and proofs and approved
final version of manuscript; assisted in the writing of the manuscript; contributed
continual feedback on experimental design and execution
Principal supervisor confirmation
I have sighted email or other correspondence from all co-authors confirming their certifying
authorship.
Name Signature Date
Chapter 6: Marker sets diagnostic for groups of genetic variants
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Chapter 6: Marker sets diagnostic for groups of genetic variants
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Chapter 6: Marker sets diagnostic for groups of genetic variants
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Chapter 6: Marker sets diagnostic for groups of genetic variants
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Chapter 6: Marker sets diagnostic for groups of genetic variants
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Chapter 6: Marker sets diagnostic for groups of genetic variants
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Chapter 6: Marker sets diagnostic for groups of genetic variants
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Chapter 6: Marker sets diagnostic for groups of genetic variants
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Supplementary data
Supplementary Table 1. Single-nucleotide polymorphisms extracted from multilocus
sequence typing data using the Not-N module of “Minimum SNPs”.
CC No. STs
SNP 1 (%)
SNP 2 (%)
SNP 3 (%)
SNP 4 (%)
SNP 5 (%)
SNP 6 (%)
SNP 7 (%) SNP 8 (%)
No. pathways
a E. coli, MLST scheme 1
1 105 lysP198 C
(71.2) fadD195 A (82.1)
uidA518 G (93.1)
aspC333 A (98.6)
aspC131 G (100) --- --- --- 0
2 21 fadD45 G
(100) --- --- --- --- --- --- --- 26
3 10 aspA240 C (100) --- --- --- --- --- --- --- 2
4 9 clpX495 A
(100) --- --- --- --- --- --- --- 2
5 8 uidA518 A
(100) --- --- --- --- --- --- --- 0
6 6 icdA126 T
(98.2) icdA336* G (100) --- --- --- --- --- --- 13
7 6 aspA57 A
(100) --- --- --- --- --- --- --- 3
8 5 mdh291 T
(75.7) mdh459 G (99.4)
icdA225* T (100) --- --- --- --- --- 22
9 4 icdA336* A (100) --- --- --- --- --- --- --- 1
10 4 icdA225* G (100) --- --- --- --- --- --- --- 8
E. coli, MLST scheme 2
1 270 fumC416 C (52.8)
icd265 T (74.6)
recA163 C (84.7)
fumC257 G (90.5)
icd146 G (94.2)
fumC123 G (96.3)
fumC65 G (97.8)
fumC296 A (98.5) 0
2 36 gyrB180 T
(92.7) purA83 G
(96.2) adk203 A
(98.6) adk118* A (99.4)
fumC107 C
(100) --- --- --- 1
3 23 mdh348 C
(98.7) adk328 C
(99.7) adk148 C
(100) --- --- --- --- --- 2
4 13 adk203 T
(100) --- --- --- --- --- --- --- 0
5 11 icd331 A
(100) --- --- --- --- --- --- --- 0
6 11 recA136 C
(96.9) mdh85 T
(100) --- --- --- --- --- --- 4
7 9
recA100 G/T
(94.9) gyrB372 T (100) --- --- --- --- --- --- 0
8 9 fumC107*
T (100) --- --- --- --- --- --- --- 0
9 7 icd283 T (99.5)
adk331 C (100) --- --- --- --- --- --- 2
10 7 mdh92 A
(100) --- --- --- --- --- --- --- 0
11 6 fumC123 T (100) --- --- --- --- --- --- --- 0
12 6 adk118* G (100) --- --- --- --- --- --- --- 0
H. influenzae MLST, clonal complexes only (8 SNPs) ST-6 and ST-53 71
atpG150* C (77.0)
frdB105* C (95.8)
atpG90 C (100) --- --- --- --- --- 1
ST-53
only 13 frdB360 A
(92.4) fucK330 G (100) --- --- --- --- --- --- 2
Chapter 6: Marker sets diagnostic for groups of genetic variants
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ST-222 15
adk129* T (100) --- --- --- --- --- --- --- 0
ST-3 9 frdB105* T (99.0)
atpG150* C (100) --- --- --- --- --- --- 14
ST-18 9
atpG119 C (100) --- --- --- --- --- --- --- 6
ST-209 8
atpG150* A (87.3)
adk129* C (100) --- --- --- --- --- --- 49
ST-124 7
adk34 T (100) --- --- --- --- --- --- --- 6
S. aureus MLST ST-5 122 n/a n/a n/a n/a n/a n/a n/a n/a n/a ST-8 108 n/a n/a n/a n/a n/a n/a n/a n/a n/a ST-30 92 n/a n/a n/a n/a n/a n/a n/a n/a n/a ST-45 46
pta312 A (99.4)
yqiL303* A (99.8)
glpF66 C (100) --- --- --- --- --- 23
ST-1 46 n/a n/a n/a n/a n/a n/a n/a n/a n/a ST-97 35
aroE212 A (87.4)
yqiL303* A (97.6)
glpF276* G (99.2)
arcC78 G (99.6)
pta85* G (99.8) --- --- --- 0
ST-15 35
arcC199 A (59.5)
yqiL333 T (85.6)
pta85* G (97.4)
pta294 A (98.9)
aroE238 G (99.0)
gmk16 C (99.2)
yqiL513 G (99.4) --- 0
ST-121 27
arcC184 A (95.5)
aroE102 T (99.1)
pta85* A (100) --- --- --- --- --- 8
ST-22 21
yqiL168 A (95.9)
yqiL88 G (99.8)
pta85* A (100) --- --- --- --- --- 39
ST-133 18
aroE79 G (100) --- --- --- --- --- --- --- 4
ST-78 14
glpF231 C (100) --- --- --- --- --- --- --- 1
ST-59 13
pta177 G (100) --- --- --- --- --- --- --- 1
ST25 9
glpF276* A (100) --- --- --- --- --- --- --- 1
C. jejuni MLST ST-21 422 n/a n/a n/a n/a n/a n/a n/a n/a n/a
ST-825 400
glyA42 A/C
(68.1) glyA3* T (92.7)
glnA45 G/T
(97.4) glnA108* G (98.2)
tkt189* A/G/C (98.7)
glnA240 A (98.9)
tkt28 T (99.1)
glnA132* A (99.2) 0
ST-45 137 n/a n/a n/a n/a n/a n/a n/a n/a n/a ST-257 67 n/a n/a n/a n/a n/a n/a n/a n/a n/a ST-353 57 n/a n/a n/a n/a n/a n/a n/a n/a n/a ST-177 50
gltA180 C (99.9)
tkt189* T (100) --- --- --- --- --- --- 18
ST-42 33 n/a n/a n/a n/a n/a n/a n/a n/a n/a ST-403 31
tkt234 A (90.1)
aspA9 C (99.3)
aspA342 C (100) --- --- --- --- --- 1
ST-51 30
uncA165 C (99.1)
glyA264 T (99.7)
glnA108* G (99.9)
glnA288 C (100) --- --- --- --- 67
ST-354 28
aspA414 C (64.3)
aspA84 G (90.6)
tkt189* A/C
(94.9) glyA3* T (96.6)
pgm34 C (97.9)
pgm405* T (98.6)
uncA375* C
(99.1) --- 0
ST-52 26
glyA504 T (79.5)
glyA3* T (96.7)
uncA375* C
(98.3)
uncA189* C
(98.9) glnA12
G (99.1) --- --- --- 0 ST-574 26 n/a n/a n/a n/a n/a n/a n/a n/a n/a
ST-22 24
glyA114 C (87.9)
gltA294 C (98.2)
glnA132* A (99.0)
uncA189* C
(99.6)
pgm435 T
(99.9) pgm405* T (100) --- --- 3
ST-460 23
glnA18 C (83.9)
tkt132 T (99.8)
tkt189* C (100) --- --- --- --- --- 65
Chapter 6: Marker sets diagnostic for groups of genetic variants
- 158 -
A total of 15 SNPs required to differentiate the 10 main CCs of E. coli (scheme 1); 24 SNPs required to differentiate the
12 main CCs of E. coli (scheme 2); 30 SNPs required to differentiate 9 of the 13 main CCs of S. aureus; and 27 SNPs
required to differentiate 5 of the 13 major CCs of C. jejuni.
a Corresponds to the number of alternate outputs provided by Not-N that are not shown in the table.
*SNP discriminates multiple CCs
Fields marked ‘n/a’ failed to yield a confidence of ≥98% after eight SNPs.
Chapter 7: General Discussion
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CHAPTER SEVEN
GENERAL DISCUSSION
Chapter 7: General Discussion
- 160 -
7.1 Discussion
In this body of work I have described the development of three innovative real-time
PCR-based methods for the rapid, cost-effective and high-resolution fingerprinting
of the common foodborne pathogens, Campylobacter jejuni and Campylobacter coli.
Highly informative genotypic markers were identified from large DNA sequence and
comparative genome hybridisation (CGH) databases of C. jejuni and C. coli using
the in-house computer software package “Minimum SNPs”. A combinatorial
approach targeting differentially evolving genetic loci on the real-time PCR platform
was employed, enabling maximal resolving power to be achieved on a single
instrument. The first method involved the identification and interrogation of seven
highly informative SNPs derived from slowly-evolving housekeeping loci; the second
method focussed on examining the presence or absence of dispensable genes found
predominantly within plasticity regions (PRs) of the C. jejuni genome; and the third
method analysed the rapidly evolving clustered regularly interspaced short
palindromic repeat (CRISPR) locus of C. jejuni. In addition to these novel real-time
PCR-based genotyping strategies developed for C. jejuni and C. coli, highly
informative genotypic markers for other clinically important infectious agents were
identified using a new module of “Minimum SNPs” that was developed as part of
this project.
Differentiation between variants within a species has far-reaching applications, such
as for epidemiological surveillance and source tracing, for identifying hyperinvasive
or hypervirulent clones from their non-invasive counterparts, for point-of-care
diagnosis and in biodefense. C. jejuni and C. coli characterisation in particular is of
Chapter 7: General Discussion
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considerable interest from the perspective of the food processing, disease control
and clinical diagnostic industries [1, 2]. Most probably because of the intense
industry interest in these pathogens, a vast number of methods have been
developed for fingerprinting C. jejuni and C. coli isolates to suit a variety of
purposes, ranging from simple detection and speciation of Campylobacter spp.
through to high-resolution characterisation for the purposes of intensive
epidemiological studies [1].
In recent years, phenotypic characterisation of C. jejuni and C. coli (such as
serotyping, phage typing or hippuricase detection) has been largely superseded by
genotypic methods, due to the reliability, reproducibility, resolution and cost-
effectiveness of genotyping. Genotyping methods currently used for C. jejuni and C.
coli fingerprinting include multilocus sequence typing (MLST) [3], flagellin A short
variable region (flaA SVR) sequencing [4], pulsed-field gel electrophoresis (PFGE)
[5], and the emerging CGH arrays [6-14]. MLST involves the DNA sequence
characterisation of seven housekeeping loci that are widely distributed throughout
the bacterial genome [15]. Sequence variants, termed sequence types (STs), can
be placed within the C. jejuni and C. coli population structure when they share four
or more alleles with the founder clone of a clonal complex (CC) [3]. MLST is an
excellent method for investigations that examine isolates over large time scales;
however, the reasonably low resolving power of MLST makes this method
inappropriate for detailed epidemiological studies or for outbreak investigations,
where a high degree of resolution is required. In these circumstances, flaA SVR
sequencing has commonly been used in conjunction with MLST to obtain more
highly discriminatory strain fingerprints than either method can provide alone [16-
18].
Chapter 7: General Discussion
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flaA encodes the A subunit of the Campylobacter flagellum and therefore is under
selective pressure to evade the host immune system [16]. The combination of
MLST-flaA SVR thus provides an indication of both the position of an isolate within
its population structure as well as more recent evidence of genetic exchange,
allowing the differentiation of epidemiologically unrelated isolates that may be
indistinguishable by MLST. Although the value of MLST and flaA SVR sequencing as
epidemiological tools for C. jejuni and C. coli characterisation is unmistakable, these
methods are reliant on DNA sequencing and as a consequence are expensive and
cumbersome [19], particularly for smaller laboratories that lack access to their own
sequencing apparatus. In the case of MLST, fourteen corrected sequences must be
obtained before the ST of an isolate can be assigned, making it expensive and
labour-intensive to genotype large numbers of isolates. Therefore, MLST and flaA
SVR sequencing are undesirable techniques for routine microbial surveillance.
The ‘gold standard’ PFGE procedure is so named due to the high resolution obtained
when using this technique for strain characterisation, and in particular PFGE is the
method of choice for outbreak investigations of most bacteria. The value of PFGE for
routine characterisation of campylobacteriosis, however, is questionable as this
technique is sensitive to subtle genetic changes, potentially obscuring existing
strain relationships and hampering epidemiological studies of C. jejuni and C. coli
populations over time [20, 21]. Unlike MLST and flaA SVR, PFGE relies on the
comparison of electrophoresis banding patterns and therefore the isolate profiles
are subject to potential ambiguities in interpretation, particularly when comparing
results between laboratories [16]. For these reasons, PFGE will undoubtedly become
superseded by newer-generation genotyping methods that not only provide
comparably high discriminatory power but that also enable the investigator to
Chapter 7: General Discussion
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accurately place a given bacterial isolate within the appropriate population
framework.
CGH DNA microarrays have gained popularity in recent years as they facilitate
whole-genome comparison of large numbers of bacterial isolates in a reasonably
short period of time and at a substantially lowered cost when compared with
genome sequencing. Since the first CGH study of C. jejuni was described in 2001,
several C. jejuni CGH studies have emerged in the published literature and the
genomes of over 300 strains have been directly compared by this technique [6-14].
Early CGH studies in C. jejuni identified genetically diverse regions scattered within
the otherwise mostly syntenic C. jejuni genomes, called PRs, where a significant
bulk of apparently dispensable ‘binary’ genes (genes that are present in some
strains but absent in others) reside [7]. The presence or absence of these binary
genes can be used to directly compare the relationships between strains on a
whole-genome level. In the context of a routine microbial genotyping strategy, CGH
is currently not practical due to its extremely high cost, labour-intensity and
complex data analysis. It is foreseeable that CGH may become a commonly used
microbial genotyping strategy; however, significant advances in microarray
technology and data analysis would need to be made before the widespread use of
this method is adopted.
It was the main hypothesis of this project that large comparative bacterial genetic
approaches, such as MLST and CGH, provide a reservoir of genetic information that
can be exploited to identify small numbers of highly informative genotyping markers
that form part of rationally designed genotyping assays. In keeping with this
hypothesis, Chapter Three of this thesis describes the development of a single-
Chapter 7: General Discussion
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nucleotide polymorphism (SNP)-based assay for genotyping C. jejuni and C. coli.
SNPs were identified from MLST data using the “Minimum SNPs” software, which
incorporates the Simpson’s Index of Diversity (D) algorithm [22, 23]. High-D SNPs
were selected by “Minimum SNPs” based on their ability to maximally discriminate
959 C. jejuni and C. coli ST variants. The primary aim of the SNP assay was to
obviate the labour-intensity and cost of MLST whilst maintaining a comparable
degree of strain resolution. Seven SNPs, providing a D of 0.98 compared with full
MLST, were extracted from the C. jejuni and C. coli MLST data using “Minimum
SNPs”. An allele-specific real-time PCR method was successfully developed and used
to interrogate these seven SNPs in a well-characterised collection of 154 Australian
C. jejuni and C. coli sporadic gastroenteric isolates.
It was found that the seven-member SNP profiles showed an incomplete correlation
to MLST CCs, with non-related STs (STs from different CCs) on occasion sharing
identical SNP profiles. Several studies have confirmed that C. jejuni undergoes
high-frequency recombination events, even in housekeeping genes such as those
targeted by MLST, resulting in mosaic patterns within genetic loci [3, 17, 24-27].
This high-frequency recombination results in a weakly clonal population structure in
which successor clones derived from a common ancestor are more likely to arise by
recombination than mutation [3, 26]. Therefore, frequent homologous
recombination in C. jejuni and C. coli MLST loci appears the most feasible
explanation as to why the seven-member SNP profiles did not completely correlate
with the MLST CC structure.
Despite the incongruence of certain SNP profiles with MLST CCs, an interesting and
significant outcome from this study was that addition of the hypervariable flaA SVR
Chapter 7: General Discussion
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locus to the seven-member SNP profiles resolved, in almost all cases, non-related
STs from one another. This finding strongly suggests that genotyping methods are
most useful for the purposes of high-resolution epidemiological analyses when used
in combination with other methods that target differentially evolving genetic loci.
The concept of combinatorial typing using differentially evolving genetic markers
has been described by Keim and co-workers [28] under the acronym PHRANA
(progressive hierarchal resolving assays using nucleic acids). PHRANA is a nested
hierarchal strategy that involves the progressive interrogation of stable genetic
markers that have low resolution in concert with increasingly unstable markers that
possess higher discriminatory power. The combination of slowly- and rapidly-
evolving genetic markers allows the phylogenetic position of a given isolate within
the bacterial population to be determined whilst providing high-resolution
discrimination between closely related isolates [28].
Although it was a powerful finding that the addition of flaA SVR genotypes to the
SNP profiles provided genotypes comparable in resolving power to MLST-flaA SVR,
sequencing of the flaA SVR locus is not ideal for similar reasons to MLST. It was of
interest to develop an inexpensive and rapid genotyping strategy to replace flaA
SVR that, similarly to the SNP typing procedure, was based on real-time PCR. In a
similar fashion to the identification of highly informative SNPs from MLST data, I set
out to exploit the CGH datasets available for C. jejuni in order to identify a small set
of binary genes that, in concert with the MLST-derived SNPs, could be used as
highly informative genotyping markers to increase the resolving power of the
seven-member SNP assay. Unlike the housekeeping gene-derived SNPs, which
evolve over reasonably slow periods of time, the binary genes were hypothesised to
undergo more rapid rates of evolution due to their apparent dispensable nature.
Chapter 7: General Discussion
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Therefore, the aim of the work described in Chapter Four was to identify a small
number of highly informative binary genes that could be used in combination with
the seven-member SNP set as a replacement to flaA SVR sequencing.
The D algorithm of “Minimum SNPs” was once again used to identify eight binary
gene markers – Cj0629, Cj0265c, Cj0178, Cj0299, Cj1319, Cj1723c, Cj0008 and
Cj0486 - that completely resolved (D=1) 19 C. jejuni strains from two CGH studies.
To enable analysis by “Minimum SNPs” the CGH data of the ~1,600 genes for the
19 strains were converted into a pseudo-DNA sequence alignment, with gene
presence denoted as a ‘T’ and gene absence an ‘A’. A large cohort of sporadic
gastroenteric Australian C. jejuni and C. coli isolates (n=181) were tested for the
presence or absence of the eight binary genes using the real-time PCR platform.
Seven of the eight genes could be clearly defined as present or absent in the
Australian isolates, whereas Cj0629 contained a third intermediary state, which was
conferred by sequence mismatches between the primer and template. The stability
of the intermediary state of Cj0629 was demonstrated by testing the status of this
gene prior to and after repeated subculturing of the relevant strains (results not
published), indicating high reproducibility of the binary typing method. This was the
first study to successfully identify highly informative binary genes from CGH data,
facilitated by computational analysis of the pseudo-sequence alignment. Clearly,
such an approach is not limited to C. jejuni and could be readily applied to any
organism for which comparative sequence datasets are available.
During the course of this study, a much larger CGH study using 111 C. jejuni strains
emerged in the published literature [12]. Therefore, the performance of the original
eight binary genes was compared with new targets derived from the larger dataset
Chapter 7: General Discussion
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using “Minimum SNPs”. It was found that the original targets performed reasonably
well but not as optimally as the newer binary gene targets, although there was
some overlap in the targets chosen, suggesting that the original binary gene set
was adequate for strain discrimination. Additionally, as the binary gene assay had
already been developed and tested it was not practical to incorporate the new
targets into the existing assay. This finding underscores the dynamic nature of
comparative genetic data and the necessity to periodically update the chosen
markers as more data is made available. Future studies of C. jejuni using the binary
gene approach should accommodate the increasing volumes of CGH data to obtain
optimal resolution. To date, predominantly human gastroenteric isolates have been
assessed by CGH. As a greater diversity of C. jejuni strains are examined, such as
isolates from specific ecological niches, the binary gene assay will likely prove even
more powerful as targets can be tailored to suit specific end-user requirements.
Comparison of the SNP-binary gene assay with the SNP-flaA SVR, MLST-flaA SVR
and MLST-binary gene assays demonstrated that all four methods performed
comparably well in discriminating epidemiologically unrelated isolates. However,
none of the assays enabled discrimination of isolates to the same degree as the
‘gold standard’ PFGE methodology. Therefore, the aim of Chapter Five was to add
another locus to the existing SNP-binary gene assay in order to attain resolution
comparable to or surpassing PFGE. In keeping with the PHRANA concept, a locus
was sought that could be efficiently used in combination with the slowly-evolving
SNPs and the moderately unstable binary gene markers.
The CRISPR locus of C. jejuni was chosen as a target to suit this purpose for two
reasons; a) this gene, like any repeat region, is genetically unstable and therefore
Chapter 7: General Discussion
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rapidly evolving, and b) little is known about the distribution and function of
CRISPRs in C. jejuni and C. coli. Repeat regions are typically characterised by size
measurement or DNA sequencing. As previously stated, a major aim of this project
was to develop single-step genotyping methods based on the real-time PCR
platform. The only viable way to characterise repeat regions using real-time PCR is
to determine their unique melting characteristics; however, at the commencement
of this project, the available real-time PCR apparatus lacked the sensitivity to
accurately and reproducibly perform melting temperature (Tm) analysis of PCR
amplicons. Recent advances in thermal uniformity and optics of real-time PCR
apparatus have resulted in the emergence of devices that can perform high-
resolution melt (HRM) analysis, enabling PCR products to be denatured over
temperature increments as low as 0.01oC [29].
The hypothesis that HRM could be used to characterise the C. jejuni CRISPR locus
was tested on 181 sporadic C. jejuni and C. coli isolates of Australian origin. During
the course of the investigation a further 29 C. jejuni isolates, 22 of which were
epidemiologically implicated in outbreaks in Queensland, Australia, were included in
the study and also characterised using the SNP, binary gene, CRISPR HRM and
PFGE methodologies. On the whole the CRISPR results obtained in this body of work
correlated with a previous C. jejuni CRISPR study [26] in terms of CRISPR
distribution, size and prevalence. However, it was an interesting finding that DNA
sequencing revealed no overlap between the European and Australian isolate
CRISPR spacer sequences, strongly suggesting that the CRISPR locus of C. jejuni is
highly polymorphic and rapidly evolving.
Chapter 7: General Discussion
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Another interesting outcome from this study was that the SNP profile typified by ST-
48 was the most common ST (20%) found in the sporadic and outbreak Australian
C. jejuni/C. coli collections, and whilst in each case a single DR was present, there
was variation within these CRISPR sequences that could, in most cases, be detected
by HRM. In addition, the ST-48 isolates differed at a single binary gene, Cj0629,
with strain representatives of all three possible gene states (present, absent and
intermediate). These findings strongly indicate that the ST-48 genotype, unlike in
the US and UK, is ubiquitous and numerically dominant in the Australian C. jejuni
population and plays a significant role in human gastroenteric disease in this
country. This study also demonstrated the value of the combinatorial approach in
differentiating between epidemiologically unrelated cases of ST-48 isolate
occurrence.
The addition of the CRISPR locus to the SNP-binary approach was assessed in both
the sporadic gastroenteric and outbreak C. jejuni and C. coli isolates. In the
gastroenteric isolates, CRISPR HRM separated, in many cases, isolates with identical
SNP-binary profiles, suggesting that these isolates are related but probably not
epidemiologically linked. In contrast, CRISPR HRM did not differentiate the SNP-
binary profiles of the 22 C. jejuni outbreak isolates and in every case the SNP-
binary-CRISPR HRM genotypes correlated with the epidemiological data.
Significantly, addition of the CRISPR HRM profiles to the SNP-binary assay
demonstrated that a comparable degree of resolution to PFGE was obtainable in
both the sporadic and outbreak isolate collections. These results emphasise the
value of the PHRANA approach for high-resolution C. jejuni and C. coli
characterisation and show that the SNP-binary-CRISPR HRM assay can supplant
PFGE for both sporadic and outbreak epidemiology without suffering the
Chapter 7: General Discussion
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shortcomings of PFGE; namely, the difficulty in profile interpretation and labour-
intensity. Alternatively, use of the binary-CRISPR approach alone provides
epidemiological linkage of C. jejuni and C. coli genotypes comparable to those
obtained with the SNP-binary-CRISPR method, albeit with lower resolution,
suggesting that binary-CRISPRs could be used for studies that do not require such
high informative power.
An unexpected but significant outcome that originated from the HRM investigation
was that SYBR® Green I proved much more robust and reproducible than the more
widely adopted HRM chemistry, SYTO® 9. This is the first study to directly compare
the performance of SYBR® Green I and SYTO® 9 using HRM, as well as to apply HRM
to the analysis of genetic polymorphisms other than SNPs. Future research in this
area should focus on comparing other HRM-ready chemistries to SYBR® Green I,
potential candidates including but not limited to BEBO [30], EvaGreen™ [31], LC
Green® [32], SYTOX Orange and TO-PRO-3 [33], to determine the optimal dye for
not only CRISPR characterisation, but for any polymorphic target that can be
assessed by HRM. The issue of HRM data portability encountered in this study has
recently been overcome by our research group (Alex Stephens, personal
communication) making HRM a very attractive and cost-effective alternative to DNA
sequencing.
It was recognised by our research group that one valuable component lacking in the
“Minimum SNPs” software was the ability to select for genetic markers that
confidently and efficiently discriminated user-defined sets of variants. Examples of
such an application include distinguishing strains that are implicated in human
disease from non-pathogenic clones, delineating isolates with particular host
Chapter 7: General Discussion
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specificities, or identifying variants with increased resistance to antimicrobials. To
address this deficit, “Minimum SNPs” was upgraded to incorporate a novel ‘Not-N’
algorithm. The purpose of Not-N is to allow the user to efficiently identify
informative genetic targets that discriminate all variants within a population of
interest from the remaining strain population. Such genetic markers could then
form the basis of targeted genotyping assays to answer epidemiologically or
clinically relevant questions depending on the requirements of the user.
Initially it was investigated whether Not-N could identify SNPs that delineated the
major CCs of C. jejuni, Haemophilus influenzae, Escherichia coli and Staphylococcus
aureus. Overall this endeavour was not as successful as anticipated, particularly
when examining the larger CCs of C. jejuni and S. aureus. The most probable
explanation is that recombination between CCs has played a role in generating
genetic diversity in both species; as a result, STs from different CCs on occasion
share identical MLST alleles, making it difficult or impossible to identify SNPs that
differentiate all STs within a CC from the remaining ST population. This hypothesis
was strengthened by the observation that informative SNPs were identified by Not-
N when smaller CCs of all species were examined, most probably because there are
correspondingly fewer recombinants within these CCs. Therefore the primary
conclusion from the Not-N CC analysis was that this algorithm was not useful for
identifying CC-specific SNPs, particularly for the larger CCs of C. jejuni and S.
aureus, due to the underlying effects of recombination within these bacterial
populations. However, it is probable that Not-N would be highly successful in
identifying phylogenetic SNPs for lowly recombining species, such as Mycobacterium
tuberculosis or Bacillus anthracis, and this warrants further study.
Chapter 7: General Discussion
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In light of the poor performance of Not-N in identifying CC-specific SNPs for C.
jejuni and S. aureus, the software was extended to CGH datasets and viral genome
sequence data in an attempt to find population-specific genetic markers in these
different datasets. CGH data from C. jejuni [12], Yersinia enterocolitica [34] and
Clostridium difficile [35] were selected for Not-N examination as these CGH studies
have utilised Bayesian-based algorithms for defining phylogenetic clades predictive
of infection source (C. jejuni), pathogenicity (Y. enterocolitica) or niche adaptation
(C. difficile). Unlike the CC analysis, the utility of the Not-N algorithm in analysing
complex CGH datasets was demonstrated; Not-N identified sets of genes that
completely correlated with the predicted phylogenetic clades for these three
organisms. Significantly, the targets identified by Not-N consistently outperformed
those identified using MacClade parsimony-based software, which is the software of
choice for reconstructing phylogeny and for interpreting patterns of character
evolution [36]. The targets selected by Not-N for C. jejuni, Y. enterocolitica and C.
difficile are highly informative genetic markers that can easily be incorporated into
current genotyping assays to enable rapid division of isolates based on their
infection source, pathogenicity or niche adaptation, respectively.
Not-N was also applied to viral genome sequence data in an attempt to identify
subtype-specific SNPs diagnostic for particular clinical outcomes or treatment
regimes. I focussed in particular on hepatitis C virus (HCV) as there is a strong
correlation between HCV genotype and patient response to α-interferon/ribavarin
therapy [37]. In addition, many HCV genotypes show geographical specificity, such
as the widespread distribution of HCV subtype 1a throughout Northern Europe and
the US [37]. Most HCV genotyping strategies currently on the market target the 5’-
non-coding region (5’-NCR) as this region is the most conserved in the HCV genome
Chapter 7: General Discussion
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[38]. However, the lack of diversity within the 5’-NCR results in an inability to
discriminate between certain common HCV subtypes, such as 1a and 1b or 2a and
2c [39, 40]. As a consequence the assays either incorrectly assign subtypes or
require additional tests to be carried out to differentiate the subtypes [38].
In contrast to current HCV genotyping methodologies, Not-N successfully identified
15 SNPs from within the RNA-dependent RNA polymerase (NS5B) locus that
efficiently delineated the 13 major subtypes of HCV – 1a, 1b, 2a, 2b, 2c, 3a, 3b, 4a,
4d, 4f, 4t, 5a and 6a - with 100% subtype specificity. This was a surprising finding
as the HCV genome is known to undergo high-frequency recombination, and as
such results similar to those seen using the MLST data were expected. The 15 Not-
N HCV SNPs have immense potential in succeeding existing HCV genotyping
methods, resulting in improved and accurate diagnosis of HCV infection. It is
envisaged that these 15 SNPs will be incorporated into a real-time PCR-based
diagnostic test to allow the rapid, accurate and inexpensive determination of HCV
subtypes. HRM is a promising technology in this regard.
7.2 Conclusions and future directions
This study has laid the foundation for rapid, high-resolution and inexpensive
genotyping of C. jejuni and C. coli using systematically chosen markers with a
future view to employing these procedures in larger scale epidemiological
investigations or for routine surveillance of these pathogens. The estimated cost of
the SNP-binary-CRISPR HRM approach per isolate is between AU$35 and $45,
excluding set-up expenses, making these assays an attractive cost-effective option
for high-throughput and high resolution genotyping of C. jejuni and C. coli. The
Chapter 7: General Discussion
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ultimate outcome from Chapters Three, Four, and Five of this project (a novel C.
jejuni and C. coli PHRANA-based genotyping strategy) would be to apply these
methods to effectively determine the sources of many seemingly sporadic cases of
campylobacteriosis. To achieve this aim, these assays need to be applied to C.
jejuni and C. coli populations with strong supporting epidemiological data or from
particular ecological niches, such as isolates from poultry meat processing, to
definitively determine the routes of transmission to humans. Only then can effective
intervention strategies be devised to reduce the incidence of campylobacteriosis in
the food chain and to increase public awareness of campylobacteriosis prevention.
I have shown that comparative genetic and genomic data from bacterial and viral
species facilitates the identification of small numbers of highly informative genotypic
targets that have potential use in a myriad of applications. In this project both the
D and Not-N modules of “Minimum SNPs” were integral in the development of either
de novo methodologies (as described for C. jejuni and C. coli in Chapters Three,
Four and Five) or in the identification of superior replacements for currently adopted
genetic markers and assays (as described for CGH and HCV genome datasets in
Chapter Six). However, the approaches described in this thesis are not restricted to
particular organisms and can potentially be applied to any species for which
comparative genetic data is available. In this context, the “Minimum SNPs” software
package will prove indispensable for developing or refining genotyping assays that
are targeted towards a limitless range of organisms. In addition, the emerging HRM
or LOaC technologies provide exciting new tools for reducing the cost of current
sequence-based and real-time PCR-based genotyping assays. Potential avenues of
immediate future research include the replacement of the current allele-specific
(AS) SNP interrogation method with a simple and cost-effective HRM procedure, or
Chapter 7: General Discussion
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the development of HRM protocols to supplant sequence-based methods such as
MLST or flaA SVR sequencing.
7.3 Major findings of this thesis
1. Using “Minimum SNPs”, seven SNPs were identified from the C. jejuni/C. coli
MLST database that provided a D of 0.98 compared with complete MLST
characterisation. The combination of a real-time PCR-based high-D SNP
assay for the C. jejuni and C. coli SNPs with flaA SVR sequencing provided
both a comparable degree of resolution and epidemiological fingerprints
highly similar to the commonly used MLST-flaA SVR approach.
2. Using “Minimum SNPs”, eight binary genes were found in 18 C. jejuni strains
characterised by CGH that provided a D of 1 compared with complete CGH
characterisation. Addition of the eight binary genes to the high-D SNP assay
yielded comparable resolution to SNP-flaA SVR, providing a replacement for
the DNA sequencing-based flaA SVR method.
3. Real-time PCR-based HRM analysis of the CRISPR locus of C. jejuni, in
combination with the SNP-binary gene method, permitted strain
discrimination that was comparable to the current gold standard procedure,
PFGE. The benefits of the SNP-binary-CRISPR HRM assay over MLST, flaA
SVR and PFGE lie in the reduced cost, time and labour intensity of the real-
time PCR-based methodologies whilst maintaining all assays on a unified
platform.
Chapter 7: General Discussion
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4. HRM was shown, for the first time, to be efficacious in differentiating genetic
polymorphisms other than SNPs, strongly suggesting that this emerging
technology is a cost-effective and efficient alternative to DNA sequencing.
5. This thesis also showed for the first time a direct comparison between the
SYBR® Green I and SYTO® 9 chemistries using HRM. Contrary to commonly
accepted view, the SYBR® Green I chemistry was superior to SYTO® 9 for
CRISPR characterisation using HRM, proving a more reproducible and robust
dye than SYTO® 9.
6. The third module of the “Minimum SNPs” software, Not-N, identified genes
superior to those previously identified by MacClade parsimony-based
software for C. jejuni, Y. enterocolitica and C. difficile CGH data. A small
number of genes (between two and four) were found by Not-N that
characterised isolates, with 100% confidence, based on their infection
source, pathogenicity or niche adaptation.
7. Not-N identified 15 SNPs from the NS5B gene of HCV that, with 100%
confidence, delineated the 13 major HCV subtypes. These 15 SNPs are
superior in performance to SNPs that are currently used for HCV genotyping.
Chapter 7: General Discussion
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Appendix
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APPENDIX
Isolate collection no. 1: Institute of Medical and Veterinary Sciences (IMVS) OzFoodNet 'sporadic' human campylobacteriosis isolates
5 F363 11 C. jejuni ST-353 A A C A1 C G C 1 n/a I A P A P A A A 1 3 1 85 F412 11 C. jejuni ST-353 A A C A1 C G C 1 n/a I A P A P A A A 1 3 1 85 F448 11 C. jejuni ST-353 A A C A1 C G C 1 n/a I A P A P A A A 1 3 1 ND
527 F128 11 C. jejuni ST-353 A A C A1 C G C 1 n/a I A P A P A A A 1 3 1 8527 F212 11 C. jejuni ST-353 A A C A1 C G C 1 n/a I A P A P A A A 1 4 1 ND21 F449 8 C. jejuni ST-21 A G C A1 T1 A C 4 n/a P A P P P P P P 8 31 6 ND21 F494 8 C. jejuni ST-21 A G C A1 T1 A C 4 n/a P A P P P P P P 8 20 4 1053 F162 1 C. jejuni ST-21 A G C A1 T1 A C 4 n/a I A P P P P P P 3 32 6 10190 F304 1 C. jejuni ST-21 A G C A1 T1 A C 4 n/a A P P P P A P P 13 43 3 ND569 F112 1 C. jejuni ST-21 A G C A1 T1 A C 4 n/a A P P P P A P P 13 44 3 ND43 NCTC 11168 9 C. jejuni ST-21 A G C A1 T1 A C 4 n/a P P P P P P P P 9 21 4 ND25 F093 1 C. jejuni ST-45 G G T1 A1 C G T 6 n/a A A A A P A A A 17 22 4 125 F405 1 C. jejuni ST-45 G G T1 A1 C G T 6 n/a A A A A P A A A 17 22 4 ND45 F377 5 C. jejuni ST-45 G G T1 A1 C G T 6 n/a A P A P P A A A 19 51 5 19529 F381 9 C. jejuni ST-45 G G T1 A1 C G T 6 n/a A A A P A A A A 11 59 2 521616 NCTC 11351 156 C. jejuni ST-403 G G T1 A1 C G T 6 n/a A P A A P A A A 20 52 5 ND42 F063 9 C. jejuni ST-42 G G C A1 C G T 9 n/a A A A A A A A A 24 n/a n/a 1042 F064 1 C. jejuni ST-42 G G C A1 C G T 9 n/a A A A A A A A A 24 n/a n/a 2242 F067 9 C. jejuni ST-42 G G C A2 C G T 9 n/a A A A P A A A A 11 n/a n/a 342 F235 9 C. jejuni ST-42 G G C A3 C G T 9 n/a A A A P A A A A 11 n/a n/a 5148 F071 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a A A P P P A P P 22 5 1 ND48 F089 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a A A P P P A P P 22 5 1 1148 F491 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a A A P P P A P P 22 5 1 1148 F022 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a I A P P P A P P 7 5 1 1148 F168 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a I A P P P A P P 7 5 1 1248 F211 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a I A P P P A P P 7 5 1 1148 F217 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a I A P P P A P P 7 5 1 ND48 F350 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a I A P P P A P P 7 5 1 ND48 F353 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a I A P P P A P P 7 5 1 1148 F418 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a I A P P P A P P 7 5 1 2348 F421 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a I A P P P A P P 7 5 1 1148 F440 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a I A P P P A P P 7 5 1 ND48 F460 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a I A P P P A P P 7 5 1 ND48 F513 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a I A P P P A P P 7 5 1 ND48 F410 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a P A P P P A P P 10 5 1 ND48 F152 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a P A P P P A P P 10 5 1 ND48 F024 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a P A P P P A P P 10 5 1 1148 F027 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a P A P P P A P P 10 5 1 ND48 F028 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a P A P P P A P P 10 5 1 1148 F219 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a P A P P P A P P 10 5 1 ND48 F255 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a P A P P P A P P 10 5 1 ND48 F002 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a P A P P P A P P 10 5 1 1148 F492 1 C. jejuni ST-48 A A T1 A T1 G C 10 n/a P A P P P A P P 10 5 1 ND
SUMMARY OF C. jejuni AND C. coli GENOTYPING RESULTS
Binary genes
gltA 12
uncA189
pgm 348
tkt 297
possible ST from SNPs* Cj0629
SNP type
MLST-derived high-D SNPsMLST
CC aspA 174
glyA 267
glnA 369
ST Isolate IDflaA SVR
SpeciesCj0265c Cj0178 Cj0299 Cj1319 Cj1723c Cj0008 Cj0486
BTCRISPR
HRM type
CRISPR repeat
no.PFGE
Appendix
- 184 -
50 F239 350 C. jejuni ST-21 A G C G T1 A C 5 n/a P P P P P A A P 21 16 6 ND50 F051 1 C. jejuni ST-21 A G C G T1 A C 5 n/a P P P P P A A P 21 3 1 ND50 F431 1 C. jejuni ST-21 A G C G T1 A C 5 n/a P P P P P A A P 21 60 2 ND50 F536 1 C. jejuni ST-21 A G C G T1 A C 5 n/a P P P P P A A P 21 17 4 1050 F014 8 C. jejuni ST-21 A G C G T1 A C 5 n/a P P P P P P A P 25 33 6 ND50 F251 8 C. jejuni ST-21 A G C G T1 A C 5 n/a P P P P P P A P 25 16 4 650 F045 10 C. jejuni ST-21 A G C G T1 A C 5 n/a P P P P P P A P 25 60 2 ND451 F113 1 C. jejuni ST-21 A G C G T1 A C 5 n/a A P P P P A P P 13 18 4 10451 F286 1 C. jejuni ST-21 A G C G T1 A C 5 n/a A P P P P A P P 13 19 4 10536 F079 10 C. jejuni ST-21 A G C G T1 A C 5 n/a P A P P P A A P 14 n/a n/a 1451 F280 2 C. jejuni ST-443 A G C G C A C 11 n/a A A P P P A A P 16 34 6 ND52 F009 4 C. jejuni ST-52 G G C G C A C 12 n/a P A P P P A A P 14 45 3 3752 F041 4 C. jejuni ST-52 G G C G C A C 12 n/a P A P P P A A P 14 23 4 3352 F132 4 C. jejuni ST-52 G G C G C A C 12 n/a P A P P P A A P 14 24 4 3452 F183 4 C. jejuni ST-52 G G C G C A C 12 n/a P A P P P A A P 14 53 5 3352 F226 4 C. jejuni ST-52 G G C G C A C 12 n/a P A P P P A A P 14 35 7 35161 F451 2 C. jejuni ST-52 G G C G C A C 12 n/a A A P P P A A P 16 25 4 46161 F501 2 C. jejuni ST-52 G G C G C A C 12 n/a A A P P P A A P 16 25 4 ND161 F004 4 C. jejuni ST-52 G G C G C A C 12 n/a A A P P P A A P 16 26 4 44161 F025 10 C. jejuni ST-52 G G C G C A C 12 n/a A A P P P A A P 16 46 3 4570 F266 4 C. jejuni ST-52 G G C G C A C 12 n/a P A P P P A A P 14 3 1 ND61 F033 14 C. jejuni ST-61 G A T1 G T2 A C 13 n/a A A A P A A A A 11 61 2 11227 F001 10 C. jejuni ST-206 A A T1 A1 T1 A C 16 n/a P P P P P A P P 26 39 3 18227 F309 10 C. jejuni ST-206 A A T1 A1 T1 A C 16 n/a P P P P P A P P 26 39 3 ND227 F159 1 C. jejuni ST-206 A A T1 A1 T1 A C 16 n/a P P P P P A P P 26 39 3 18227 F392 1 C. jejuni ST-206 A A T1 A1 T1 A C 16 n/a P P P P P A P P 26 39 3 18227 F401 1 C. jejuni ST-206 A A T1 A1 T1 A C 16 n/a P P P P P A A P 21 39 3 18233 F288 1 C. jejuni ST-45 A G T1 A1 C G T 7 n/a A P A A P A A A 20 47 3 ND197 F006m 12 C. jejuni ST-257 G A C G C A T 14 n/a I P P P A A P P 4 2 1 ND197 F042 12 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 15 4 43257 F101 1 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 54 5 40257 F301 1 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 54 5 42257 F125 2 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 55 5 ND257 F254 4 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 54 5 ND257 F402 8 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 54 5 ND257 F053 12 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 54 5 41257 F092 12 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 54 5 ND257 F130 12 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 54 5 ND257 F151 12 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 15 4 49257 F167 12 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 54 5 ND257 F276 12 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 54 5 ND257 F308 12 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 54 5 31257 F404 12 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 54 5 41257 F502 12 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 54 5 ND257 F519 12 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 54 5 ND257 F087 20 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 54 5 40257 F218 20 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 54 5 ND532 F470 12 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 62 2 ND532 F535 12 C. jejuni ST-257 G A C G C A T 14 n/a A A P P A A A P 15 63 2 ND312 F100 1 C. jejuni ST-658 G G C G C G C 17 n/a A A P P P A P A 34 8 1 38524 F055 10 C. jejuni ST-353 G A C A1 C G C 2 n/a P A P A P A A A 30 27 4 2524 F380 10 C. jejuni ST-353 G A C A1 C G C 2 n/a P A P A P A A A 30 27 4 25526 F081 3 C. jejuni n/a A G T1 G C A C 22 n/a A A A A P A A A 17 40 3 9
Cj0008 Cj0486
CRISPR HRM type
CRISPR repeat
no.PFGEaspA
174glyA 2
67glnA 3
69gltA 1
2uncA189
pgm 348
tkt 297
SNP type
possible ST from SNPs*
Binary genes
BTCj0629 Cj0265c Cj0178 Cj0299 Cj1319 Cj1723c
ST Isolate IDflaA SVR
SpeciesMLST
CC
MLST-derived high-D SNPs
Appendix
- 185 -
354 F050 20 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 66 11 48354 F066 37 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 67 11+4 39528 F489 1 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 64 8 ND528 F495 1 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 64 8 ND528 F511 1 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 64 8 ND528 F228 11 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 64 8 30528 F310 20 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 64 8 30528 F316 20 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 64 8 32528 F348 20 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 64 8 30528 F360 20 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 64 8 ND528 F395 20 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 64 8 ND528 F396 20 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 64 8 1528 F400 20 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 64 8 30528 F428 20 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 64 8 ND528 F429 20 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 64 8 ND528 F443 20 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 65 5+8 ND528 F453 20 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 64 8 ND528 F475 20 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 64 8 ND528 F490 20 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 64 8 ND528 F500 20 C. jejuni ST-354 G A C G C A C 15 n/a A A P P P A A P 16 64 8 ND533 F537 1 C. jejuni ST-52 G A C G C A C 15 n/a I A P A P A A A 1 41 3 27449 F141 14 C. jejuni ST-61 A A T1 A1 C A C 20 n/a A A P P P A A A 18 36 7 5449 F062 33 C. jejuni ST-61 A A T1 A1 C A C 20 n/a A A P P P A A A 18 36 7 50531 F486 1 C. jejuni n/a A A T1 A1 C A C 20 n/a A A P P P A A A 18 3 1 ND531 F114 2 C. jejuni n/a A A T1 A1 C A C 20 n/a A A P P P A A A 18 3 1 ND531 F005 5 C. jejuni n/a A A T1 A1 C A C 20 n/a A A P P P A A A 18 3 1 47531 F038 5 C. jejuni n/a A A T1 A1 C A C 20 n/a A A P P P A A A 18 7 1 15531 F039 5 C. jejuni n/a A A T1 A1 C A C 20 n/a A A P P P A A A 18 3 1 ND531 F061 5 C. jejuni n/a A A T1 A1 C A C 20 n/a A A P P P A A A 18 3 1 16531 F166 5 C. jejuni n/a A A T1 A1 C A C 20 n/a A A P P P A A A 18 3 1 13531 F178 20 C. jejuni n/a A A T1 A1 C A C 20 n/a A A P P P A A A 18 3 1 17531 F522 20 C. jejuni n/a A A T1 A1 C A C 20 n/a A A P P P A A A 18 n/a 1 ND531 F108 20 C. jejuni n/a A A T1 A1 C A C 20 n/a A P P P P A A A 23 3 1 13523 F044 1 C. jejuni ST-658 A G T1 A1 C A C 18 n/a I P A P A A P P 27 2 1 20523 F213 71 C. jejuni ST-655 A G T1 A1 C A C 18 n/a I P A P A A P P 27 2 1 ND523 F030 90 C. jejuni ST-656 A G T1 A1 C A C 18 n/a I P A P A A P P 27 2 1 ND523 F187 1 C. jejuni ST-657 A G T1 A1 C A C 18 n/a I P A A A A A P 28 2 1 ND523 F037 2 C. jejuni ST-658 A G T1 A1 C A C 18 n/a I P P P A A P P 4 2 1 25523 F270 11 C. jejuni ST-658 A G T1 A1 C A C 18 n/a I P P P P A A P 5 9 1 ND523 F509 71 C. jejuni ST-658 A G T1 A1 C A C 18 n/a I P P P A A A P 6 2 1 21525 F057 2 C. jejuni ST-607 A G C A1 C A T 21 n/a A A P A P A A A 31 48 3 53525 F118 2 C. jejuni ST-607 A G C A1 C A T 21 n/a A A P A P A A A 31 29 6 25525 F147 2 C. jejuni ST-607 A G C A1 C A T 21 n/a A A P A P A A A 31 29 6 ND525 F216 2 C. jejuni ST-607 A G C A1 C A T 21 n/a A A P A P A A A 31 29 6 ND525 F234 2 C. jejuni ST-607 A G C A1 C A T 21 n/a A A P A P A A A 31 29 6 25525 F455 2 C. jejuni ST-607 A G C A1 C A T 21 n/a A A P A P A A A 31 29 6 29530 F105 8 C. jejuni n/a G G C G T1 A C 23 n/a A A P A P A P A 32 49 3 ND530 F459 8 C. jejuni n/a G G C G T1 A C 23 n/a A A P A P A P A 32 56 5 ND530 F445 8 C. jejuni n/a G G C G T1 A C 23 n/a P A P P P A P A 33 49 3 ND530 F165 8 C. jejuni n/a G G C G T1 A C 23 n/a P A P P P A P A 33 49 3 28530 F387 8 C. jejuni n/a G G C G T1 A C 23 n/a P A P P P A P A 33 50 3 26530 F388 8 C. jejuni n/a G G C G T1 A C 23 n/a P A P P P A P A 33 49 3 26530 F520 8 C. jejuni n/a G G C G T1 A C 23 n/a A A P P P A P A 34 49 3 ND535 F007 4 C. jejuni ST-460 G A T1 G C A C 19 n/a A A A A P A A P 29 37 7 36537 F364 11 C. jejuni ST-353 A A C A1 C A C 3 n/a I A P A P A A P 2 3 1 ND538 F458 12 C. jejuni ST-45 G G T1 A1 C G C 8 n/a A A A P A A A A 11 38 7 ND567 F090 9 C. jejuni ST-22 G G T1 A1 C G C 8 n/a A A P P A A A A 12 28 4 24555 F068 16 C. coli n/a T G T2 A2 T2 G T 24 n/a A A P P A A A A 12 n/a n/a 7555 F069 16 C. coli n/a T G T2 A2 T2 G T 24 n/a A A A P A A A A 11 n/a n/a 11
Cj0629
SNP typegltA 1
2uncA189
pgm 348
tkt 297
Binary genes
aspA 174
BTCRISPR
HRM type
CRISPR repeat
no.PFGEglyA 2
67glnA 3
69
ST Isolate IDflaA SVR
SpeciesMLST
CC
MLST-derived high-D SNPspossible ST from SNPs* Cj0265c Cj0178 Cj0299 Cj1319 Cj1723c Cj0008 Cj0486
Appendix
- 186 -
Isolate collection no. 2: Princess Alexandra Hospital 'sporadic' human campylobacteriosis isolates
ND PA01 30 C. coli n/a T G T2 A2 T2 G T 24 ST-555 A A A P P A A P 36 n/a n/a NDND PA02 17 C. coli n/a T G T2 A2 T2 G T 24 ST-555 A A A P P A A P 36 n/a n/a NDND PA03 16 C. coli n/a T G T2 A2 T2 G T 24 ST-555 A A A A P A A A 17 n/a n/a NDND PA19 17 C. coli n/a T G T2 A2 T2 G T 24 ST-555 A A A P P A A P 36 n/a n/a NDND PA20 467 C. coli n/a T G T2 A2 T2 G T 24 ST-555 A A A P P A A A 35 n/a n/a NDND PA04 16 C. jejuni n/a G A C G C A T 14 ST-257 A A P P A A A P 15 54 5 NDND PA22 16 C. jejuni n/a G A C G C A T 14 ST-257 A A P P A A A P 15 54 5 NDND PA25 16 C. jejuni n/a G A C G C A T 14 ST-257 A A P P A A A P 15 54 5 NDND PA05 36 C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 A A P P P A P P 22 2 1 NDND PA06 36 C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 A A P P P A P P 22 2 1 NDND PA07 36 C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 I A P P P A P P 7 2 1 NDND PA13 36 C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 A A P P P A P P 22 2 1 NDND PA14 36 C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 A A P P P A P P 22 2 1 NDND PA15 36 C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 A A P P P A P P 22 2 1 NDND PA16 36 C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 A A P P P A P P 22 2 1 NDND PA23 36 C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 A A P P P A P P 22 2 1 NDND PA24 36 C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 P A P P P A P P 10 2 1 NDND PA26 36 C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 A A P P P A P P 22 2 1 NDND PA29 36 C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 I A P P P A P P 7 2 1 NDND PA08 222 C. jejuni n/a A A T1 A1 C A C 20 ST-449 A A P P P A A A 18 59 2 NDND PA09 57 C. jejuni n/a G G C G C A C 12 ST-161 P A P P P A A P 14 42 3 NDND PA11 57 C. jejuni n/a G G C G C A C 12 ST-161 P A P P P A A P 14 42 3 NDND PA12 18 C. jejuni n/a G A C G C A C 15 ST-354 A A P P P A A P 16 64 8 ND227 PA17 9 C. jejuni ST-206 A A T1 A1 T1 A C 16 ST-227 P A P P P A P P 10 39 3 NDND PA21 9 C. jejuni n/a A A T1 A1 T1 A C 16 ST-227 P A P P P A P P 10 39 3 NDND PA18 9 C. jejuni n/a A G C G T1 A C 5 ST-50 P A P P P A A P 14 30 6 ND583 PA28 239 C. jejuni ST-45 G G T1 A1 C G T 6 ST-583 A A A P A A A A 11 9 4 ND
SNP type
ST Isolate IDflaA SVR
SpeciesMLST
CC
MLST-derived high-D SNPspossible ST from SNPs*
Binary genes
BTCRISPR
HRM type
CRISPR repeat
no.Cj0178 Cj0299 Cj1319 Cj1723c Cj0008 Cj0486PFGEaspA
174glyA 2
67glnA 3
69gltA 1
2uncA189
pgm 348
tkt 297
Cj0629 Cj0265c
Appendix
- 187 -
Isolate collection no. 3: Queensland Health Scientific Services 'outbreak' human campylobacteriosis isolates
ND QHSS1 ND C. jejuni n/a G G T1 A1 C G C 8 ST-538 A A P P A A A A 12 1 4 PT 4ND QHSS5 ND C. jejuni n/a G G T1 A1 C G C 8 ST-538 A A P P A A A A 12 1 4 PT 4ND QHSS6 ND C. jejuni n/a G G T1 A1 C G C 8 ST-538 A A P P A A A A 12 1 4 PT 4ND QHSS7 ND C. jejuni n/a G G T1 A1 C G C 8 ST-538 A A P P A A A A 12 1 4 PT 4aND QHSS23 ND C. jejuni n/a G G T1 A1 C G C 8 ST-538 A A P P A A A A 12 57 2 PT 11ND QHSS24 ND C. jejuni n/a G G T1 A1 C G C 8 ST-538 A A P P A A A A 12 57 2 PT 11ND QHSS19 ND C. jejuni n/a G G T1 A1 C G C 8 ST-538 A A P P P A P P 22 2 1 PT 10ND QHSS8 ND C. jejuni n/a A G T1 A1 T1 G C 27 NEW A A P P P A P P 22 2 1 PT 3ND QHSS4 ND C. jejuni n/a A G C G T1 A C 5 ST-50 P P P P P A A P 21 11 4 PT 8ND QHSS18 ND C. jejuni n/a A G C G T1 A C 5 ST-50 P P P P P A A P 21 12 4 PT 5ND QHSS20 ND C. jejuni n/a A G C G T1 A C 5 ST-50 P P P P P A A P 21 12 4 PT 5ND QHSS21 ND C. jejuni n/a A G C G T1 A C 5 ST-50 P P P P P A A P 21 58 2 PT 5ND QHSS26 ND C. jejuni n/a G G C G C A C 12 ST-52 P A P P P A A P 14 13 4 PT 1ND QHSS27 ND C. jejuni n/a G G C G C A C 12 ST-52 P A P P P A A P 14 13 4 PT 1ND QHSS28 ND C. jejuni n/a G G C G C A C 12 ST-52 P A P P P A A P 14 13 4 PT 1ND QHSS29 ND C. jejuni n/a G G C G C A C 12 ST-52 P A P P P A A P 14 13 4 PT 1ND QHSS17 ND C. jejuni n/a G G C A1 C A C 26 NEW P A P P P A A P 14 14 4 PT 9ND QHSS2 ND C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 A A P P P A P P 22 2 1 PT 6ND QHSS3 ND C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 A A P P P A P P 22 2 1 PT 6ND QHSS9 ND C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 A A P P P A P P 22 2 1 PT 6ND QHSS10 ND C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 A A P P P A P P 22 2 1 PT 6ND QHSS11 ND C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 A A P P P A P P 22 2 1 PT 6ND QHSS12 ND C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 A A P P P A P P 22 2 1 PT 6ND QHSS15 ND C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 A A P P P A P P 22 2 1 PT 6ND QHSS16 ND C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 A A P P P A P P 22 2 1 PT 6ND QHSS22 ND C. jejuni n/a A A T1 A1 T1 G C 10 ST-48 A A P P P A P P 22 2 1 PT 6AND QHSS25 ND C. jejuni n/a A A T1 A1 T1 A C 16 ST-227 P P P P P A P P 26 39 3 PT 2ND QHSS13 ND C. jejuni n/a G A C A1 C G T 25 NEW A A A A A A A A 24 10 1 PT 7ND QHSS14 ND C. jejuni n/a G A C A1 C G T 25 NEW A A A A A A A A 24 10 1 PT 7
Abbreviations: ST, sequence type; MLST CC, multilocus sequence typing clonal complex; BT, binary type; CRISPR HRM, clustered regularly interspaced short palindromic repeat high-resolution melt; n/a, not applicable; ND, not determined.
NB: The PFGE performed on the OzFoodNet and QHSS isolates was done separately, therefore PFGE types are not directly comparable between these two collections. Isolates in bold were subjectedto CRISPR sequencing.
* Based on known MLST profiles from the Australian OzFoodNet isolates. When an isolate contains a profile not previously encountered in the OzFoodNet isolates, the profile is designated "New".
Cj0008 Cj0486
CRISPR repeat
no.PFGE
CRISPR HRM typeaspA
174glyA 2
67glnA 3
69gltA 1
2pgm 3
48tkt 297
Cj0629
SNP type
ST Isolate IDflaA SVR
SpeciesMLST
CC
possible ST from SNPs*
Binary genes
BTCj0265c Cj0178 Cj0299 Cj1319 Cj1723c
uncA189
MLST-derived high-D SNPs
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