development of novel combinatorial methods for...

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

v

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:

vii

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.

xvii

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.

xix

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

xxi

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


Fingerprinting of Campylobacter jejuni by using resolution-optimized binary gene

targets derived from Comparative Genome Hybridization studies 124

CHAPTER FIVE. RESULTS 135


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


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

Chapter 1: Introduction

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CHAPTER ONE

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


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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.


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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:


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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


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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


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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.


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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


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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.


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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.


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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.

9. Duim, B., Wassenaar, T. M., Rigter, A. and Wagenaar, J. 1999. High-resolution

genotyping of Campylobacter strains isolated from poultry and humans with amplified

fragment length polymorphism fingerprinting. Appl Environ Microbiol. 65: 2369-

2375.

10. Wells, J. M. and Bennik, M. H. J. 2003. Genomics of food-borne bacterial

pathogens. Nutr Res Rev. 16: 21-35.

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5: 507-520.

Chapter 2: Literature Review

- 17 -

CHAPTER TWO

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.


- 19 -

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


- 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


- 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


- 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].


- 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].


- 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


- 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


- 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


- 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


- 28 -

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


- 29 -

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


- 30 -

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.


- 31 -

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


- 32 -

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


- 33 -

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


- 34 -

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


- 35 -

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


- 36 -

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.


- 37 -

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,


- 38 -

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,


- 41 -

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


- 42 -

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


- 43 -

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


- 44 -

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


- 46 -

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].


- 47 -

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


- 48 -

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


- 49 -

(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].


- 50 -

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


- 51 -

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


- 52 -

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


- 53 -

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


- 54 -

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


- 55 -

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].


- 56 -

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

halla

This figure is not available online. Please consult the hardcopy thesis available from the QUT Library


- 57 -

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


- 58 -

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


- 59 -

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


- 60 -

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.


- 61 -

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].


- 62 -

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


- 63 -

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].


- 64 -

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


- 65 -

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


- 66 -

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,


- 67 -

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.


- 68 -

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.


- 76 -

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


- 79 -

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


- 80 -

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,


- 81 -

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


- 82 -

[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


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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halla

This article is not available online. Please consult the hardcopy thesis available from the QUT Library

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,


Brisbane QLD 4059 Australia

Appl Environ Microbiol. (2006) 72: 7793-7803.

Chapter 4. Identifying binary gene targets in Campylobacter jejuni

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halla


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.


Institute of Health and Biomedical Innovation



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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halla


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.


Institute of Health and Biomedical Innovation



BMC Bioinformatics (2007) 8:278.


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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


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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


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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


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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


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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


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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


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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].


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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


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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-


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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


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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.


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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


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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


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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.


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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


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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


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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.


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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


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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


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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


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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.


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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.


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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


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


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


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


SpeciesMLST

CC

possible ST from SNPs*

Binary genes

BTCj0265c Cj0178 Cj0299 Cj1319 Cj1723c

uncA189

MLST-derived high-D SNPs

development of novel combinatorial methods for...

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