comparative study of coagulase negative from...
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Comparative study of Coagulase Negative
Staphylococci (CoNS) from Clinical Isolates, Skin
and Nasal Sources
A Thesis in Molecular Microbiology
By
Malik Asif Hussain
MBBS
B.Sc (Medical Lab. Technology)
Thesis submitted to Queensland University of Technology in fulfilment of
the requirements for the degree for Masters of Applied Science (Research)
July 2011
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StatementofOriginalAuthorship
The work contained 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.
____________________________
Malik Asif Hussain.
Date: 19‐07‐2011
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DEDICATION
I dedicate this work to my beloved parents, supportive brothers and to my sweet sister.
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Acknowledgements
First of all, I would like to thank my principal supervisor, Assoc. Professor Flavia Huygens, for all
her support, efforts and guidance. It would not have been possible to complete this work
without her motivation and encouragement at every single step. Her great vision, knowledge
and experience made things very smooth and organized. Thank you very much for providing
assistance and helping me.
Secondly, I am really grateful to my associate supervisor, Associate Professor Megan Hargreaves,
who was always ready to help and guide. Without her support it would have been very difficult
to finalize this work. Thank you for your help during my course.
Thirdly, to my brother, Dr Malik Altaf Hussain, who has been a source of inspiration throughout
my educational career. He has helped and is helping me to cover all milestones of life. Thank you
very much.
I am thankful to all people at QUT, who have helped me. My special thanks to Vincent, Sue,
Marysia and Mark who helped me in completing my experiments. I am thankful to Irani
Rathnayake and Maxim Sheludchenko for their extreme help during my work. I am also thankful
to Chaminda Ranasinghe, Farhana Sharmin, Phillipa Perrott, Sharri Minion and all other co‐
workers. I have learnt a lot from you all and thank you for your help and support.
Finally, I am thankful to my family and friends, who have contributed to provide a supportive
and encouraging environment around me.
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Keywords
Coagulase‐Negative Staphylococci
High Resolution Melt Analysis (HRMA)
Single Nucleotide Polymorphism (SNP)
Staphylococcus epidermidis
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TableofContents
Statement of Original Authorship .......................................................................................................... 3
DEDICATION ........................................................................................................................................... 5
Acknowledgements ................................................................................................................................ 7
Keywords ................................................................................................................................................ 9
Table of Contents ................................................................................................................................. 11
List of Tables ......................................................................................................................................... 15
List of Figures ........................................................................................................................................ 17
List of Abbreviations ............................................................................................................................. 19
ABSTRACT ............................................................................................................................................. 21
CHAPTER 1 ............................................................................................................................................ 23
INTRODUCTION .................................................................................................................................... 23
1.1 INTRODUCTION .......................................................................................................................... 24
1.2 HYPOTHESIS, AIMS AND OBJECTIVES ......................................................................................... 26
1.3 CRITICAL ANALYSIS OF THE LITERATURE .................................................................................... 27
1.3.1 General overview of Staphylococci ..................................................................................... 27
1.3.2 Coagulase‐Negative Staphylococci (CoNS) .......................................................................... 29
1.3.3 Pathogenesis (CoNS) ............................................................................................................ 30
1.3.4 Staphylococcus epidermidis ................................................................................................. 32
1.3.5 Antibiotic resistance in S. epidermidis ................................................................................. 45
1.3.6 Genotyping and Phenotyping .............................................................................................. 48
1.3.7 Prevention, problems and future directions ....................................................................... 51
1.4 SIGNIFICANCE ............................................................................................................................. 54
1.5 CONCLUSION .............................................................................................................................. 56
CHAPTER 2 ............................................................................................................................................ 57
MATERIALS AND METHODS ................................................................................................................. 57
2.1 ISOLATES ..................................................................................................................................... 58
2.2 BACTERIAL CULTURES ................................................................................................................. 58
2.2.1 Clinical Isolates .................................................................................................................... 58
2.2.2 Skin and Nasal mucosa swab samples ................................................................................. 59
2.3 DNA EXTRACTION ....................................................................................................................... 59
2.4 GENTOTYPING OF CoNS USING SINGLE NUCLEOTIDE POLYMORPHISMS (SNPs) (AIMS 1 and
2) ....................................................................................................................................................... 61
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2.4.1 SNP identification ................................................................................................................. 61
2.4.2 Primers ................................................................................................................................. 62
2.4.3 Real‐Time PCR ...................................................................................................................... 63
2.4.4 Assignment of SNP profiles using High‐Resolution Melt curve analysis .............................. 64
2.4.5 Confirmation of HRM‐SNP profiles using DNA sequencing ................................................. 65
2.4.6 SNP validation by DNA sequencing ...................................................................................... 65
2.5 DETECTION OF ica GENES INVOLVED IN BIOFILM FORMATION (AIM 3) .................................... 69
2.5.1 Primers ................................................................................................................................. 69
2.5.2 Conventional PCR ................................................................................................................. 70
2.5.3 Gel electrophoresis .............................................................................................................. 71
2.6 DETECTION OF ANTIBIOTIC RESISTANCE GENES (AIM 3) ............................................................ 71
2.6.1 Primers ................................................................................................................................. 71
2.6.2 Real‐Time PCR ...................................................................................................................... 72
CHAPTER 3 ............................................................................................................................................ 75
GENOTYPING OF CoNS .......................................................................................................................... 75
3.1 INTRODUCTION ........................................................................................................................... 76
3.2 AIMS ............................................................................................................................................ 77
3.3 METHODS .................................................................................................................................... 77
3.4 RESULTS ....................................................................................................................................... 78
3.4.1: SNP analysis using HRMA .................................................................................................... 78
3.4.2 Strain code assignment to SNP profiles ............................................................................... 81
3.4.3: ScreenClust HRM analysis ................................................................................................... 83
3.5 CONFIRMATION OF HRM‐SNP PROFILES USING DNA SEQUENCING .......................................... 85
3.6 CONFIRMATION OF HRM‐SNP PROFILES USING DIFFERENCE CURVE ANALYSIS ........................ 86
3.7 DISCUSSION ................................................................................................................................. 89
CHAPTER 4 ............................................................................................................................................ 91
ica OPERON AND ANTIBIOTIC RESISTANCE GENES ............................................................................... 91
4.1 INTRODUCTION ........................................................................................................................... 92
4.2 ica OPERON ................................................................................................................................. 93
4.2.1 Aim 3 .................................................................................................................................... 93
4.2.2 Methods ............................................................................................................................... 94
4.2.3 Results .................................................................................................................................. 94
4.3 ANTIBIOTIC RESISTANCE GENES.................................................................................................. 97
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4.3.1 Aim 3 .................................................................................................................................... 97
4.3.2 Methods .............................................................................................................................. 97
4.3.3 Results ................................................................................................................................. 99
4.4 DISCUSSION .............................................................................................................................. 100
4.4.1 ica Operon ......................................................................................................................... 100
4.4.2 Antibiotic resistance genes ................................................................................................ 102
CHAPTER 5 .......................................................................................................................................... 103
GENERAL CONCLUSIONS, ................................................................................................................... 103
LIMITATIONS AND FUTURE DIRECTIONS ............................................................................................ 103
REFERENCES ....................................................................................................................................... 107
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ListofTables
Table 2.1: List of SNPs derived from the Minimum SNPs software program indicating the
cumulative Simpson’s Index of Diversity (D) value for each SNP
Table 2.2: Primer sequences for each SNP
Table 2.3: Each RT‐PCR Reaction contained (SNP Analysis)
Table 2.4: Primers used for DNA sequencing
Table 2.5: Each PCR reaction contained (DNA Sequencing)
Table 2.6: Each PCR reaction contained (DNA Sequencing 2)
Table 2.7: Steps of Ethanol/EDTA precipitation
Table 2.8: Primer sequences for ica genes
Table 2.9: Each PCR reaction contained (ica genes)
Table 2.10: Primers and controls for antibiotic resistance genes
Table 2.11: Each Real‐Time PCR reaction contained (Antibiotics)
Table 3.1: SNPs and Strain codes (SC)
Table 3.2: Strain codes (SC) distribution amongst different sources
Table 3.3: DNA sequencing results of nucleotide bases present at specific SNP positions
Table 4.1: Comparison of each ica gene positivity
Table 4.2: Comparison of ica gene combinations in isolates
Table 4.3: Summary of individual ica genes present in clinical vs. non‐clinical isolates
Table 4.4: Comparison of ica gene combinations in isolates from clinical and non‐clinical sources
Table 4.5: The presence of resistance genes in isolates from various sources
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ListofFigures
Figure 1.1: Pathogenesis of staphylococcal infections
Figure 1.2: Infections associated with indwelling devices
Figure 1.3: Identification scheme for CoNS
Figure 1.4: Process of biofilm formation
Figure 1.5: Procedure used for MLST to compare different genes and alleles
Figure 2.1: Basic principle involved in DNA extraction
Figure 3.1 (a): HRM analysis using Rotor‐Gene 6000 cycler software showing positive and
negative samples.
Figure 3.1 (b): HRM analysis using Rotor‐Gene 6000 cycler software showing only positive
samples (after deselecting negative ones).
Figure 3.2 (a): Normalized graph analysis showing a negative sample along with positive samples.
Figure 3.2 (b): Normalized graph analysis showing all positive samples only.
Figure 3.3: SceenClust data represented for each isolate tested.
Figure 3.4: Arrangement of data in cluster plot arrangement after Sceenclust analysis.
Figure 3.5 (a): Normalized graph analysis, comparing samples from different clusters.
Figure 3.5 (b): Difference graph analysis, comparing samples from different clusters.
Figure 3.6 (a): Normalized graph analysis, comparing two samples from same cluster and one
from a different cluster.
Figure 3.6 (b): Difference graph analysis, comparing two samples from same cluster and one
from a different cluster.
Figure 4.1: Electrophoresis of PCR products for IS256 and icaD genes
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Figure 4.2: A 2 % agarose gel showing the presence of the icaD gene in two S. epidermidis
isolates.
Figure 4.3: Normalised melt curve analysis comparing a positive control to two unknown
isolates.
Figure 4.4: Difference graph analysis comparing a positive control to two unknown isolates.
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ListofAbbreviations
CFU Colony Forming Unit
CNS Central Nervous System
CoNS Coagulase‐Negative Staphylococci
CSF Cerebro‐Spinal Fluid
CVC Central Venous Catheter
DNA Deoxyribonucleic Acid
IVIG Intravenous Immunoglobulins
MGEs Mobile Genetic Elements
MLST Multilocus Sequencing Typing
NA Nucleic Acid
NICU Neonatal Intensive Care Unit
NNIS National Nosocomial Infection Surveillance
PAMPs Pathogen Associated Molecular Patterns
PCR Polymerase Chain Reaction
PFGE Pulsed‐Field Gel Electrophoresis
PIA Polysaccharide Intracellular Adhesion
PJIs Prosthetic Joint Infections
PSA Polysaccharide Adhesion
PSMs Phenol‐Soluble Modulins
PVE Prosthetic Valve Endocarditis
SEM Scanning Electron Microscopy
SNPs Single‐Nucleotide Polymorphisms
STs Sequence Types
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ABSTRACT
Staphylococci are important pathogenic bacteria responsible for a range of diseases in humans.
The most frequently isolated microorganisms in a hospital microbiology laboratory are
staphylococci. The general classification of staphylococci divides them into two major groups;
Coagulase‐positive staphylococci (e.g. Staphylococcus aureus) and Coagulase‐negative
staphylococci (e.g. Staphylococcus epidermidis). Coagulase‐negative staphylococcal (CoNS)
isolates include a variety of species and many different strains but are often dominated by the
most important organism of this group, S. epidermidis. Currently, these organisms are regarded
as important pathogenic organisms causing infections related to prosthetic materials and
surgical wounds. A significant number of S. epidermidis isolates are also resistant to different
antimicrobial agents. Virulence factors in CoNS are not very clearly established and not well
documented. S. epidermidis is evolving as a resistant and powerful microbe related to
nosocomial infections because it has different properties which independently, and in
combination, make it a successful infectious agent, especially in the hospital environment. Such
characteristics include biofilm formation, drug resistance and the evolution of genetic variables.
The purpose of this project was to develop a novel SNP genotyping method to genotype S.
epidermidis strains originating from hospital patients and healthy individuals. High‐Resolution
Melt Analysis was used to assign binary typing profiles to both clinical and commensal strains
using a new bioinformatics approach. The presence of antibiotic resistance genes and biofilm
coding genes were also interrogated in these isolates.
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CHAPTER1
INTRODUCTION
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1.1INTRODUCTION
The staphylococci are important pathogenic bacteria responsible for a range of diseases in
humans. Hospital‐acquired infections and antibiotic‐resistant strains attributed to this group of
bacteria have become endemic in hospitals in many countries and are associated with serious
public health issues. Moreover, the emergence of new strains which cause severe community‐
acquired infections in immuno‐compromised patients, as well as in healthy people, and the
presence of multiple drug‐resistant strains in agricultural and domestic animals are also very
important from the pathogenic point of view.
The most frequently isolated microorganisms in a hospital microbiology laboratory are
staphylococci. The general classification of the staphylococci divides them into two major
groups; Coagulase‐positive staphylococci (e.g. Staphylococcus aureus) and Coagulase‐negative
staphylococci (e.g. Staphylococcus epidermidis). Coagulase‐negative staphylococcal (CoNS)
isolates include a variety of species and many different strains but are often dominated by the
most important organism (clinically) of this group, S. epidermidis. For a long time, these
microorganisms were regarded as non‐pathogenic for humans and their growth in laboratories
was attributed to contamination. Bondonaik (2006) reported that culture results showing
growth of S. aureus are regarded as an infection while CoNS isolation is not further investigated
because they are grown as a result of contamination and are mostly non‐pathogenic. Most
recently, these organisms are regarded as now important pathogenic organisms causing
infections related to prosthetic materials and surgical wounds.
Clinically CoNS are emerging as an important group of pathogenic staphylococci. Various strains
and species of CoNS are commonly found on the skin and mucous membranes of humans and
other animals as a part of the normal flora. Furthermore, CoNS can be divided into two groups
depending on whether they are resistant or susceptible to novobiocin. S. saprophyticus is the
most commonly isolated bacterium in the novobiocin‐resistant group while S. epidermidis is the
most frequently isolated species of the novobiocin‐susceptible group which usually infects
immuno‐compromised patients (von Eiff et al., 2002). Most common victims of S. epidermidis
infection are premature newborns, patients with leukaemia or other malignant diseases,
intravenous drug abusers and patients with indwelling polymer bodies, such as prosthetic
devices or intravenous catheters ( Cosgrove and Carmeli, 2003; Otto, 2009; Foster, 2009).
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A significant number of S. epidermidis isolates are also resistant to different antimicrobial
including erythromycin, clindamycin and gentamicin. Vancomycin is the drug of choice for
nosocomial S. epidermidis infections, at least at the initiation of therapy (Foster, 2009). This
literature review will discuss CoNS and their pathogenesis, in general, and S. epidermidis, in
particular.
The epidemiology of S. epidermidis is not well known. Most studies have utilized Pulsed‐Field Gel
Electrophoresis (PFGE) analysis, a costly and time‐consuming technique that is reported to have
problems with reproducibility (Blanc et al., 2001). Other gel based techniques such as amplified
polymorphic DNA analysis, multilocus variable number of tandem repeat analysis and amplified
fragment length polymorphism analysis have the same problem with reproducibility of results.
Comparatively, Polymerase Chain Reaction (PCR) and Multilocus Sequence Typing (MLST)
analysis, which are Deoxyribonucleic Acid (DNA)‐based sequencing methods, are highly
reproducible. Single Nucleotide Polymorphism (SNP) typing is a new technique which is derived
from the MLST database and is used for a number of bacteria, viruses and other genetic based
studies.
Studies to generate information on S. epidermidis are very important because of its emergence
as a significant pathogenic organism, especially in relation to indwelling devices. Genomic
techniques to identify strains of S. epidermidis and other CoNS are not well developed. The
virulence factors of these bacteria are also not well documented. This research project is aimed
at developing techniques for the robust and rapid characterization of CoNS strains, their
antibiotic resistance profiles and characterization of the ica operon. Intercellular adhesion (ica)
genes have a significant role in the pathogenic nature of S. epidermidis as these genes are
involved in production and expression of various characteristics like biofilm, Polysaccharide
Intracellular Adhesion (PIA) and autolysins. In the current study, the application of a SNP
genotyping method to characterize CoNS strains will be investigated.
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1.2HYPOTHESIS,AIMSANDOBJECTIVES
Hypothesis
That a SNP genotyping method can be used to differentiate S. epidermidis strains originating
from clinical specimens and healthy individuals.
Aim 1
Development of a novel S. epidermidis SNP genotyping method using the “Minimum SNPs”
software program.
Objective 1
Design Single Nucleotide Polymorphism (SNP) primers for S. epidermidis genotyping using Real‐
Time PCR and High Resolution Melt Analysis.
Aim 2
Characterization of S. epidermidis strains isolated from clinical and non‐clinical sources.
Objective 2
To apply SNP genotyping to S. epidermidis strains isolated from patient cultures, skin and nasal
swabs from healthy individuals.
Aim 3
To determine virulence factors, including the presence of adherence/biofilm forming factors and
the antibiotic resistance profiles of S. epidermidis strains from patients and healthy individuals.
Objective 3
To apply PCR for the detection of biofilm genes and Real‐Time PCR for the detection of antibiotic
resistance genes in S. epidermidis strains isolated from patients and healthy individuals.
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1.3CRITICALANALYSISOFTHELITERATURE
1.3.1GeneraloverviewofStaphylococci
Staphylococci are Gram‐positive round (cocci) bacteria, measuring about 0.5‐1 μm in diameter
and are arranged as grape‐like clusters because of their division across two planes. This
characteristic is important in distinguishing staphylococci from streptococci which are also
Gram‐positive cocci but divide only in one plane and are therefore arranged in the form of long
chains (Foster, 2009). Staphylococci are widely distributed in nature and are found as part of the
normal microflora of humans and other animals, air, soil, water and processed food products
such as cheese and sausages (Kloos et al., 1992). Although staphylococci are found widely
distributed in the environment, few species are dominant in specific areas. This genus contains
40 species, with more than 20 subspecies (Mellmann et al., 2006). The number is increasing as
our knowledge of this genus increases. Structural characteristics of these Gram‐positive cocci
make them capable of tolerating heat, dryness, dehydration and low water activity. This
tolerance allows them to have widespread distribution in the environment (Kloos et al., 1992).
Staphylococci can be broadly divided into two groups; Coagulase‐positive and Coagulase‐
negative staphylococci. All staphylococci are Coagulase‐negative except S. aureus and S.
intermedius, which are Coagulase‐positive. S. aureus is found in the nasal passages and axillae
while S. epidermidis, which is the most important member of the CoNS, is a common human skin
commensal (Foster, 2009; Kloos et al., 1992). S. aureus is a recognized pathogen while CoNS are
usually responsible for sub‐clinical infections, which means that these infections lack typical
signs and symptoms of disease (Von Eiff et al., 2002). Oliveira et al. (2003) has mentioned that S.
aureus carry more and effective virulence factors and is a potential threat for infections in
various clinical settings while CoNS usually need an opportunity such as catheter‐aided entry
into the body to be infectious.
Other species of staphylococci are infrequent human commensals. S. aureus has a number of
virulence factors including surface proteins promoting colonization, factors inhibiting
phagocytosis (capsule, Immunoglobulin binding protein A etc.) and many toxins which cause
damage to host tissues. Comparatively, CoNS express fewer virulence factors, and hence are less
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virulent. S. aureus and mostly CoNS (especially S. epidermidis) readily grow on implanted devices
(Foster, 2009).
Figures 1.1 and 1.2 show various infections caused by staphylococci associated with indwelling
devices, upon which these organisms can grow and cause infections.
Figure 1.1: Pathogenesis of staphylococcal infections (Adapted from Foster, 2009).
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Figure 1.2: Infections associated with indwelling devices (Adapted from Foster, 2009).
1.3.2Coagulase‐NegativeStaphylococci(CoNS)
Coagulase is a surface protein which has the ability to convert fibrinogen to fibrin, thus resulting
in clot formation in plasma, which is the basis of the tube coagulase test. Staphylococci that lack
this protein do not form a clot in the coagulase test and are called Coagulase‐negative
staphylococci (Foster, 2009). In other words, CoNS are differentiated from the closely related
but more virulent S. aureus by their inability to produce free coagulase. Currently, there are
more than 40 recognized species of CoNS (Rogers et al., 2009).
Amongst CoNS, S. epidermidis is the major cause of infections associated with indwelling
devices. In general, CoNS are also responsible for peritonitis in patients receiving continuous
ambulatory peritoneal dialysis and endocarditis (prosthetic valves). Other species such as S.
haemolyticus, S. warneri, S. hominis, S. capitis, S. intermedius, S. schleiferi and S. simulans rarely
cause infections in humans. S. lugdunesis is a newly recognized species. As CoNS have fewer
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virulence factors compared to S. aureus, infections are mostly indolent and chronic with few
obvious symptoms (Foster, 2009).
CoNS, as part of the microflora of the skin, generally have a benign relationship with their host
(Kloos and Bannerman, 1994). CoNS are normally present on human skin and mucous
membranes and generally do not cause infections (Weinstein et al., 1990). They act as
commensals or saprophytic organisms but in cases of damage to the skin such as by trauma,
needles or implantation of foreign bodies, these organisms can gain entry to the host’s internal
system (Kloos and Bannerman, 1994). CoNS require breaching epithelial barriers and access to
cause infections (Kocianova et al., 2005) and Intravascular Medical Devices (IMDs) allow them to
gain entry across body protective layers. Once CoNS reach through the epithelial protective
layer, they can be highly infectious.
CoNS have the ability to adhere to host or foreign body surfaces. Moreover, they are capable of
avoiding the host immune system and produce factors which can cause damage to the host.
Such characteristics enable CoNS to present as pathogens (Kloos and Bannerman, 1994). CoNS
are among the most frequently isolated bacteria in the laboratory and are becoming increasingly
important infectious agents of hospital‐acquired bacteraemia, mainly because of the increasing
use of different prosthetic devices and other invasive technologies in medical institutions (von
Eiff et al., 2002).
1.3.3Pathogenesis(CoNS)
Virulence factors in CoNS are not very clearly established and documented but many such
factors are already known for S. aureus. Compared to S. aureus, no major virulence factors or
toxins have been found in CoNS and it is clear that development and persistence of CoNS
infections must be due to alternative mechanisms (Huebner and Goldmann, 1999). The following
are some of the proposed structural components involved in CoNS virulence.
PlasmidsandTransposons
Plasmids are involved in the spread of antibiotic resistance determinants among staphylococci
including CoNS (Forbes and Schaberg, 1983; Malachowa and Deleo, 2010). For example, Mobile
Genetic Elements (MGEs) which encode methicillin resistance are frequently transferred from S.
epidermidis to S. aureus (Hanssen et al., 2004).
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SurfaceProteins
Certain structural components are thought to be involved in the adherence process. Veenstra et
al. (1996) reported that a fimbria‐like protein is responsible for this attachment. Other
investigators have suggested that a 140‐kD extra‐cellular protein is an important adherence tool
of S. epidermidis (Hussain et al., 1997). According to Rupp and Archer (1992) an uncharacterized
heam‐agglutinin is also involved in adherence to polymer surfaces. Rohde et al. (2006) described
Autolysin (AtlE) in attachment to various surfaces.
CapsularPolysaccharides
Capsular polysaccharides have a major role as virulence factors but little is known about their
chemical nature and specific roles. Bayston and Penny (1972) were the first to propose the role
of polysaccharides in the pathogenesis of S. epidermidis infecting Central Nervous System (CNS)
shunts when they observed a substance they named as “slime” which could be stained by dyes
specific for polysaccharides. Later, other studies confirmed their observations. Tojo et al. (1988)
characterized a specific polysaccharide which was named capsular Polysaccharide Adhesion
(PSA) due to its involvement in adhesion. In another investigation, polysaccharide intracellular
adhesion (PIA) was identified, which is a linear homo‐polymer of B‐1,6‐linked glucosamine (Mack
et al., 1996). They also cloned and sequenced five intercellular adhesion (ica) genes (icaA, icaB,
and icaC, icaD, and icaR) involved in the production of this polysaccharide. The exact role of the
different structural components of CoNS virulence factors is still poorly understood.
It is essential to determine the structural components of CoNS virulence factors and their
mechanisms in order to gain an understanding of the pathogenesis of CoNS. It is also important
to understand the mechanisms that CoNS employ to escape recognition by the host immune
system. Host innate immune system recognises Pathogen Associated Molecular Patterns
(PAMPs) on bacterial surfaces as foreign particles and reacts to remove such bacteria. S.
epidermidis and S. aureus produce Phenol‐Soluble Modulins (PSMs), which assists the organism
to evade the host immune system (Otto, 2009).
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In addition, a better understanding of the genetic components involved in the development of
antimicrobial resistance in CoNS will assist in the development of effective therapy of infections
caused by CoNS (Rohde et al., 2006; Conlon et al., 2002).
1.3.4Staphylococcusepidermidis
S. epidermidis is the dominant and most frequently isolated species among CoNS. A study
focusing on the analysis of S. epidermidis strains in the nares of healthy adults showed that there
are multiple strains in each individual (Hu et al., 1995). Kloos and Musselwhite (1975) reported
ten to twenty‐four different strains of S. epidermidis residing on human skin as part of normal
flora. S. epidermidis is a commensal microorganism on the human skin but is also recognized as
an important opportunistic pathogen especially causing infections of indwelling devices (CDC,
2004). Catheter‐related diseases, hospital‐acquired infections and cases of bacteraemia are
mostly linked to S. epidermidis (Mohammad et al., 2011).
Genomic studies have shown that S. epidermidis has the ability to survive under the harsh
conditions of its natural habitat, the skin. For example, it contains eight sodium ion/proton
exchangers and six transport systems for osmo‐protection (Zhang et al., 2003; Gill et al., 2005).
Other than S. epidermidis, other CoNS species and strains are usually less virulent and are
difficult to identify as different species. Moreover with the exception of S. lugdunensis, other
CoNS species are mostly non‐pathogenic in humans (Osmon et al., 2000).
1.3.4.1Identification
Traditional methods such as biotyping, antibiotic resistance profiling and plasmid analysis have
limited discrimination and reproducibility. A number of tests such as biochemical tests,
chromatography, genotyping, ribotyping etc. are used to discover and classify new species and
strains. Increasing the number of tests used to analyse S. epidermidis is beneficial as it would
result in further differentiation and identification of various strains and species (Becker et al.,
2004; Carretto et al., 2005). Molecular techniques such as PCR combined with phenotyping is
more informative compared to conventional phenotypic tests alone (O'Gara and Humphreys,
2001). Palka‐Santini et al. (2007) found that biochemical, immunological and enzymatic
methods, such as the coagulase test, are not very accurate as various strains have varying
biochemical and immunologic characteristics. They used a PCR‐based assay which proved much
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better than routine testing because it not only reduced the time to get results but also was able
to detect other properties such as virulence components and antibiotic‐ resistant encoding
genetic elements simultaneously. This technique was able to not only differentiate S. aureus
from Gram‐negative bacteria but also successfully separated them from CoNS.
Kloos and Schleifer (1975) determined the natural relationships of CoNS based on systematic
studies to identify different CoNS species. Species were identified based on morphological,
physiological and biochemical characteristics, antibiotic susceptibility patterns, and cell wall
composition but this method was not very specific as it was designed to identify all known CoNS
(i.e. clinical, veterinary, and alimentary isolates) and was too time‐consuming to be used in
routine diagnostic laboratories. De Paulis et al. (2003) developed a scheme to identify CoNS
species or species groups (i.e. a group approach) that can be used by most clinical laboratories.
This five‐test simple scheme of identification concentrates on the species or species groups as
follows:
1. The S. epidermidis group (S. epidermidis, S. capitis subsp. ureolyticus, and S. caprae)
2. The S. haemolyticus group (S. haemolyticus, S. auricularis and S. casseolyticus)
3. The S. saprophyticus group (S. saprophyticus subsp. saprophyticus, and S. hominis subsp.
novobiosepticus)
4. The S. warneri group (S. warneri and S. hominis subsp. hominis)
5. The S.cohnii group (S. xylosus, and S. cohnii subsp. urealyticum, S. lugdunensis, S. schleiferi
subsp. schleiferi, S. capitis subsp. capitis, S. simulans and S. cohnii subsp. cohnii)
Biochemical reactions were used as the basis for species identification rather than their
phylogenetic relationships (De Paulis et al., 2003). It was possible to identify species by
performing one or two additional tests. A comparative study of two commercial identification
methods, Staph‐Zym (Rosco, Taastrup, Denmark) and API‐Staph (bioMe´rieux, Marcy l’E´ toile,
France), was done with the results obtained by using Rosco diagnostic tablets (non‐growth tests)
together with the results of the reference method of Kloos and Schleifer (1975). A simplified
scheme for CoNS identification is given in Figure 1.3.
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Coagulase
Positive Negative
(eg. S. aureus)
Xylose and/or arabinose
Positive (weakly) Negative
(eg. S. xylosus )
Sucrose
Negative Positive
(eg. S. schleiferi, S. caprae etc.)
Trehalose
Positive Negative
(eg. S. haemolyticus, S. saprophyticus, S. hominis etc.)
Maltose
Positive (weakly) Positive
(eg. S. simulans)
Mannitol
35
Mannitol (Continued from last page)
Positive Negative
(eg. S. capitis)
Anaerobic growth on thioglycate
Positive Negative
(S. epidermidis) (eg. S. hominis subsp. hominis etc.)
Figure 1.3: Identification scheme for CoNS (Simplified from Cunha et al., 2004).
36
1.3.4.2PathogenesisofS.epidermidis
Hospital‐acquiredinfections
S. aureus and S. epidermidis are mostly involved in nosocomial infections, particularly in those
patients who have invasive procedures such as use of indwelling devices and implanted objects
(von Eiff et al., 2002). S. epidermidis is evolving as a resistant and powerful microbe related to
nosocomial infections because it has different properties which independently and in
combination make it a successful infectious agent, especially in the hospital environment. Such
characteristics include biofilm formation, drug resistance and the evolution of genetic variables
(Ziebuhr et al., 2006). Choong and Whitfield (2000) mention a few factors that increase the
chances of acquiring these infections. Such factors include the duration of surgery and hospital
stay, the number of invasive procedures, infectious sources within the body, amongst others.
Other elements such as the increased use of Intravascular Medical Devices (IMDs) and prolonged
survival of immuno‐deficient patients and those with terminal illnesses have also increased the
number of S. epidermidis infections (Otto, 2009).
S. epidermidis behaves as an opportunistic bacterium and is frequently involved in infections in
patients with impaired immunity (Spare, 2003). The current use of immunosuppressive drugs,
for example in organ transplant patients, is an important contributor to the increase in infections
caused by S. epidermidis, particularly with the increasing practice of transplant surgery.
Bacteraemia
The importance of bacteraemia caused by CoNS was recognized in a report on nosocomial
bacteraemias by Raad and Bodey (1992). They reported the occurrence of 50,000‐120,000 CoNS
bacteremia cases per year in the United States alone. In a statistical report by the National
Nosocomial Infections Surveillance (NNIS) program, CoNS constituted 8 % of all nosocomial
infections (Banerjee et al., 1991). It is true that these numbers are significant but one fact should
be kept in mind that CoNS are frequent contaminants in laboratory cultures so it is difficult to
decide whether growth is the result of a contamination or infection (Huebner and Goldmann,
1999). Frequent and regular use of invasive devices such as intravenous lines is usually
associated with infection which is followed by sepsis. Sepsis often causes high morbidity and
mortality (Loonen et al., 2009).
Mostly, bacteraemia caused by CoNS is not life‐threatening, especially if prompt treatment is
given but on occasions systemic sepsis syndrome also occurs, mainly in those patients who have
37
underlying complications or suppressed immune systems (i.e. immune‐compromised patients).
CoNS are also involved in exit‐site infections, thrombophlebitis, infective endocarditis and
abscesses (Henderson, 1988).
Catheter‐RelatedInfections
Staphylococci are the most common infectious organisms involved in indwelling device‐related
infections and are responsible for severe complications (Toba et al., 2011). CoNS are an
important cause of bacteraemia related to catheters. S. epidermidis is responsible for 50‐70 % of
such infections (Archer, 1995). Rupp (2004) reported that CoNS are responsible for about 30 to
40 % of infections related to invasive devices. The most important and common infection caused
by S.epidermidis is the colonization and growth upon medical devices (Vuong and Otto, 2002).
Miragaia et al. (2008) also reported S.epidermidis as the most important infectious agent for this
type of infection. Even in catheter‐associated infections of the urinary tract, staphylococci are
the commonest organisms identified (Gad et al., 2009).
The use of catheters is increasing dramatically. In the United States alone, about 180 million
peripheral and seven million central devices are used per annum (Hanna, 2005). About two
hundred and fifty thousand catheter related septicemia cases are diagnosed annually in the
United States with 1‐2 % deaths, increased cost and hospital stay (O’Grady et al., 2002; Raad et
al., 2007). Antibiotics should not be used in cases where growth of the bacteria occurs from
samples taken from catheter surfaces alone. Unless there are signs and symptoms indicative of
septicemia along with positive blood culture, growth from catheter tips is merely indicative of
surface colonization (Safdar et al., 2005).
CentralNervousSystemShuntInfections
The predominant bacteria isolated from Cerebro‐Spinal Fluid (CSF) shunt infections are CoNS (48
% to 67 % of isolates), and the main species isolated is S. epidermidis. This infection is probably
introduced during the insertion of these devices or subsequent manipulations (Roos and Scheld,
1997). Factors which will increase the chances of Central Venous Catheter (CVC) infections
include an age of less than six months, surgical skills and duration of surgery. Once infection is
suspected, CSF culturing is required or culturing of catheter surfaces after their removal (Boyce,
2004). Removal of the shunt may be necessary with replacement after a few days following use
of antibiotics or immediate replacement under antibiotic cover. Clinical manifestations are often
not specific or diagnostic such as fever (+/‐ rigors), nausea, vomiting, and lethargy. Gram‐
staining and culture of CSF is diagnostic. Intraventricular vancomycin (+/‐ gentamicin), plus
38
systemic administration of vancomycin (+/‐ rifampicin) is required for treatment (Frame and
McLaurin, 1984). Rarely, immune complex deposits in kidneys cause glomerulonephritis also
known as shunt nephritis (Huebner and Goldmann, 1999). Schreffler et al. (2002) recommend
that CNS shunt removal and antibiotic therapy is required for CNS shunt infections and more
than 50 % of such infections are caused by CoNS.
SurgicalSiteInfections
Although factors such as age, nutrition, weight, duration of surgery and surgery skills contribute
to wound infections, which are mostly superficial, the main source of organisms is from the
patient rather than staff and surroundings (Wong, 2004). Two factors are related to CoNS
infection of surgical sites; if the cut is not very deep and if the procedure did not have much of a
contamination risk. In other words, it can be said that organs such as the bowels are less at risk
of contracting a CoNS infection after procedures compared to surgeries performed on relatively
cleaner areas such as skin (Boyce, 2004). CoNS can also infect prosthetic vascular grafts, which
usually occur within 30 days of graft insertion, and this occurrence of infection is not related to
the material used for grafts. Infection rates and complications depend on sites of insertion, for
example aortic graft infections have a higher mortality rate (O`Brien and Collin, 1992; Rogers et
al., 2009).
Prostheticmaterialrelatedinfections
S. epidermidis is also involved in prosthetic joints, vascular graft, surgical wounds, CNS shunt and
cardiac device infections (Rogers et al., 2009). Kobayashi et al. (2006) described staphylococci as
one of the commonest microorganisms involved in orthopedic infections. Notably, S. epidermidis
causes about 13 % of Prosthetic Valve Endocarditis (PVE) infections, with a high rate of
intracardiac abscesses (38 %) and mortality (24 %) (Chu et al., 2009). However, compared to S.
aureus, PVE and other complications are rare among S. epidermidis infections, which are usually
sub‐acute and chronic (Otto, 2009). Hellmark et al. (2009) found S. epidermidis as the most
important pathogen in infections related to indwelling devices, especially Prosthetic Joint
Infections (PJIs). Boyce (2004) reported that patients who had joint surgery, collateral pre‐
existing infections and other pathologies such as rheumatoid arthritis, were more prone to
developing infections. Furthermore, joint infections can be divided into three stages depending
on the time between surgery and infection diagnosis. For prosthetic joint infections, various
procedures such as joint replacement, resectional arthroplasty and simple debridement can be
performed, with different indications, merits and demerits (Kathie et al., 2009).
39
NeonatalIntensiveCareUnit(NICU)
As CoNS are nosocomial pathogens, they have particular significance in the Neonatal Intensive
Care Unit (NICU) infections. There are certain risk factors, in addition to the context of intensive
care, such as very low birth weight, prematurity, and immaturity of immunological defenses.
Impaired ability to opsonize and kill staphylococci is also present in neonates. Along with these
factors, the current use of, and requirement for, catheters and vascular intervention in intensive
care units also pose an important and additional risk factor (Maas et al., 1998).
Bacteria originate from the neonatal ICU environment and also from tissues such as skin,
mucous membrane in the nares and the umbilicus for example, and become reservoirs for
microbes as they grow and proliferate in these areas (Klingenberg et al., 2007). Factors such as
preterm and low birth weight, Total Parenteral Nutrition (TPN), umbilical lines and risk carrying
pregnancies are believed to be involved in more and more reported cases of neonatal infections
caused by CoNS. Neonates might require antimicrobial therapy as well as device removal, if
these are used (von Eiff et al., 2005; Benzies, 2008).
Cardiacinfections
Archer and Tenenbaum (1980) reported that S. epidermidis is also involved in post‐operative
infections in patients undergoing cardiac surgery and many isolates of S. epidermidis causing
these infections exhibit multiple antibiotic resistances. CoNS are present in 15 to 40 % of
Prosthetic Valve Endocarditis (PVE) cases, mostly involving S. epidermidis. Moreover, a large
number of patients have complications such as heart failure, intracardiac abscesses and
malfunctioning of artificial valves (Lalani et al., 2006; Wang et al., 2007). Endocarditis (Prosthetic
Valve Endocarditis‐PVE) is usually caused by methicillin‐resistant organisms because these
organisms originate from the hospital environment and are diagnosed by blood cultures and
other cardiac system studies. Such infections are usually associated with significant death rates
(Archer and Climo, 2005).
Archer and Tenenbaum (1980) compared methicillin resistance in isolates from chest wounds of
patients undergoing cardiac surgery, to isolates from the chest skin of the same patients before
surgery. Results of this study showed that 54 % of S. epidermidis isolates from chest wounds
were methicillin‐resistant compared to only 4 % of isolates from the chest skin of patients before
surgery. Another study by Bentley et al. (1973) also showed similar results; methicillin‐resistant
S. epidermidis strains were isolated from cardiac surgery patients receiving methicillin, but these
organisms were absent in the same patient before treatment with methicillin, which indicates
40
the ability of these organisms to acquire resistance to the agents used against them.
Interestingly, ten of 118 chronic‐care patients receiving no or few antibiotics harbored
methicillin‐resistant S. epidermidis. Cardiac pacemaker infections also have many complications
and need combination therapy with multiple intravenous drug therapy along with removal of
the pacemaker (Gandelman et al., 2007).
Mastistis
S. aureus is regarded as an important infectious organism in mastitis but recent studies have
suggested the involvement of CoNS (especially S. epidermidis), which have a significant role in
such infections. A study by Delgado et al. (2009) compared culture results of milk from 30
women. Upon culturing, 27 of these grew bacteria. S. epidermidis was isolated from 26 and S.
aureus from 8 patients. In total, 270 staphylococcal isolates were recovered from the milk of
women with mastitis, among these 200 were identified as S. epidermidis using phenotypic
assays, species‐specific PCR and DNA sequencing. Genotyping was performed by PFGE. The PFGE
profiles of the S. epidermidis strains were compared to 105 isolates from the milk of healthy
women. 76 of these isolates were studied for virulence factors and antibiotic resistance. Results
showed that strains that have the biofilm‐related icaD gene, showed resistance to oxacillin,
erythromycin, clindamycin and mupirocin and these strains were significantly more prevalent
among the isolates from mastitis milk. Different views and results have been presented
regarding the source, mode of transmission and pathogenicity. For example, CoNS have been
reported as the most commonly isolated pathogens in bovine mastitis by Pyorala and Taponen
(2009) but Schukken et al. (2009) did not account CoNS as significant for the same condition.
Endophthalmitis
S. epidermidis also causes endophthalmitis. In fact, it is the most common cause for this
condition following penetrating injuries to the eye and the risk increases up to 15 % if foreign
material remains at the injury site (Duch‐Samper et al., 1997). Preservation of vision doesn’t
have a good prognosis in such cases. Following vitrectomy, S. epidermidis is again the most
important pathogen causing endophthalmitis and the source of bacteria is the patient`s skin
flora (Bannermann et al., 1997)
Haematologicalmalignancies
CoNS are the most common organisms associated with bacteraemia in patients with
malignancies and the main species involved is S. epidermidis. S. hominis was also isolated but
was found to be a result of contamination. Similarly, in a few cases, S. epidermidis grew due to
41
contamination. So it is very important, from the diagnostic point of view, to be careful to avoid
contamination because the laboratory cannot differentiate growth source whether it is from
bacteraemia or contamination (Persson et al., 2006)
1.3.4.3Virulence
S. epidermidis has certain adaptations such as the down‐regulation of certain processes such as
Nucleic Acid (NA), proteins and cell wall biosynthesis and the formation of a biofilm. This process
may be involved in protecting bacteria from antibiotics which attack actively growing cells, for
example, aminogylocides, penicillins and quinolones (Otto, 2009). Similarly, Donlan and
Costerton (2002) found that the slow growth of bacteria in a stationary phase reduces
metabolic activity of those antibiotics which kill metabolically active cells (growth‐dependent
antibiotics, e.g. cell‐wall‐active antibiotics). Our innate immune system is the first line of
defense, acting in a non‐specific way. S. epidermidis has the ability to evade ingestion and killing
by neutrophils. Our acquired immune response, which produces specific and unique defense
against different organisms, is not well understood against S.epidermidis. As it is difficult to clear
S. epidermidis infections despite the presence of antibodies against it, it indicates that the
acquired host defense system is ineffective against S. epidermidis. This may be due to S.
epidermidis exopolymers that protect the cells from antibody recognition and lysis. Also, the
immune system might be less active against a prevalent colonizing bacteria (Otto, 2009).
1.3.4.4Adhesion
Gristina (1987) subdivided the process of infection into the stages of attachment, adhesion, and
aggregation. Electron microscopic studies by Peters et al. (1982) have shown that bacteria after
attaching, form colonies and produce a biofilm. Those strains of S. epidermidis which lack the
ability of adherence or cluster formation, are less virulent (Rupp et al., 2001). Adhesion is the
first step in the development of infection and various studies have shown different results
regarding the mechanisms and structural components involved in this process.
Bacterial cell surface hydrophobicity is involved in adhesion to abiotic surfaces such as catheters
(Vacheethasanee et al., 1998). Specific proteins which affect surface adhesion in S. epidermidis,
are the protein Autolysin (AtlE), a biofunctional adhesion and autolysin (Heilmann et al., 1997)
and the Bap protein (Tormo et al., 2005). These proteins are likely to contribute to the
42
hydrophobic nature of the cell surface. Rupp et al. (2001) used a rat Central Venous Catheter
(CVC) infection model to assess the importance of the proteinacious Autolysin (AtlE) and the PIA
in the pathogenesis of S. epidermidis. CVC‐associated infection analysis revealed the importance
of initial adherence (associated with AtlE) and biofilm production (mediated by PIA) in the
pathogenesis of S. epidermidis. Biochemical components of PIA are responsible for the presence
of both positive and negative charges at the same time. These polysaccharides do not have
branching patterns which aids in the formation of effective cell to cell bonding and biofilm
development (Mack et al., 1996). The major Autolysin (AtlE) is also thought to play a dual role in
adherence of S. epidermidis (Rohde et al., 2006) as it is active in attachment to both
unconditioned and conditioned polymer surfaces. The high resistance of S. epidermidis biofilms
to antibiotics is due to the densely adherent growth mode, rather than the build‐up of the
extracellular polymer substance matrix (Qu et al., 2010).
Sousa et al. (2009) studied the ability of eight strains of S. epidermidis to adhere to acrylic and
to silicone surfaces, the two materials commonly used in the manufacture of different medical
devices. Under controlled growth conditions, total cell count of adherent bacteria was done
using different techniques including Scanning Electron Microscopy (SEM). The sessile drop
contact angle technique (Busscher et al., 1984) was used to study hydrophobicity parameters of
substrata and bacterial surfaces. Nearly all S. epidermidis strains adhered better to the silicone
substrate than to acrylic, though a few strains showed better adherence to silicone. Roughness
of acrylic and silicone surfaces was also studied. Results showed that the hydrophobic nature of
the biomaterial surface has a significant role in initial adhesion, as increased adherence was
observed for the hydrophobic silicone substrate than to the less hydrophobic acrylic material.
The increased roughness of silicone also affects bacterial adhesion. On the other hand, bacterial
surface physicochemical properties seem to have less effect on binding, highlighting the
importance of other cell surface factors in the adhesion process. Hussain et al. (1997) also
mentioned another protein component, the Accumulation Associated Protein (Aap), which has
some role in the adhesion process.
1.3.4.5Biofilms
Biofilms play a vital role in pathogenesis and plays a significant part in morbidity and mortality
(Rohde et al., 2006). Initial adhesion and aggregation of microbes onto multiple layers is
followed by biofilm formation. There is a view that before the actual process of biofilm
43
development and formation, the surfaces of indwelling devices are “conditioned” in vivo.
Different components present in body secretions such as saliva, mucus, urine form a coat by
adsorbing o the surfaces of devices to form a conditioning layer or film upon which actual
bacterial growth and biofilm formation occurs (Choong and Whitfield, 2000). This conditioning
film acts as an attaching surface for bacteria (Fitzpatrick et al., 2005). Another view is that this
primary adhesion is dependent on the chemistry of the material used for implanted devices,
which also affects other processes including biofilm formation and its thickness (Patel et al.,
2007). Dunne (2002) has described other factors such as electrostatic and hydrophobic
interactions, Van der Walls forces and temperature that also impact on biofilm formation.
PIA synthesis needs the presence of the ica operon (intercellular adhesion) because the enzymes
which are involved in its synthesis are controlled by the intercellular adhesion operon (icaADBC)
(Heilmann et al., 1996; Stevens et al., 2008). Although the association of a biofilm in causing
such infections has been established, there remains some ambiguity regarding the exact
mechanism. Using the electron microscope, it has been observed that there is deposition of host
secretions on the device followed by growth of the bacteria upon it. Once the biofilm is formed,
migration of bacteria along the device itself and its systemic spread speeds up. Overall, the
migration process of S. aureus is quicker than S. epidermidis. Although strains that produce
biofilm are more infective, ica mutation does not have much impact on bacterial ability to cause
infections. Biofilm is a very useful and powerful factor contributing to device related infections
of staphylococci (Toba et al., 2011). Stevens et al. (2009) studied various protein components
and the PIA of biofilm in S. epidermidis and have suggested that PIA has a primary role in biofilm
positivity while protein components contribute significantly towards the maturation of the
biofilm. They also stated that biofilm formation makes S. epidermidis an important pathogen
which causes meningitis related to various devices. Stevens et al. (2009) also mentioned that
the formation of biofilm occurs by the participation of components arising from sources, the
host and the bacteria. In addition, biofilm formation can have different mechanisms such as ica‐
dependent and protein‐dependent formation. An explanation of how biofilm formation is
occurring is that other components such as accumulation associated protein (Aap) are not
activated if ica is present and the formation of biofilm is controlled by the ica operon but when
ica is not active, other mechanisms of biofilm formation start operating.
Biofilm development therefore requires adhesive forces for both the colonization of surfaces
and cell to cell interactions. Also, disruptive forces are required for the formation of channels
(fluid‐filled), which are important for nutrient delivery across all biofilm cells. The same
44
disruptive forces cause detachment of clusters of cells from biofilms and might be a mechanism
for the spread of bacteria and cause disseminated infection (O’Toole et al., 2000). The
extracellular PSA is a significant virulence determinant and is required for biofilm formation and
adhesion, which is encoded by the ica operon, and is subjected to phase variable regulation, and
on and off switching mechanism (O'Gara and Humphreys, 2001).
Stepanovi et al. (2000) described the development of a simple and efficient method for the
quantification of biofilm formation based upon the standard microtiter‐plate test, where
glucose enhances adherence, which agrees with the study by Christensen et al. (1985) and
Mulder and Degener (1998). However this effect is not dependent on glucose concentration.
Kong et al. (2006) have described a process of Quorum Sensing (QS) which states that there is a
presence of cell‐to‐cell signaling for biofilm formation which makes it structurally a very
organized and effective defense.
Biofilm modifications are important in providing a safe environment for bacteria. Examples of
such modifications are: (i) a limited access of harmful molecules such as antibiotics and immune
system products to bacteria, (ii) lower levels of inflammation which reduce chemotaxis of
defense cells to the area of infection and; (iii) fermentation as an energy source rather than
aerobic processes and other metabolic changes (Yao et al., 2005). Slime production adds
resistance to antimicrobials (Mohammad et al., 2011). McCann et al. (2008) have described the
role of specific bacterial surface proteins such as PIA, Aap, Bap, AtlE involved in binding and
attachment and the formation and function of the biofilm. If bacterial cells are exposed to
certain substances or factors which are harmful to them or aid in growth, such as salt, nitrates,
oxygen and antibiotics, then the production of PIA and biofilm development processes are
increased (Schlag et al., 2007).
Otto (2009) described the process of biofilm formation, which is shown in Figure 1.4. This figure
shows the maturation of cell growth after initial attachment and detachment of bacteria from
these initial foci of growth.
45
Figure 1.4: Process of biofilm formation (Adapted from Otto, 2009).
1.3.5AntibioticresistanceinS.epidermidis
The emergence of multiple drug resistant strains of S. epidermidis is causing treatment
problems. S. epidermidis in stationary growth phase avoids antibiotic effects when growing on
surfaces of implants. Biofilm production also plays an additional role in avoiding toxic effects
(Donlan, 2000). Otto (2004) attributed increasing resistance to frequent antimicrobial use, use of
broad spectrum antibiotics, diagnostic and prescription mistakes, patient`s compliance and
extensive use of drugs for livestock and agricultural purposes. Complicating factors such as
overstay in hospital, slow recovery, less options available for treatment for example, are directly
related to the presence of more immuno‐compromised patients, more invasive procedures and
the increased use of microbial control procedures (Cosgrove and Carmeli, 2003).
Mohammad et al. (2011) investigated the presence of resistant strains in the nasal mucosa of
medical staff to understand the cycle of hospital related infections. They tested 99 S. epidermidis
isolates for slime production (34.3 % positive). Out of these 94 % were resistant to penicillin, 88
% resistant to tetracycline, 79 % resistant to erythromycin and 77 % were resistant to
clindamycin. Overall, they found that 66.7 % of S. epidermidis strains resistant to multiple
antibiotics. Mack et al. (2005) reported the presence of methicillin (oxacillin) resistance in 90 %
of staphylococcal isolates obtained from the hospital environment. The reason for the presence
of increased resistance in isolates from hospital settings is significant and attributable to the
regular use of antibacterial agents for treatment and disinfection (McCann et al., 2008). S.
46
epidermidis strains are capable of acquiring and developing resistance to potent drugs used
against it, for example, mupirocin is used in the form of a nasal ointment in health professionals
and Mohammad et al. (2011) found 5.1 % of S. epidermidis isolates resistant to mupirocin. This
shows a strong association between the presence of a slime layer and antibiotic resistance
implicated by the prevalence of mecA and aap genes in S. epidermidis isolates.
Antibiotics alone are mostly not effective and very often removal of the device is required.
Strains infecting hospitalized patients are resistant to most of the antibiotics currently being
used to treat infections and vancomycin remains the only antibiotic to which resistance has not
developed yet (Foster, 2009) although studies by other researchers have found the presence of
vancomycin resistance (Jones, 2006; Deresinski, 2007). Hellmark et al. (2009) investigated the
antimicrobial activities of 16 antibiotics against S. epidermidis isolated from Prosthetic Joint
Infections (PJIs), with particular focus on rifampicin and rpoB variability. Multi‐resistance (i.e.
resistant to members of more than three classes of antibiotics) was found in 91 % of the isolates.
39 % were resistant to rifampicin, associated with one or two Single‐Nucleotide Polymorphisms
(SNPs) in the rpoB gene. This resistance was different when various culture media were used;
however the reason for these variable resistance results using different agars was not explained.
85 % of the isolates were found to have the mecA gene, which encodes for methicillin resistance.
Among recently available antibiotics, susceptibility to tigecycline and linezolid was found in all
isolates, and 97 % were susceptible to daptomycin. Two novel antibiotics, dalbavancin and
ceftobiprole were also tested, although these antibiotics are not available for routine use yet.
For antibiotics, resistance varied between 0 % (vancomycin) to 82 % (trimethoprim–
sulphamethoxazole). S. epidermidis strains involved in Prosthetic Joint Infections (PJIs) often
show multi‐resistance, including resistance to rifampicin, which is mainly caused by one or two
SNPs. Some of the newer antimicrobial agents may provide alternatives for monotherapy or
combination therapy with rifampicin. The detection of the mecA gene is important in the
treatment of infections of S. epidermidis, which is the most common cause of PJIs (Zimmerli et
al., 2004).
Results of vancomycin resistance by Foster (2009) are contrary to other studies which reported
that there has been an emergence of vancomycin resistance since 1990 in S. epidermidis.
Vancomycin (a glycopeptide) was an effective drug against penicillin‐resistant staphylococci
(Jones, 2006; Deresinski, 2007). CoNS which have acquired methicillin resistance are usually
difficult to eradicate and require a combination of antibiotics and device removal (Mermel et al.,
2001). Diekema et al. (2001) have reported up to 90 % resistance to methicillin during the 1990s.
47
Methicillin‐Resistant Coagulase‐Negative Staphylococci (MR‐CNS) are now widely distributed
among different environmental sources. Huber et al. (2011) detected them in livestock and
chicken carcasses (48.2 % of total samples), and in 46.4 % of isolates from milk and humans
(49.3 % of total isolates). These MR‐CNS belong to different strains with different percentages of
samples resistant to methicillin and various strains dominant in specific environments. For
example, humans mostly have S. epidermidis and S. haemolyticus. In total, Huber et al. (2011)
tested 414 CoNS strains and found that 33‐49 % were resistant to beta‐lactam antimicrobials
and/or ciproflaxacin, clindamycin, erythromycin and tetracycline. These figures show that MR‐
CNSs are resistant to many antimicrobials. These findings make it clear that multidrug‐resistant
CoNS are emerging; moreover, we know that this resistance can be transferred from CoNS to S.
aureus. The mecA gene can now be found in both coagulase‐positive and ‐negative staphylococci
and is mobile thanks to its presence in a mobile genetic element called the Staphylococcal
Cassette Chromosome (SCCmec) (Huber et al., 2011).
Clinical Ophthalmology (2010) described various factors for colonization of ocular systems with
methicillin‐resistant bacteria. For instance, advancing age increases their growth in patients with
Diabetes Mellitus (DM), however, interestingly there is no increase in the risk of acquiring
methicillin‐resistant organisms even in Health Care Workers (HCWs). Methicillin resistance was
reported in 47.1 % (178 out of 378) of S. epidermidis (Methicillin‐Resitant S. epidermidis ‐ MRSE)
as compared to S. aureus (Methicillin‐Resistant S.aureus ‐ MRSA), of which only 29.5 % (26 out of
88) isolates showed resistance. Comparing the presence of this resistance in Health Care
Workers (HCW) to non‐HCW, no significant difference was found (40.7 % and 41.2 % resistance),
respectively. This resistance is believed to be from a protein which is encoded by the mecA gene.
Though for laboratory evaluations, oxacillin is used instead of methicillin and the term
“Methicillin resistance” is used to denote general resistance to Beta‐lactams.
Natural substances like honey also possess certain antimicrobial activities against various
microbes. French et al. (2005) tested the antibacterial activity of honey against 18 strains of
CoNS and found that growth of all 18 isolates was inhibited in the presence of honey.
Comparison of pure natural honey and different dilutions of the honey in regards to their
antibacterial activity was performed and results showed that natural honey was 5.5 to 11.7
times more active due to the osmotic effect produced by the sugar present in the honey. Results
indicate that diluted honey, even up to 20 times, can be used as an antibacterial agent. Also,
reports show rapid healing of wounds infected with S. aureus and Pseudomonas species when
honey is used (Molan and Betts, 2004). Moreover, wound site exudation is limited by the anti‐
48
inflammatory activities of honey thus preventing growth of the bacteria. It also provides a moist
environment that has a positive effect on the healing process and in addition, honey being a
natural product, is not harmful to tissues such as different anti‐septic solutions (Molan, 2002).
To summarise development of resistance to various drugs is observed in CoNS and there is a
need to increase our knowledge regarding the mechanism of the development of such
resistances, the transfer of resistance among various bacteria and their strains and the reasons
and factors for this resistance. Only then will we be able to successfully develop techniques,
methods and strategies to avoid the development of resistance to antibiotics and address this
problem in an effective way.
1.3.6GenotypingandPhenotyping
Phenotypic methods rely on observing various characteristics such as morphology, size, growth
characteristics, resistance or susceptibility to various antibiotics and metabolic properties. It
means that these methods detect features that are expressed by DNA rather than testing the
DNA itself (Zadoks and Watts, 2009). Variation of such features in different strains is problematic
at the species level (Heikens et al., 2005). This can result in wrong identification, classification
and reporting of CoNS (Ben‐Ami et al., 2005). Due to the expression of different components in
strains of the same species, it makes phenotypic methods less reliable than genotyping (Heikens
et al., 2005). Genotypic methods are DNA based and include examples such as ribotyping,
Amplified Fragment Length Polymorphism (AFLP) and DNA sequencing. These methods can
categorize bacteria to the species and strain levels (Zadoks and Watts, 2009).
Various genotyping techniques are now being used to analyse the epidemiological relationships
of different organisms. These techniques have different applications, for example, Pulsed‐Field
Gel Electrophoresis (PFGE) is the preferred method used in studies to determine patient‐to‐
patient spread of organisms while plasmid or transposon analysis is performed to analyse the
transfer of gene(s) among different strains of bacteria (Singh et al., 2006). The following sections
provide brief introductions to some important genotypic techniques useful for S. epidermidis
and other CoNS studies.
49
1.3.6.1Pulsed‐FieldGelElectrophoresis(PFGE)
Currently, different methods for genotyping S. epidermidis isolates are available. PFGE is based
on the enzymatic digestion of the whole chromosome followed by fragment size separation on
an agarose gel (Hu et al., 1995). This method is regarded as poorly informative in regards to
long‐term epidemiological studies (Blanc et al., 2001). PFGE is mostly the method of choice
because it provides extensive genomic details, however, sometimes other methods are required
to analyse relationships among different strains (Foley et al., 2004). As PFGE uses distances
between restriction fragments to differentiate different strains, any change affecting
chromosomal structure will introduce an issue with the reproducibility of results. In comparison
to PFGE, PCR‐based methods reveal details of less than 10 % of the chromosomal structure.
Therefore, to analyse chromosomal changes, PFGE is more useful (Singh et al., 2006).
1.3.6.2MultilocusSequenceTyping(MLST)andSingleNucleotidePolymorphism(SNP)
profiling
MLST is regarded as the gold standard method for genotyping S. epidermidis (Enright et al.,
2000), however, some reports suggest that contaminants may also sometimes display the same
profiles (Tang et al., 1997). For long term epidemiological studies, MLST is the method of choice
(Enright et al., 2000). Singh et al. (2006) described MLST as a more advanced tool for genotypic
studies and it compares multiple genes simultaneously. It must be remembered that MLST is a
method used to determine the diversity of genes and comparison of changes in genes. Methods
based on MLST such as a SNP‐based approach are being currently developed. SNPs use
combinations of Single Nucleotide Polymorphisms (SNPs) derived from the MLST databases to
differentiate different isolates. The MLST database uses variants of housekeeping genes and
normally contains seven fragments of such variants. A particular isolate contains different
combinations of these fragments, known as Sequence Types (STs). These SNPs are identified in
the entire MLST database using a computer program called “Minimum SNPs”. SNP profiles are
derived from specific STs using highly discriminatory SNPs based on the Simpson’s index of
diversity (D). This technique has been demonstrated for genotyping of various microbes
(Robertson et al., 2004; Price et al., 2007; Merchant‐Patel et al., 2010; Rathnayake et al., 2011).
The use of MLST has revealed characteristics and information such as the structural variations
among various species, evolutionary development and epidemiology of S.epidermidis (Thomas et
50
al., 2007). At least nine unrelated lineages of S.epidermidis types involved in nosocomial
infections worldwide have been reported by Miragaia et al. (2007) using the MLST database.
Figure 1.5 describes how MLST is used to compare different genes and alleles.
Figure 1.5: Procedure used for MLST to compare different genes and alleles (Adapted from
Singh et al., 2006).
1.3.6.3HighResolutionMeltAnalysis(HRMA)
HRM analysis is a technique for the fine‐detailed analysis of DNA, especially
variations/mutations in different strains of a species. This methodology requires PCR to be
performed on the same sample in advance and is a quick and time saving method (Ghorashi et
al., 2011). Furthermore, as there is not much handling of the sample, the chances of
contaminating it are also far less compared to conventional PCR. In order to increase its
differentiating power, PCR which is performed prior to HRMA can be modified to produce
sufficient quantities of DNA product which can be subsequently analysed by HRM (Ghorashi et
al., 2011).
51
This technique is not only useful for bacterial studies but is also used in other genetic studies as
well. Ghorashi et al. (2011) have proved the significance of HRMA following a RT‐PCR by
detecting viral strains involved in “Infectious Bursal Disease Virus (IBDV)”. The results are very
promising as it was possible to successfully differentiate known strains (these strains are also
used in vaccine preparation) and strains with variations. They were also able to detect and
differentiate strains by sampling bursal tissue directly which show that this technique is very
sensitive and discriminative. RT‐PCR HRM curve analysis can produce results in only eight hours
after receiving the specimen, which means it is very quick and even cost effective compared to
other methods used for IBDV detection. That means if there is an outbreak, this technique can
be used to detect it at early stages and control measures can be implemented immediately in
order to minimize the spread of the virus.
Our laboratory has a particular interest in the application of HRM to interrogate complex
variations targeting various bacterial loci. Stephens et al. (2008) has successfully applied HRMA
to the spa repeat region of S. aureus. Similarly, Clustered Regularly Interspaced Short
Palindromic Repeats (CRISPRs) locus analysis for Campylobacter jejuni (Price et al., 2007), flaA
fragment for Campylobacter jejuni and Campylobacter coli (Merchant‐Patel et al., 2010) and SNP
genotyping of Enterococcus faecalis and Enterococcus faecium (Rathnayake et al., 2011) have
been investigated in our laboratory. This project forms part of the overall objective of our
research group which is to develop novel, rapid and robust genotyping systems that are
informative and applicable to the rapid diagnosis of clinical disease.
1.3.7Prevention,problemsandfuturedirections
The use of prophylactic therapy, vaccination and eradication of S. epidermidis is not appropriate
(Otto, 2009). One reason is that there is no proven vaccine available and it is not possible to use
such a vaccine in the traditional way (Otto, 2008). Secondly, if we eradicate S.epidermidis, there
is a high chance that another harmful organism might colonize the skin and mucous membranes
and pose therapeutic challenges, so prevention by sterilization of equipment and devices and
disinfection of body parts of patients and health‐care personnel, is the best way to avoid
infections (Rogers et al., 2009).
The removal of central venous catheters is a controversial topic. Attempts to overcome infection
with antibiotic cover without removing the catheter can be attempted but successive positive
cultures on three occasions is an indication for the removal of the device (Mohan et al., 2006).
52
The combination of antibiotics gives better results (Savey et al., 1992) but again the chance of
acquiring resistance is always present. Antibiotic lock technique has been used with great
success, it involves the use of high concentrations of an antibiotic (+/‐ heparin) installation in
CVC at intervals, to prevent colonization of the device and subsequent infection (Haimi‐Cohen et
al., 2001). Even this carries a risk of resistance acquisition and a more severe infection can be the
result. Other techniques such as antibiotic‐impregnated devices, vancomycin prophylaxis,
Intravenous Immunoglobulins (IVIG), pathogen‐specific antibodies etc. have varying success
(Mohan et al., 2006).
Interestingly, Mobile Genetic Elements (MGEs) which encode methicillin resistance are
frequently transferred from S.epidermidis to S.aureus, but this transfer seems to be unilateral as
no transfer of antibiotic resistance genes has been observed from S. aureus to S. epidermidis
thus far (Hanssen et al., 2004). A detailed study of genes that are transferred from S. epidermidis
to S.aureus, and possibly to other bacteria is required (Li et al., 2009). There is also a great
necessity to investigate and determine details about the life cycles of S. epidermidis, both as a
commensal and pathogenic organism, and the relationship between these two life cycles (Otto,
2009).
Although the preferred method for culturing staphylococci is by culturing on blood‐enriched
media, it is a time consuming procedure. Early diagnosis and treatment can definitely reduce
morbidity and mortality related to sepsis. For this reason, molecular methods of diagnosis
should be developed and applied (Loonen et al., 2009).
As it is very extremely difficult to eradicate CoNS, it is necessary to follow good clinical practices
where the chance of introducing bacteria is minimized. Following such procedures is very
important and if contamination occurs, then the growth of bacteria and establishment of
infection should be controlled (Kathie et al., 2009). For example it is very important to properly
sterilize equipment and use sterile techniques while inserting any prosthetic material or device.
Attempts have been made and research continues to prepare material that resists bacterial
adherence and growth. Kathie et al. (2009) listed a few measures for infection control related to
the use of indwelling materials. Such measures include the use of prophylactic antimicrobials,
especially if surgery is performed, use of antiseptics, shaving of the surgical area, proper surgical
procedures and wound care after surgery (Kathie et al., 2009).
According to Harbarth et al. (2003), up to 32 % of hospital‐acquired infections can be avoided by
practicing measures and procedures to avoid infections. Coating devices with different materials
53
is another strategy to avoid infection and colonization of such devices. These coatings include
antibiotics such as ciprofloxacin, ampicillin, clindamicin, antiseptics such as benzalkonium
chloride, gentian violet, iodine and silver coating to produce low‐voltage current impulses to
break or block bacterial attachment (Liu, 1993).
It is obvious from the above discussion that there is a great need to investigate the
characteristics of CoNS. By acquiring this information, we will be able to develop techniques and
procedures to enable the rapid and accurate diagnosis of CoNS disease, which implies
appropriate patient therapy and outcome.
54
1.4SIGNIFICANCE
S. epidermidis is considered normal flora of the skin and mucous membranes and is regarded as
non‐pathogenic organism, however, evidence is mounting, documenting its opportunistic
pathogenic role. The emergence of multi‐resistant S. epidermidis and its associated virulence is
prompting further research in this area. There are different opinions and results about the
source of CoNS in causing infections. For example according to Otto (2009) S. epidermidis
colonize human skin, and therefore are probably contaminated during insertion of medical
devices. Currently, the increased use of various devices is resulting in escalated infection
numbers. However, Archer and Tenenbaum (1980) reported little relation between skin flora
containing CoNS with post‐operative infections in cardiac surgeries, as only 4 % of infections
were caused by CoNS which were present on the skin before surgery. Studies have also shown
that transmission of CoNS from medical staff involved in care and treatment is a possible with
one study finding 18% of isolates from nasal samples from medical personnel and students were
positive for CoNS (Shittu et al., 2006).
Thus it is very important to determine the exact source of CoNS which are involved in causing
infections. If these sources can be identified accurately, prevention strategies to avoid the
introduction of these organisms can be implemented. It is clear from this review that the
prevention of entry of these bacteria is most important as an effective way to avoid infections.
Antibiotic therapy should not be used as the first option as more and more antibiotic resistance
is developing in these bacteria. Excessive and prophylactic use of antibiotics is the main reason
for the development of this multi‐resistance. Therefore, excessive use of antibiotics should be
discouraged and other strategies should be applied to avoid contamination of medical devices
and wounds by these bacteria. For the development of such strategies, the main source(s) of
CoNS should be identified. In this research project, a novel SNP genotyping method will be
applied to determine the clonal distribution of isolates from clinical, skin and nasal S. epidermidis
strains along with an investigation of antibiotic resistance and virulence factors present in these
strains.
We need to know the sources and the mechanisms of CoNS transmission in order to isolate and
identify them to species level. We must also consider the usefulness and economical benefits of
treatment and measures adapted to prevent infections from a particular species before
targeting it per se (Zadoks and Watts, 2009). Strains of S. epidermidis involved in nosocomial
55
infections have obvious genetic variability (Nunes et al., 2005) which makes it important to
develop techniques for rapid and accurate diagnosis of species or subspecies involved.
Patient skin, contaminated devices, medical staff and air are potential sources of S. epidermidis
leading to infections later on. There are different views about reservoirs of infectious organisms,
for example, patient`s skin especially at the insertion site of indwelling devices, contaminated
and colonized devices, air and medical personnel. All of these sites may serve as potential
reservoirs of this organism (McCann et al., 2008).
Rupp (2003) identified peripheral skin as a source of microbes involved in CVCs infections.
Health care workers often carry multidrug‐resistant organisms on their hands (skin) and are a
source for hospital acquired infections. Paul et al. (2011) have found that health care worker`s
hands become more contaminated after working with patients. This is indicated by 90.9 %
contamination after working with patients compared to 59.1 % before seeing patients.
Staphylococci are the main organisms involved in such contaminations. They also proposed that
to avoid these hospital‐acquired infections, there is a need to strictly follow hygiene and sterile
procedures. Moreover, treating such infections using routine therapy is not effective because
these organisms that are introduced from hospital settings are much more resistant to routine
antimicrobials (Paul et al., 2011).
These organisms are particularly important as they are present in the environment, for example
on various surfaces in clinics, and skin of humans. This means that infections can occur due to
the introduction of CoNS from the environment or from the patient`s own skin flora or from
visitors or from medical workers. Their clinical importance is further enhanced by the
development of antibiotic resistance. This project investigated the CoNS genotypes found in
three different sources (i) hospital patient isolates (clinical), (ii) skin and (iii) nasal mucosa
isolates from healthy individuals. HRM‐SNP analysis was applied, which is a novel genotyping
method, not described elsewhere. In addition, the presence of antibiotic resistance and
virulence genes in these strains was also determined. This type of study is particularly important
as it highlights the significance and increased role of CoNS as pathogenic bacteria.
56
1.5CONCLUSION
As part of the normal flora of skin and mucous membranes, S. epidermidis were, until recently,
regarded as non‐pathogenic organism but research based evidences have proved its pathogenic
nature. This emergence of recognition of S. epidermidis as a clinically important organism and its
multiple drug resistance has made it essential to study different aspects of its life cycle,
virulence, transmission mode and factors which contribute to the increasing medical problems
related to this organism.
An important health related issue is the involvement of S. epidermidis in causing infections
related to indwelling devices and there are differing opinions regarding the source of the
organisms involved in these infections. It is very important to clarify whether these organisms
are introduced into the body from the skin of patients or from the environment and possibly
medical personnel.
This research project is designed to investigate the possible source of S. epidermidis strains
responsible for these infections and to characterize the different strains involved. We will also
determine the antibiotic resistance profiles of these strains, as well as the presence of any
associated virulence and biofilm capability. As these organisms are capable of acquiring
resistance against antibiotics, our concern is to find the main source of their entry to the body
and suggest some measures to avoid such entry instead of relying on antimicrobial therapy only.
57
CHAPTER2
MATERIALSANDMETHODS
58
2.1ISOLATES
Three different types of sources were selected:
1. CoNS isolates originating from patient cultures were obtained from the Prince Charles
(PC) Hospital, Brisbane. These strains were cultured and isolated from patients who
suffered from septicaemia.
2. Swabs were taken from the skin of healthy individuals and pure cultures of CoNS strains
were isolated from these samples.
3. Nasal mucosa swabs were obtained from healthy individuals and pure cultures of CoNS
strains were isolated from these samples.
2.2BACTERIALCULTURES
2.2.1ClinicalIsolates
Patient cultures were acquired from the Prince Charles Hospital. These isolates were previously
identified as CoNS strains, stored on Nutrient Agar (NA) slopes (Biomérieux, Australia) at a
temperature of 4 °C. These strains were subcultured onto NA plates using the 16‐streak
method. Plates were incubated at 37 °C for 24 hours. After overnight incubation, 12‐15 colonies
were selected from each plate and were suspended in 180 µl of Lysostaphin solution (200
µg/ml). The Lysostaphin enzyme was obtained from Sigma‐Aldrich®, USA. This suspension was
incubated at 37 °C for 30 minutes. After 30 minutes of incubation this mixture was ready for
DNA extraction.
59
2.2.2SkinandNasalmucosaswabsamples
Swab samples were collected from skin and nasal mucosa of healthy individuals (second and
third year students at QUT). Ethical approval for collecting these samples from humans was
obtained from the QUT Human Research Ethics Committee. In addition, written consent was
obtained from each student participating in the study.
Both skin and nasal samples were inoculated on a selective medium for staphylococci i.e.
Mannitol Salt Agar (MSA). Plates were incubated at 37 °C for 24 hours. After overnight
incubation, isolated colonies that appeared pink/red (Coagulase‐negative staphylococci usually
produce small red/pink colonies without affecting the medium) were selected and inoculated
onto NA plates and incubated at 37 °C for 24 hours, in order to obtain pure growth. Yellow
colonies on the MSA plates (Coagulase‐ positive staphylococci produce yellow colonies with the
formation of a yellow zone around the colonies) were not selected for this study. Interestingly, a
few of the isolates which formed pink colonies on the MSA agar tested positive for coagulase
production. The Staphylase test (Sigma‐Aldrich®, USA) was performed on each of the isolates to
confirm the coagulase test. Strains that tested staphylase‐negative were used for further
investigation. Once again, 12‐15 colonies from each plate were suspended in Lysostaphin
solution (200 µg/ml) and incubated at 37 °C for 30 minutes. After 30 minutes of incubation this
mixture was ready for DNA extraction.
2.3DNAEXTRACTION
DNA extraction for nasal samples was done using an automated system for DNA extractions
based on the Corbett‐X tractor Gene automated DNA extraction system (Corbett Robotics,
Australia) as described by Stephens et al.(2007). DNA extraction from skin and patient isolates
was done using the DNeasy® Blood & Tissue Kit (QIAGEN®, Australia). Figure 2.1 shows the basic
principle involved in DNA extraction. This figure depicts that lysis of cells is followed by binding
products to a special tube provided by the manufacturer and after multiple washing steps; the
DNA is collected in a separate tube.
60
Figure 2.1: Basic principle involved in DNA extraction (Adapted from http://www.qiagen.com).
61
2.4GENTOTYPINGOFCoNSUSINGSINGLENUCLEOTIDE
POLYMORPHISMS(SNPs)(AIMS1and2)
2.4.1SNPidentification
The software package “Minimum SNPs” was used to derive a set of eight SNPs from the S.
epidermidis MLST database (http://www.mlst.net). The “Minimum SNPs” software helps in the
identification of highly resolving SNPs from large datasets such as the MLST database (Robertson
et al., 2004). “Minimum SNPs” identifies the most informative SNP and selects it as SNP1,
following which the next SNP (SNP2) is identified such that it provides high resolution in
combination with SNP1. This process is continued until the desired level of discrimination is
achieved. The highly informative SNPs are identified based on the Simpson’s index of diversity
(Hunter and Gaston 1988), or D‐value.
These SNPs were interrogated in the seven housekeeping genes of S. epidermidis which divided
strains into closely related Sequence Types (ST) and clonal complexes. The MLST housekeeping
genes used as input sequences for the Minimum SNPs program were: arcC, aroE, gtr, mutS, yqiL,
tpiA and pyrR.
Table 2.1: List of SNPs derived from the Minimum SNPs software program indicating the
cumulative Simpson’s Index of Diversity (D) value for each SNP
SNP Cumulative D‐value
arcC87 0.4831
gtr395 0.7436
pyr81 0.7996
arcC397 0.8446
mutS223 0.8931
gtr98 0.9319
62
Table 2.1: List of SNPs derived from the Minimum SNPs software program indicating the
cumulative Simpson’s Index of Diversity (D) value for each SNP
aroE265 0.9418
pyr129 0.9502
2.4.2Primers
Primers were designed using the Primer express software program (Applied Biosystems, USA).
Table 2.2 lists the primer sequences designed to genotype the CoNS isolates. Primers were
obtained from Sigma‐Aldrich®, Australia.
Table 2.2: Primer sequences for each SNP
SNP Primers Sequence (From 5’ to 3’) Tm
arcC87 forward
reverse
ATCAATTTGTAGAAGATGCTGGTCGAG
TACTWTYCAGTTCGATAATAGATATTGGTTG
62 °C
gtr395 forward
reverse
CARTGAYATAYACTGTRCCTTCAGTAGGT
TCAGGTTCAGGTAAAACGACTTTACTT
58 °C
pyr81 forward
reverse
GAATATAACAAGGRAACYAAAGATTTABTTCTATTAG
TTGTTCWATTRAATTTATTTTATCTTGTATAYGATG
58 °C
arcC397 forward
reverse
AMAACAAATATAKATACGCTTAAAACATATATT
GGCAGATTCRATTTTAGGTAGCA
55 °C
mutS223 forward
reverse
YTTWGGATCTTCCATTTGTTCACAT
GTGTGGYGTACCATATCATTCTG
58 °C
gtr98 forward
reverse
TARCATCACTTTAGGATTCATRGCTAATG
AARATCAACGGCCRCATGCT
60.5 °C
63
Table 2.2: Primer sequences for each SNP
aroE265 forward
reverse
GTTTTAATAATTGGTCGTTAAATAWTAACAAA
CATACCAGCAGGYGTAGTRTTTATTATAA
60.5 °C
pyr129 forward
reverse
AGRGGTGCYTTTTTAGCACATCRTATACA
YTTATCAACRTCRTCTCGAAAATG
58 °C
2.4.3Real‐TimePCR
The Rotor‐Gene 6000 instrument (Corbett Life Science, now QIAGEN, Australia) was used for
Real‐Time PCR experiments and the following conditions were used, as described by Stephens et
al. (2007). Table 2.3 is showing details of reagents used for RT‐PCR reaction.
Table 2.3: Each RT‐PCR Reaction contained (SNP Analysis)
No. Reagent Volume
1 Forward Primer (20 µM) 0.25 µl
2 Reverse Primer (20 µM) 0.25 µl
3 SybrGreen® Master Mix (Invitrogen, Australia) 10 µl
4 Water (DNAse/RNAse free) (Roche, Australia) 7.5 µl
5 DNA 2 µl
Total Volume 20 µl
64
Real‐Time PCR cycling conditions (Stephens et al., 2007):
Each SNP was then amplified using a three step temperature cycling procedure as follows: 50 °C
for 2 minutes, 95 °C for 2 minutes, followed by 40 cycles of 95 °C for 15 seconds, a specific
annealing temperature for each primer (Table 2.2) for 20 seconds, 72 °C for 35 seconds. After
the 40 PCR cycles, the DNA amplicons were subjected to a melt step ramping from 72 °C to 99
°C rising by 1 °C and waiting for 90 seconds for pre‐melt conditioning on first step only, followed
by a 5 second wait for each step thereafter. Following the melt step, a HRM step was done by
ramping from 70 °C to 90 °C, rising by 0.05 °C at each step, waiting for 90 seconds for pre‐melt
conditioning on first step only, followed by a 2 second wait for each step thereafter.
2.4.4AssignmentofSNPprofilesusingHigh‐ResolutionMeltcurveanalysis
Melt curves are predominantly used to determine the melting temperature (Tm) of amplified
double‐stranded DNA by recognizing that the precise shape of a melting curve is a function of
the DNA sequence. This characteristic forms the basis of HRM analysis. The Rotor‐Gene 6000
proprietary software (version 1.7.34) enables the user to visualize HRM data in multiple ways.
The negative derivative of fluorescence (F) over temperature (T) (df/dt) curve primarily displays
the Tm, the normalized raw curve depicts the decreasing fluorescence versus increasing
temperature, and difference curves, which display a user‐defined curve as the baseline (i.e. the
x‐axis), and depicts other normalized curves in relation to that baseline (Stephens et al., 2008).
Rotor‐Gene ScreenClust HRM Software is a computer based program from QIAGEN® for
analysing data from Rotor‐Gene 6000 cycler (and Rotor‐Gene Q). This software program is used
to assign genotypes to different HRM melt curves. It analyses similarities and differences in HRM
data from various specimens obtained by Rotor‐Gene cyclers (6000 or Q series). It interprets
data using innovative mathematical algorithms and divides samples into separate clusters
according to fluorescence curves. It creates a median curve by comparing fluorescence values
from all specimens and forms a residual plot. Individual samples are then analysed according to
this residual plot. This software involves “Principle Component Analysis” which determines
similarities and differences in the data and forms clusters. There are two modes of this analysis;
supervised and unsupervised. Supervised mode analyses samples against known controls while
unsupervised mode is used for analysing unknown genotypes/specimens. The unsupervised
mode of the software was used. It can be used for SNP typing, mutation scanning and detection.
65
The final data or analysis can be stored in various file formats (e.g. JPG, PDF, XLS etc.) simplifying
downstream data analysis (http://www.qiagen.com).
2.4.5ConfirmationofHRM‐SNPprofilesusingDNAsequencing
After analysing the SNP‐HRM profiles using the Rotor‐Gene ScreenClust HRM Software, DNA
sequencing was performed on selected strains from each SNP cluster. The purpose of this
sequencing was to confirm whether or not the different clusters for each SNP corresponded to a
specific nucleotide at the SNP position in the gene. As Qiagen® recommends that data might
need manual analysis and interpretation (http://www.qiagen.com), we also analysed data
manually. This manual analysis was done by looking at difference graphs by comparing all
samples in each batch to a selected (representative) sample for each cluster.
2.4.6SNPvalidationbyDNAsequencing
2.4.6.1Primers
The following primers were used for sequencing all five genes by selecting representative
isolates from each cluster of each SNP (http://www.mlst.net). Primers were obtained from
Sigma‐Aldrich®, Australia. Table 2.4 lists the primer sequences.
Table 2.4: Primers used for DNA sequencing
Target
gene
Primer Sequence Product (bp)
arc forward
reverse
TGTGATGAGCACGCTACCGTTAG
TCCAAGTAAACCCATCGGTCTG
465
aroE forward
reverse
CATTGGATTACCTCTTTGTTCAGC
CAAGCGAAATCTGTTGGGG
420
66
Table 2.4: Primers used for DNA sequencing
gtr forward
reverse
CAGCCAATTCTTTTATGACTTTT
GTGATTAAAGGTATTGATTTGAAT
438
mutS forward
reverse
GATATAAGAATAAGGGTTGTGAA
GTAATCGTCTCAGTTATCATGTT
412
pyr forward
reverse
GTTACTAATACTTTTGCTGTGTTT
GTAGAATGTAAAGAGACTAAAATGAA
428
Table 2.5 is showing details of reagents used for RT‐PCR reaction of DNA Sequencing.
Table 2.5: Each PCR reaction contained (DNA Sequencing)
No. Reagent Volume
1 5 x MyTaq Red Buffer (Bioline®, Australia) 5 µl
2 10 µM Forward Primer 1 µl
3 10 µM Reverse Primer 1 µl
4 MyTaq HS Polymerase (Bioline®, Australia) 0.25‐1 µl
5 Template DNA 1‐2 µl
6 Water (DNAse/RNAse free) (Roche, Australia) up to 25 µl
Total Volume 25 µl
Notes:
a. No addition of dNTPs is required as the 5 x MyTaq Red Buffer contains dNTPs.
b. No loading dye is required for gel electrophoresis as this is also included in the 5 x
MyTaq Red Buffer.
67
PCR cycling conditions
Each sample was then amplified using a PCR cycling procedure as follows: 95 °C for 3 minutes,
followed by 34 cycles of 95 ° C for 30 seconds, 50 ° C for 60 seconds, 72 °C for 60 seconds and 72
°C for 10 minutes. These PCR reactions were applied using a Mastercycler PCR engine
(Eppendorf®, Australia). This PCR product was tested by gel electrophoresis and the DNA was
purified by following steps (Section 2.4.6.2) using the ISOLATE kit from Bioline®, Australia.
2.4.6.2 PCRproductpurificationusingtheISOLATEkit(Bioline®,Australia)
1) A spin column was placed in collection tube (1.5 ml)
2) 250 µl of Binding buffer A was added
3) 25 µl of PCR reaction was added
4) This was spun at 10,000 rpm for 2 minutes
5) The collection tube was discarded
6) The spin column was placed into a new 1.5 ml tube
7) 10 to 20 µl elution buffer was added to the spin column membrane
8) This was incubated at room temperature for 1 minute
9) After incubation, the tube was spun at 6000 rpm for 1 minute
10) The purified PCR product was eluted from the column
This purified/isolated PCR product was used as the DNA template for sequencing reactions.
2.4.6.3SequencingPCR
The following PCR conditions (Table 2.6) were applied using a Mastercycler PCR engine
(Eppendorf®,Australia).
68
Table 2.6: Each PCR reaction contained (DNA Sequencing 2)
No. Reagent Volume
1 Template DNA 1‐2 µl
2 3.2µM Forward Primer 1 µl
3 Sequence buffer 3.5 µl
4 Big dye (Applied Biosystems®, Australia) 1 µl
5 Water (DNAse/RNAse free) (Roche, Australia) up to 20 µl
Total Volume 20 µl
Cycling conditions (http://www.mlst.net)
Each sample was then amplified using a PCR cycling procedure as follows: 94 °C for 5 minutes,
followed by 30 cycles of 96 ° C for 10 seconds, 50 ° C for 5 seconds, 60 °C for 4 minutes and 4 °C
for 10 minutes. These PCR reactions were applied using a Mastercycler PCR engine (Eppendorf®,
Australia).
Sequencing products obtained after this reaction was subjected to the following steps of
Ethanol/EDTA precipitation. This protocol is obtained from Griffith University DNA Sequencing
Facility (GUDSF). Table 2.7 mentions details of steps involved in Ethanol/EDTA precipitation.
Table 2.7: Steps of Ethanol/EDTA precipitation
Step Action
1 5 µl of 125 mM EDTA (Sigma Aldrich®, Australia) (pH 8.0) is placed into a 1.5 ml
microcentrifuge tube
2 The sequencing reactions were pipetted into the tube and vortexed briefly
3 60 µl of 100 % ethanol was added to the mixture and vortexed briefly
4 This was incubated at room temperature for 15 minutes followed by a 20 minute spin at
maximum speed
69
Table 2.7: Steps of Ethanol/EDTA precipitation
5 The supernatant was aspirated carefully and the pellet was rinsed by the addition of 70 %
ethanol and vortexing briefly
6 This was spun for 5 minutes at maximum speed
7 The supernatant was aspirated carefully and the pellet was air dried on the bench
These tubes contain the precipitated dried PCR products and were placed onto the 3500 Series
Genetic Analyser (Applied Biosystems ®, Australia).
2.5 DETECTIONOFicaGENESINVOLVEDINBIOFILMFORMATION
(AIM3)
2.5.1Primers
For the detection of the ica operon, the following primers were used (Diemond‐Hernández et al.,
2010; Ziebuhr et al., 1999). Primers were obtained from Sigma‐Aldrich®, Australia. Table 2.8
provides details of primers used for ica genes analysis.
Table 2.8: Primer sequences for ica genes
Primers Sequence (From 5’ to 3’) Product (bp)
icaA forward
icaA reverse
GAC CTC GAA GTC AAT AGA GGT
CCC AGT ATA ACG TTG GAT ACC
814
70
Table 2.8: Primer sequences for ica genes
icaB forward
icaB reverse
ATG GCT TAA AGC ACA CGA CGC
TAT CGG CAT CTG GTG TGA CAG
526
icaC forward
icaC reverse
ATA AAC TTG AAT TAG TGT ATT
ATA TAT AAA ACT CTC TTA ACA
989
icaD forward
icaD reverse
AGG CAA TAT CCA ACG GTA A
GTC ACG ACC TTT CTT ATA TT
371
2.5.2ConventionalPCR
The following PCR conditions were applied using a Mastercycler PCR engine ( Eppendorf®,
Australia). Table 2.9 provides information of reagents used for RT‐PCR reaction.
Table 2.9: Each PCR reaction contained (ica genes)
No. Reagent Volume
1 5 x MyTaq Red Buffer (Bioline®, Australia) 5 µl
2 10 µM Forward Primer 1 µl
3 10 µM Reverse Primer 1 µl
4 MyTaq HS Polymerase (Bioline®, Australia) 0.25‐1 µl
5 Template DNA 1‐2 µl
6 Water (DNAse/RNAse free) (Roche, Australia) up to 25 µl
Total Volume 25 µl
71
Notes:
a. No addition of dNTPs is required as the 5 x MyTaq Red Buffer contains dNTPs.
b. No loading dye is required for gel electrophoresis as this is also included in the 5 x
MyTaq Red Buffer.
PCR cycling conditions
Each sample was then amplified using a PCR cycling procedure as follows: 95 °C for 3 minutes,
followed by 30 cycles of 94 ° C for 30 seconds, 57 °C (icaA) /65 °C (icaB) / 43 °C (icaC) /65 °C
(icaD) for 30 seconds and 72 °C for 10 minutes. These PCR reactions were applied using a
Mastercycler PCR engine (Eppendorf®, Australia).
2.5.3Gelelectrophoresis
A 2 % agarose gel was used for electrophoresis, with a Tris‐Borate‐EDTA (TBE) running buffer. A
100 bp DNA molecular weight marker (Bioline®, Australia) was used for analysis.
2.6DETECTIONOFANTIBIOTICRESISTANCEGENES(AIM3)
2.6.1Primers
The following primers were used to detect the presence of antibiotic‐resistant genes in all the
CoNS isolates (Rathnayake et al., 2011). Primers were obtained from Sigma‐Aldrich®, Australia.
Table 2.10 lists the primer sequences and the positive controls used for each gene.
Table 2.10: Primers and controls for antibiotic resistance genes
Target
gene
Sequence (5’ to 3’) Tm
(°C)
Antibiotic Control Product
(bp)
van A forward:
TGTGCGGTATTTGGGAAACAG
reverse:
GATTCCGTACTGCAGCCTGATT
64.5
°C
Vancomycin ATCC51559
Enterococcus
faecium
72
72
2.6.2Real‐TimePCR
The Rotor‐Gene 6000 instrument (Corbett Life Science, now QIAGEN, Australia) was used to
detect the antibiotic resistance genes using the following RT‐PCR conditions as described by
Stephens et al., (2007). Table 2.11 mentions reagents used for RT‐PCR analysis of antibiotics.
Table 2.11: Each Real‐Time PCR reaction contained (Antibiotics)
No. Reagent Volume
1 Forward Primer (20 µM) 0.25 µl
2 Reverse Primer (20 µM) 0.25 µl
Table 2.10: Primers and controls for antibiotic resistance genes
van B forward:
TCTGCTTGTCATGAAAGAAAGAGAA
reverse: GCATTTGCCATGCAAAACC
64
°C
Vancomycin ATCC700802
Enterococcus
faecalis
121
gyr A forward:
CGGATGAACGAATTGGGTGTGA
reverse: AATTTTACTCATACGTGCTT
62
°C
Ciprofloxacin ATCC51559
Enterococcus
faecium
240
acc(6’)‐
aph(2’)
forward:
TCCTTACTTAATGACCGATGTACTCT
reverse: TCTTCGCTTTCGCCACTTTGA
61
°C
Gentamicin ATCC700802
Enterococcus
faecium
147
mecA forward:
GATCGCAACGTTCAATTTAATTTTG
reverse:
GCTTTGGTCTTTCTGCATTCCT
64°C
Methicillin ATCC25929
S. aureus
99
73
Table 2.11: Each Real‐Time PCR reaction contained (Antibiotics)
3 SybrGreen® Master Mix (Invitrogen, Australia) 10 µl
4 Water (DNAse/RNAse free) (Roche, Australia) 7.5 µl
5 DNA 2 µl
Total Volume 20 µl
Real‐Time PCR cycling conditions (Stephens et al., 2007)
Each SNP was then amplified using a three step temperature cycling procedure as follows: 50 °C
for 2 minutes, 95 °C for 2 minutes, followed by 40 cycles of 95 °C for 15 seconds, a specific
annealing temperature for each primer (Table 2.2) for 20 seconds, 72 °C for 35 seconds. After
the 40 PCR cycles, the DNA amplicons were subjected to a melt step ramping from 72 °C to 99
°C rising by 1 °C and waiting for 90 seconds for pre‐melt conditioning on first step only, followed
by a 5 second wait for each step thereafter. Following the melt step, a HRM step was done by
ramping from 70 °C to 90 °C, rising by 0.05 °C at each step, waiting for 90 seconds for pre‐melt
conditioning on first step only, followed by a 2 second wait for each step thereafter.
74
75
CHAPTER3
GENOTYPINGOFCoNS
76
3.1INTRODUCTION
Genotyping of CoNS is regarded as more accurate, precise and more informative compared to
phenotyping. In genotyping, DNA is analysed rather than looking at characteristics which are
expressed by DNA. Phenotyping targets structural and chemical features which are expressed
due to the particular genome of an organism. Variability in expression of such features in various
strains of the same species can lead to false results. Moreover, genotyping characterises
bacteria to species and strains levels, which is usually not the case in phenotyping (Zadoks and
Watts, 2009; Heikens et al., 2005; Ben‐Ami et al., 2005). PFGE is mostly considered as the
method of choice for epidemiological studies as it reveals detailed chromosomal information but
occasionally it requires additional studies to determine the relatedness among strains. In
comparison, PCR‐based methods rely on information from a small fragment of DNA (Foley et al.,
2004; Singh et al., 2006).
For genotyping of S. epidermidis, MLST is considered the gold standard method (Enright et al.,
2000). New techniques based on MLST, such as SNP‐based analysis, make use of MLST databases
to differentiate various strains and clonal groups. This approach has been successfully applied to
S. aureus (Robertson et al., 2004), Campylobacter species (Merchant‐Patel et al., 2010),
Enterococcus species (Rathnayake et al., 2011), viral (Ghorashi et al., 2011) and human genome
studies (Dufresne et al., 2006). Another emerging technique for rapid microbial identification
using mass spectrometry (MS) is MALDI‐TOF MS (matrix assisted lazer desorption/ionization‐
time of flight mass spectrometry). This technique performs semi‐quantitative analysis of various
proteins and genetic materials of microorganisms (Kaleta et al., 2011).
The software package “Minimum SNPs” is used to derive a minimal set of SNPs from the S.
epidermidis MLST database and Real‐Time PCR is applied to analyse the SNPs located in the
seven housekeeping genes of S. epidermidis. Depending on the combination of SNPs, S.
epidermidis isolates can be differentiated into different strains and clonal groups. Robertson et
al. (2004) successfully used the same software to analyse SNPs in S. aureus.
High Resolution Melt Analysis (HRMA) is an emerging method used to analyse and differentiate
species/strains of various micro‐organisms. HRMA is a single‐step method and only one‐step
analysis is required in a closed‐ tube system. This method is not only easy to perform, less
expensive and time saving, but is reliable and sensitive as well. RT‐PCR is performed prior to
melt curve analysis and PCR conditions can be modified to increase accuracy (Wittwer et al.,
77
2003; Ghorashi et al., 2011). The combination of RT‐PCR and melt curve analysis (RT‐PCR HRMA)
has been demonstrated to be very robust as Ghorashi et al. (2011) reported that HRMA can
detect and differentiate viral DNA in specimens collected directly from infected tissue. RT‐PCR
HRMA can be used as a rapid technique for the diagnoses of various diseases and infections.
DNA‐based methods provide very precise and useful information in identifying CoNS (Zadoks
and Watts, 2009). When using genotyping techniques, we have to select methods that target
and detect traits which are different and diverse among bacteria. By screening only a few
housekeeping genes, genotyping methods can be used to characterise bacteria to species and
strains. The results vary according to the types of CoNS and the method used, but overall
genotyping is more informative compared to phenotyping.
Single Nucleotide Polymorphism (SNP) primers for S. epidermidis genotyping were designed and
applied to Real‐Time PCR and High Resolution Melt Analysis. The SNP‐HRM profiles of S.
epidermidis strains from clinical and non‐clinical sources were determined.
3.2AIMS
Aim 1
Development of a novel S. epidermidis SNP genotyping method using the “Minimum SNPs”
software program.
Aim 2
Characterization of S. epidermidis strains isolated from clinical and non‐clinical sources.
3.3METHODS
The methods for bacterial isolation, DNA extraction, RT‐PCR (HRM‐SNP) analysis and DNA
sequencing are as described in Chapter in 2, Sections 2.2, 2.3, 2.4 and 2.4.6, respectively.
78
Based on the S. epidermidis MLST database (http://www.mlst.net), we selected a set of eight
SNPs using a computer‐based program “Minimum SNPs”. Real‐Time PCR analysis was applied to
detect the presence or absence of each SNP in each of the samples (129 samples tested) using
the Rotor‐Gene 6000 instrument (Corbett Life Science, now QIAGEN, Australia).
For analysing HRM data from the Rotor‐Gene 6000 cycler, QIAGEN® has designed “Rotor‐Gene
ScreenClust HRM Software”. By comparing High Resolution Melt (HRM) data (HRM curves), this
software divides samples into clusters based on similarities or differences amongst various
samples. Two modes of data analysis are available. Firstly, the supervised mode compares
known controls against unknown samples to perform analysis. Secondly, the unsupervised mode
can be used when no known controls are available. This mode of analyses produces a residual
plot based on all samples and compares their HRM curves to each other. The unsupervised
mode of this software was used to analyse all the strains investigated in this study.
Finally, we selected representative strains from each cluster for further validation. DNA
sequencing was performed on these selected strains from each SNP cluster to observe whether
or not these strains have different nucleotide base pairs at a particular SNP position.
Furthermore, the difference curve analysis was also used, which displays a user‐defined curve as
the baseline (i.e. the x‐axis), and depicts other normalized curves in relation to that baseline
(Stephens et al., 2008).
3.4RESULTS
3.4.1:SNPanalysisusingHRMA
In total 129 samples were tested, 48 from hospital patients and 81 from healthy individuals. In
the first step, each sample was tested for the presence or absence of each SNP individually. The
Rotor‐Gene 6000 cycler software was used for this step. For each analysis, the digital filter was
set to “heavy” to obtain smoother curve edges and the replicate grouping option was also
activated. Figures 3.1 (a) and (b) illustrate how positive samples have well formed curves
compared to negative samples which fail to do so with HRM analysis.
79
3.1 (a): HRM analysis using Rotor‐Gene 6000 cycler software showing positive and negative
samples.
3.1 (b): HRM analysis using Rotor‐Gene 6000 cycler software showing only positive samples
(after deselecting negative ones).
The Rotor‐Gene 6000 software also allows for “Normalized Graph” analysis. Figure 3.2 (a) shows
the obvious difference in the curve shape of positive samples compared to a negative sample.
Figure 3.2 (b) shows the same positive curves after deselecting or deactivating negative samples.
Negative
sample curves
80
3.2 (a): Normalized graph analysis showing a negative sample along with positive samples.
3.2 (b): Normalized graph analysis showing all positive samples only.
Positive samples Negative samples
Positive samples
81
3.4.2StraincodeassignmenttoSNPprofiles
The data obtained after SNP analysis was used to allocate strain codes to each sample. As an
example, 15 samples are shown in Table 3.1. This table shows positive and negative results for
each SNP and their respective assigned strain codes. If a sample is positive for a particular SNP it
is assigned the value of “2” while a negative one is assigned a value of “1”.
Table 3.1: SNPs and Strain codes (SC)
No. arcC87 gtr395 pyr81 arcC397 mutS223 gtr98 aroE265 pyr129 SC
HB24 + + ‐ ‐ + ‐ ‐ + 22112112
HB16 + ‐ + + + + + + 21222222
HB25 + ‐ ‐ ‐ + ‐ ‐ + 21112112
HB 9 + + ‐ ‐ + ‐ ‐ + 22112112
HB12 + ‐ ‐ ‐ ‐ ‐ ‐ ‐ 21111111
N 1 + + + + + + ‐ + 22222212
N 2 + + + + + + ‐ + 22222212
N3 + + + + + + + + 22222222
N4 ‐ ‐ + ‐ ‐ + + ‐ 11211221
N5 + + + + + + ‐ + 22222212
SK1 + + + + + + + ‐ 22222221
SK2 + + + + + + + + 22222222
SK3 ‐ ‐ + + + + + + 11222222
SK4 ‐ ‐ ‐ ‐ + + + ‐ 11112221
SK5 ‐ ‐ + + + + + + 11222222
The results for SNP analysis and strain codes for all specimens are included in Appendix 1,
Section 1.
82
Table 3.2 represents a detailed list of all the strain codes obtained in this study. It also shows the
prevalence of each strain code found for all the isolates investigated in this study.
Table 3.2: Strain codes (SC) distribution amongst different sources.
No. Code Skin Nasal Hospital Total
1 22222221 1 0 0 1
2 22222222 19 12 8 39
3 11222222 11 0 0 11
4 11112221 1 0 0 1
5 11112111 1 0 1 2
6 12222222 5 0 1 6
7 11112211 1 0 0 1
8 21222222 1 0 8 9
9 11122222 0 0 1 1
10 11112112 0 0 2 2
11 11111112 1 0 0 1
12 12111112 0 1 2 3
13 12112122 0 0 1 1
14 22222212 0 10 0 10
15 11211221 0 9 0 9
16 21221211 0 1 0 1
17 11211211 0 6 0 6
18 22212222 0 1 0 1
19 21112112 0 0 2 2
20 22112112 0 0 3 3
21 21111111 0 0 2 2
22 11111111 0 0 1 1
23 21222221 0 0 5 5
24 21112111 0 0 3 3
25 11122212 0 0 1 1
26 21111112 0 0 1 1
27 22112212 0 0 1 1
28 22111112 0 0 1 1
83
Table 3.2: Strain codes (SC) distribution amongst different sources.
29 21111212 0 0 1 1
30 21111222 0 0 1 1
31 21122122 0 0 1 1
Total 41 40 48 129
3.4.3:ScreenClustHRManalysis
Positive samples from section 3.4.2 were analysed further using the Rotor‐Gene ScreenClust
HRM Software. As described earlier in the methods section, this software compares HRM‐curve
profiles of samples and separates them into clusters based on similarities or differences amongst
the HRM curves generated for different strains. Figure 3.3 shows how this software represents
data in a list format.
84
Figure 3.3: SceenClust date represented for each isolate tested.
In addition to the list format, the ScreenClust software program can also represent the data in
the form of a “Principal Component Analysis” which separates samples in the form of three‐
dimensional groups/clusters. Figure 3.4 shows a cluster plot of the same data shown in Figure
3.3.
85
Figure 3.4: Arrangement of data in cluster plot arrangement after Screenclust analysis.
3.5CONFIRMATIONOFHRM‐SNPPROFILESUSINGDNASEQUENCING
A representative strain from each cluster was selected and DNA sequencing was performed to
validate the SNP present at each of the respective positions. Table 3.3 shows the results for
three of the eight SNPs. The remaining five SNP data sets are included in Appendix I, Section 2.
86
Table 3.3: DNA sequencing results of nucleotide bases present at specific SNP positions
SNP Batch number Cluster # Sample No. Nucleotide base
arcC 87
1 Cluster 1 A 1 C
Cluster 2 A 3 T
2 Cluster 1 A 5 A
Cluster 2 A 6 C
3 Cluster 1 A 7 A
Cluster 2 A 9 G
pyr129
1 Cluster 1 C1 and C 2 G and T,
respectively.
Cluster 2 C 4 T
2 Cluster 1 C 5 T
Cluster 2 C 7 A
3 Cluster 1 C 9 T
Cluster 2 C 11 G
arcC 397
1 Cluster 1 A 1 C
Cluster 2 A 3 T
2 Cluster 1 A 5 A
Cluster 2 A 6 C
3 Cluster 1 A 7 A
Cluster 2 A 9 G
3.6CONFIRMATIONOFHRM‐SNPPROFILESUSINGDIFFERENCECURVE
ANALYSIS
HRM‐SNP profiles were also observed by performing “Normalized graph” and “Difference graph”
analysis using the Rotor‐Gene 6000 software. As an example, two samples (A1 and A3) from
different clusters for the SNP arcC 87 is shown in figure 3.5.
87
Figure 3.5 (a): Normalized graph analysis, comparing samples from different clusters.
Figure 3.5 (b): Difference graph analysis, comparing samples from different clusters.
A3 (T)
A1 (C)
A3 (T) A1 (C)
88
Figure 3.6 shows the normalized and difference graph analysis for two strains from the same
cluster compared to a strain from a different cluster.
Figure 3.6 (a): Normalized graph analysis, comparing two samples from same cluster and one
from a different cluster.
Figure 3.6 (b): Difference graph analysis, comparing two samples from same cluster and one
from a different cluster.
C2 (T)
C1 (G)
C3 (A)
C1 (G) C2 (T)
C3 (A)
89
3.7DISCUSSION
In total, 129 isolates were tested for the presence or absence of eight SNPs separately. Forty
eight isolates were from hospital patients while the remaining 81 isolates were from healthy
individuals. Nonclinical samples were obtained from two different sources, 41 isolates were
obtained from the skin of healthy individuals whereas the remaining 40 isolates originated from
the nasal mucosa of healthy individuals.
Firstly, we performed difference curve analysis to observe the presence or absence of each SNP
in all samples. In total, eight SNPs were tested. All samples tested positive for at least one SNP
except for one isolate which tested negative for all. The Rotor‐Gene 6000 software program
makes it easy to deselect or deactivate negative samples so that further analysis can be done on
positive samples only. Secondly, this data was arranged in table format and assigned strain
codes depending on the set of SNPs present. This strain code presentation makes it simple to
differentiate isolates based on their SNP profiles. In total, 31 different strain codes were
obtained, representing 31 different strains. Hospital samples had 21 different strains. In
comparison, skin and nasal samples had nine and seven binary types, respectively. This clearly
demonstrates the increased diversity of hospital CoNS compared to those in healthy individuals.
This might be due to the selective hospital environment as a result of the increased use of
disinfectants and antimicrobial agents.
It is obvious from this data analysis that certain strains dominate a particular source. For
example, in skin samples, three main strains were identified. Out of 41 skin samples, 19/41
(46.34 %) samples belonged to the same strain (SC 22222222) followed by 11/41 (26.83 %) (SC
11222222) and 5/41 (12.20 %) (SC 12222222). In the case of nasal samples, the same strain (SC
22222222), which also dominated in skin samples, was present in the highest number i.e. 12/40
(30 %). Three more strains were present in significant numbers; firstly (SC 22222212) had 10/40
(25 %), secondly (SC 11211221) had 9/40 (22.5 %) and thirdly (SC 11211211) had 6/40 (15 %)
samples. Interestingly, three strain code type‐strains were present only in nasal samples.
Hospital samples were more diverse, although, there are two strains present in significant
numbers. The SC 22222222, which dominated in skin and nasal isolates, is present in 8/48 (16.66
%) of clinical isolates. Similarly, 8/48 (16.66 %) isolates belong to a different strain code (SC
21222222). This data clearly shows that certain strains are present in prominent numbers in a
certain environment, for example, SC 22222212 was found for 10/40 (25 %) strains isolated from
90
nasal samples but was absent in skin and hospital isolates. Similarly, other strains are widely
distributed. For example, SC 22222222 is detected in all three sources; 19/41 (46.34 %) in skin,
12/40 (30 %) in nasal and 8/48 (16.66 %) in hospital isolates.
This strain code analysis can be a useful method to quickly recognize S. epidermidis strains.
Additionally, we can perform DNA sequencing to make the diagnosis more precise and accurate.
For example, if we only test 4 SNPS and find all of them positive in a sample (SC 2222), then after
DNA sequencing we can assign a new code which will also include information about bases
present at other positions in the gene. After such analysis, this code might look like 2G2T2T2A or
2A2A2G2T. This type of code will be a very easy and reliable way of recognizing a particular
strain.
Although ScreenClust HRM analysis is a useful program to divide samples into clusters after
analysing their HRM profiles, it has some limitations. It successfully divided samples into
different groups, as confirmed by “Difference Graph” analysis, however, this division is based on
HRM curve profiles rather than the genetic composition of the DNA region tested. For example,
samples C2 and C4 have the same nucleotide base (T‐Thymine) at the pyr129 position however
ScreenClust placed them into two separate groups. In another case, C1 (G‐ Guanine) and C2 (T‐
Thymine) were assigned to the same cluster by the software but they have different bases at this
particular SNP position. This is occurring due to the presence of additional SNPs close to the SNP
interrogated in this experiment. The presence of these SNPs also affects the shape of the HRM
curve. ScreenClust places samples into various groups based on curve parameters, and
therefore, DNA sequencing has to be performed to validate the nucleotide base present at a
particular position (Wittwer et al., 2003; Ghorashi et al., 2011).
In conclusion, it is evident that HRM‐SNP analysis is a useful tool to genotype S. epidermidis
isolates. This method is rapid, simple and quick to perform and can provide specific genetic
information relating to the strain characteristics of both clinical and non‐clinical S. epidermidis
strains.
91
CHAPTER4icaOPERONANDANTIBIOTICRESISTANCEGENES
92
4.1INTRODUCTION
Intercellular adhesion (ica) genes are important determinants of S. epidermidis pathogenicity.
The presence of ica in S. epidermidis plays an important role in its survival in the hospital setting
and ica‐positivity is prominent in S. epidermidis strains isolated from such environments
(Kozitskaya et al., 2005). icaA, icaD, icaB and icaC are components of the ica locus and a fifth
part, the icaR gene, is a repressor gene regulating the icaADBC operon expression and,
therefore, regulates biofilm formation and PIA synthesis. PIA production also involves a trans‐
membrane protein, the icaC protein (Rohde et al., 2006; Conlon et al., 2002).
Ziebuhr et al. (1997) compared the presence of ica genes in pathogenic S. epidermidis and
saprophytic strains and found significant differences in results as 85 % of S. epidermidis isolates
had the ica cluster, whereas this cluster was found in only 6 % of the saprophytic strains. In
another study by Galdbart et al. (2000), the ica operon was found in 81.5 % of clinical isolates,
out of which about 63 % (62.9) were also positive for biofilm formation as well. Frebourg et al.
(2002) found the presence of ica genes in 77 % of blood borne isolates and only 38 % of
commensals were positive.
The production of PIA is related to the presence of the ica operon. ica controls the synthesis of
specific enzymes which in turn are responsible for PIA synthesis (Mack et al., 1994; Heilmann et
al., 1996). Interestingly, Gad et al. (2009) studied the presence of icaA and icaD genes and
biofilm production in staphylococci and found all biofilm producing strains to be positive for
both ica genes and both of these genes were absent in those strains which were not biofilm
producers.
Although there is a link between ica genes and biofilm formation (Fluckiger et al., 2005)
researchers such as Qin et al. (2007) have detected a population of staphylococci which are
biofilm producers but are ica‐negative. Fitzpatrick et al. (2002) also presented a view that ica
presence per se is not required for biofilm formation. Diemond‐Hernández et al. (2010) also
found ica operon‐negative staphylococci producing biofilms. Moreover, PIA production, which is
usually associated with biofilm formation, is solely not sufficient to synthesize a biofilm as Chokr
et al. (2006) have described a biofilm production mechanism in staphylococci which is not
dependent upon PIA. Hennig et al. (2007) described biofilm production as a process which
depends upon multiple factors and can occur in the absence of PIA. Alternatively, biofilm
formation is mediated by a protein “Biofilm Associated Protein (Bap)” (McCann et al., 2008).
93
This ica operon has also been associated with antibiotic resistance (Heilmann et al., 1997). The
presence of biofilm plays an important and significant role in antibiotic resistance, as it provides
a safe environment for bacteria against antimicrobials by limiting their access to bacterial cells
covered by biofilm (Yao et al., 2005). While studying the mechanism of treatment failure in
staphylococcal infections, Diemond‐Hernández et al. (2010) mention that the presence of icaA,
icaD and an insertion sequence element (IS256) in a strain makes it more resistant and is
strongly related to treatment failure. Experimentally, exposure to harmful substances such as
antimicrobials results in an increased production of PIA and biofilm (Schlag et al., 2007).
Mohammad et al. (2011) also reported the presence of increased resistance amongst slime
producing bacteria. Production of biofilm reduces penetration and access of antibacterial
components such as complement, antibodies and various antibiotics to bacteria, hence
decreasing the activity of these substances against bacteria (Costerton, 2005).
The development of resistance to multiple drugs in S. epidermidis is important and is related to
biofilm production and its growth pattern (Donlan, 2000). Hospital cultures of microbes show
more resistance than those arising from public places (Archer and Armstrong, 1983: Cove et al.,
1990). The current practice of prescribing antibiotics, including antibiotic‐prophylaxis, is an
important reason for the emergence of such strains (Sanchez et al., 1992). In this study, clinical
CoNS strains and those isolated from healthy individuals were screened for the presence of each
of the ica genes (ADBC).
4.2icaOPERON
4.2.1Aim3
The aim of this analysis was to detect and compare the presence of the ica genes (ADBC) in
isolates obtained from three different sources: (i) hospital patients, (ii) skin and (iii) nasal
mucosa of healthy individuals. As the presence of the ica operon is important in pathogenesis
and virulence of CoNS, it is important to compare the presence or absence of it in clinical and
non‐clinical specimens.
94
4.2.2Methods
Conventional PCR was performed on all isolates from patients and healthy individuals. This was
followed by agarose gel (2 %) electrophoresis. A 100 bp DNA molecular weight marker (Bioline®,
Australia) was used for comparison and analysis. Primers, PCR conditions and agarose gel details
are given in Chapter 2, Section 2.5. Figure 4.1 is an example of an agarose gel of PCR products for
the IS256 and icaD genes.
Figure 4.1: Electrophoresis of PCR products for IS256 and icaD genes (Diemond‐Hernández et al.,
2010).
4.2.3Results
The following figure and tables show the results for the detection of the ica genes in isolates
from all three sources; patients, skin and nasal mucosa of healthy individuals. Figure 4.2 shows
the gel electrophoresis result for the icaD gene. There are two icaD‐ positive samples S.
epidermidis isolates.
95
Figure 4.2: A 2 % agarose gel showing the presence of the icaD gene in two S. epidermidis
isolates.
Table 4.1 shows the number of samples that tested positive for each of the four ica genes
tested. Values of positive samples are displayed in both numbers and percentages.
Table 4.1: Comparison of each ica gene positivity
Sources of
isolates
Total number of
isolates
icaA(+ve) icaB(+ve) icaC(+ve) icaD(+ve)
Patient 48 12 (25 %) 10 (20.83 %) 16 (33.33 %) 14 (29.16 %)
Skin 41 8 (19.51 %) 8 (19.51 %) 10 (24.39 %) 8 (19.51 %)
Nasal 40 6(15 %) 7(17.5 %) 13 (32.5 %) 8 (20 %)
TOTAL 129 26 (20.15 %) 25 (19.37 %) 39 (30.23 %) 30 (23.25 %)
100bp DNA molecular weight
Positive sample
96
Table 4.2 is a comparison of two important combinations of ica genes. Both numbers and
percentage values are shown for each group.
Table 4.2: Comparison of ica gene combinations in isolates
Source of
isolates
Total number
of isolates
Presence of ica A and D Presence of all ica operon genes (ADBC)
Patient 48 10 (20.83 %) 8 (16.66 %)
Skin 41 8 (19.51 %) 7 (17.07 %)
Nasal 40 6 (15 %) 6 (15 %)
TOTAL 129 24 (18.60 %) 21 (16.27 %)
Table 4.3 is a summary of the presence of each individual ica gene in clinical vs. non‐clinical
strains.
Table 4.3: Summary of individual ica genes present in clinical vs. non‐clinical isolates
Source of isolates Total number
of isolates
icaA(+ve) icaB(+ve) icaC(+ve) icaD(+ve)
Patients (Clinical) 48 12 (25 %) 10 (20.83 %) 16 (33.33 %) 14 (29.16 %)
Skin and Nasal
(Non‐Clinical)
81 14 (17.28 %) 15 (18.51 %) 23 (28.39 %) 16 (19.75%)
TOTAL 129 26 (20.15 %) 25 (19.37 %) 39 (30.23 %) 30 (23.25 %)
97
Table 4.4 compares the presence of the icaA and icaD genes as well as the presence of all four
ica genes in clinical vs. non‐clinical isolates.
Table 4.4: Comparison of ica gene combinations in isolates from clinical and non‐clinical
sources
Source of isolates Total number of
isolates
Presence of ica A
and ica D
Presence of all ica operon
genes (ADBC)
Patients (Clinical) 48 10 (20.83 %) 8 (16.66 %)
Nonclinical(Skin and
Nasal)
81 14 (17.28 %) 13 (16.05 %)
TOTAL 129 24 (18.60 %) 21 (16.27 %)
The gel electrophoresis images for all the isolates tested are included in Appendix II, Section 2.
4.3ANTIBIOTICRESISTANCEGENES
4.3.1Aim3
The purpose of this aim was to determine the presence of antibiotic resistance genes in CoNS
strains isolated from hospital patients and healthy individuals.
4.3.2Methods
The presence of each respective antibiotic gene was determined by HRM analysis of the PCR
amplicons. This was done by using the Rotor‐Gene 6000 series software. All isolates were
compared to the HRM melt curves of known positive controls for each antibiotic resistance
gene. Primers, positive controls and PCR conditions are described in Chapter 2, Section 2.6.
Figure 4.3 shows the normalised HRM curve analysis of a positive control and two samples. The
difference between matching and non matching curves is evident in this picture.
98
Figure 4.3: Normalised melt cure analysis comparing a positive control to two unknown isolates.
Sample (positive)
Positive control
Sample (negative)
99
Figure 4.4 shows the difference graph analysis of the same three samples in Figure 4.2. Samples
which have normalized minus control values between the ranges of +5 and ‐5 compared to the
positive control are considered as “same” or positive while sample values outside this range are
regarded as “different” or negative.
Figure 4.4: Difference graph analysis comparing a positive control to two unknown isolates.
4.3.3Results
The presence of four antibiotic resistance genes was tested in 129 CoNS isolates. Table 4.5
provides a summary of positive isolates from different sources.
Sample (positive)
Sample (negative)
Positive control
100
Table 4.5: The presence of resistance genes in isolates from various sources
Genes Patient isolates Skin isolates Nasal isolates Total Positive
isolates
Total
isolates
Positive
isolates
Total
isolates
Positive
isolates
Total
isolates
Positive
isolates
mecA 48 4 41 0 40 0 4/129
vanA
(Vancomycin)
48 0 41 0 40 0 0/129
vanB
(Vancomycin)
48 1 41 0 40 0 1/129
gyrA
(Ciprofloxacin)
48 0 41 0 40 0 0/129
acc(6’)‐aph(2’)
(Gentamicin)
48 4 41 2 40 0 6/129
The HRM melt curves for each antibiotic gene is included in Appendix II, Section 1.
4.4DISCUSSION
4.4.1icaOperon
In total, 129 isolates were tested for the presence of the ica genes (ADBC). Of these, 48 isolates
were from patients who had septicaemia (clinical isolates) while the remaining 81 isolates were
from nonclinical sources. Nonclinical samples were obtained from two different sources. 41
isolates were cultured from the skin of healthy individuals whereas the remaining 40 samples
were obtained from nasal mucosa of healthy individuals.
The presence of the ica genes plays an important role in the pathogenicity of S. epidermidis and
other CoNS (Kozitskaya et al., 2005) and are involved in the production and expression of various
101
structures such as PIA, biofilm and autolysin (Galdbart et al., 2000; Heilmann et al., 1997). We
tested both clinical and nonclinical isolates for the presence of the ica operon. In total, 48 clinical
isolates were tested. 25 % (12/48) tested positive for the presence of icaA, 20.83 % (10/48) for
icaB, 33.33 % (16/48) for icaC and 29.16 % (14/48) for icaD. When testing for the presence of the
complete set of all four ica genes (i.e. icaA, icaB, icaC and icaD), we found 16.66 % (8) of samples
positive for this combination. The presence of icaA along with icaD was observed in 28.83 % (10)
of clinical samples and this combination is related to more resistance and treatment failures
(Diemond‐Hernández et al., 2010). They detected the presence of both genes in 22 out of 45 S.
epidermidis samples. Moreover, 21 samples also tested positive for IS256 genes. Overall,
positivity for IS256 and icaA plus icaD presence put patients at 2.24‐13.44 times more risk of
treatment failure. Diemond‐Hernández et al. (2010) noticed that the presence of combination of
ica genes especially icaA plus icaD, is related to more cases of treatment failure.
From the 41 skin isolates, the same percentage of isolates i.e. 19.51 % (8/41) had icaA, icaB and
icaD present while 24.39 % (10/41) had the icaC gene present. The complete set of all four ica
genes was detected in 17.07 % (7/41) of samples while the presence of the combination of both
icaA and icaD was found in 19.51 % (8/41) of isolates.
Nasal samples also tested positive for icaA (15 %), icaB (17.5 %), icaC (32.5 %) and icaD (20 %).
When we assess the presence of all four genes, 15 % (6/40) nasal isolates were positive and the
icaA and icaD combination was also present in the same percentage.
Overall, ica‐positive isolates were found in strains originating from each of the three different
sources, although the number of positive isolates and their percentages differ in samples from
each source. By observing individual gene positivity in all isolates, icaC is present in the highest
percentage followed by icaD, icaA and icaB respectively. In clinical isolates, icaA is present in
12/48 (25 %) of isolates while in non‐clinical isolates it is present in a low percentage, 14/81
(17.28 %). For icaB, clinical isolates have 10/48 (20.83 %) positive results and non‐clinical isolates
have 15/81 (18.51 %). Similarly, icaC is also present at a higher percentage in clinical isolates
compared to non‐clinical isolates i.e. 16/48 (33.33 %) versus 23/81 (28.39 %) respectively. The
highest percentage difference is observed in the case of icaD, which shows a difference of
almost 10 % between clinical and non‐clinical isolates. icaD was found in 14/48 (29.16 %) of
clinical isolates while only 16/81 (19.75 %) of non‐clinical isolates had the icaD gene.
102
Conversely, we compared the presence of two combinations of these genes. First, we looked at
icaA plus icaD genes. Once again clinical isolates dominated non‐clinical isolates. Simultaneous
presence of these two genes was detected in 10/48 (20.83 %) of clinical isolates and in 14/81
(17.28 %) of non‐clinical ones. Secondly, we compared the presence of all four of these genes
present together. Interestingly, there was not much difference in clinical versus non‐clinical
isolates. The complete set of these genes (icaADBC) was found in 8/48 (16.66 %) in clinical
isolates while 13/81 (16.05 %) non‐clinical samples contained the full set of genes.
Overall, the presence of individual and combinations of ica operon genes is higher in clinical
isolates compared to skin and nasal samples.
4.4.2Antibioticresistancegenes
In total, five antibiotic resistance genes were tested using Real‐time PCR followed by HRM
analysis. As this study was mainly focussing on SNPs analysis, so for detailed antibiotic resistance
profile testing in CoNS, it will be required to test more resistance genes and additional resistance
studies. Only eleven out of 129 samples tested positive for at least one of these genes. Four
clinical isolates tested positive for the mecA gene (carrying resistance for methicillin) presence
while this gene was absent in both skin and nasal samples. The vanB gene was only found in one
isolate from a patient (clinical) source and both skin and nasal samples did not have the vanB
gene. vanA was not detected in any of the isolates tested. Similarly, none of the isolates tested
were positive for the gyrA gene. This gene determines resistance to ciprofloxacin. Lastly, the acc
(6’)‐aph(2’) gene, which is associated with gentamicin resistance, was detected in four patient
isolates and two skin isolates.
In summary, both clinical and non‐clinical isolates were found to harbour the ica genes,
responsible for biofilm production, and the combination of the icaA+D genes in both clinical and
non‐clinical isolates, is of concern. This combination of ica genes has been linked to patient
treatment failure previously. In addition, the presence of the acc(6’)‐aph(2’) gene in a non‐
clinical isolate is of concern and warrants further investigation. Although, currently gentamicin is
not a routine antibiotic for treatment of infections by CoNS but the presence of such resistance
genes in non‐clinical isolates is significant and it will be useful to test for the presence of these
genes which are related to other antibiotic resistance used routinely.
103
CHAPTER5
GENERALCONCLUSIONS,
LIMITATIONSANDFUTUREDIRECTIONS
104
The staphylococci are important pathogenic bacteria responsible for a range of diseases in
humans. They are broadly divided into two groups; Coagulase‐positive staphylococci and
Coagulase‐Negative Staphylococci (CoNS). Clinically, CoNS are emerging as an important
pathogenic group of staphylococci. Currently, CoNS are considered as important pathogenic
group involved in nosocomial infections, particularly infections related to prosthetic materials
and surgical wounds. Other than this, these microbes also cause mastitis and cardiac and
neonatal intensive care unit infections. The hospital acquired infections and antibiotic‐resistant
strains attributed to this group have become endemic in hospitals in many countries. Moreover,
the emergence of new strains which cause severe community‐acquired infections in immune‐
compromised as well as healthy people is clinically very important.
These bacteria have important virulent factors that enable them to form biofilms. Biofilm layers
have complicated chemical and structural consistency. Biofilms provide a safe environment for
growth and survival of bacteria by limiting access of harmful molecules such as antibiotics and
immune system products and cells. Moreover, the emergence of resistant strains is a noticeable
issue in these organisms. This resistance is present due to mutations or acquisition of resistant
genes and formation of biofilm. Intercellular adhesion (ica) genes are present in CoNS (icaADBC
operon). These genes (icaA, icaD, icaB and icaC) have a significant role in biofilm formation.
We have found ica positivity in samples originating from clinical and non‐clinical sources. This is
very important because, as mentioned earlier, ica genes control the production of biofilm, which
is the main virulence factor of CoNS. In this study, only a few antibiotic resistance genes were
found. Moreover, two skin isolates (originating from healthy individuals) were resistant to
gentamicin. This is important clinically as skin is regarded as a potential source for the
introduction of CoNS to cause various infections. This means that if someone carries an
antibiotic‐resistant strain(s) on their skin, this can become a source of CoNS infection in these
individuals or can be transmitted to others, particularly in the hospital environment.
Genotyping is becoming an important and reliable method of diagnosing various conditions.
High Resolution Melt Analysis (HRMA) is an emerging technique used to differentiate and
analyse different species/strains of various micro‐organisms. RT‐PCR HRMA was used to analyse
a set of eight SNPs in 129 isolates. The results obtained are very encouraging, as this method
was able to differentiate between genotypes of clinical and non‐clinical S. epidermidis strains.
After analysing the eight SNPs for each sample, the data was presented as a strain code. This
strain code separated 31 different SNP combinations, indicative of 31 types of S. epidermidis
105
isolates. In addition, HRMA was also performed using the “Rotor‐Gene ScreenClust HRM
Software”. This software separates samples into clusters or groups by analysing their HRM
profiles. Overall, the ScreenClust software program showed a good concordance with the
difference HRM curve analysis.
This technique is very useful but there has some limitations. Firstly, when ScreenClust separates
isolates into clusters, it observes only HRM curve profiles rather than the genetic composition of
the DNA region tested. This means that isolates categorised into the same cluster can have
different nucleotide bases present at a particular SNP position, or isolates with the same
nucleotide base at a particular SNP position can be classified into a different cluster. A likely
explanation for this is that if other SNPs are also present in the DNA segment that is being
interrogated, and then curve profiles could look similar to each other irrespective of the SNP
interrogated at only one position. This can be referred to as a “Cancelling” effect. We can avoid
this limitation by selecting a DNA segment that only contains a single SNP, or by doing additional
investigation such as DNA sequencing. Secondly, it is not possible to compare data from one
sample batch to another batch. This problem might be resolved by an improvement in the
software capacity or by creating an option to compare inter‐batch samples.
Adapting preventive measures to avoid CoNS infections should be our main focus clinically. As
infections are mostly transferred through instruments and procedures, there is a need to
practice sterile procedures strictly and use proper instruments. Health care workers should be
offered regular training and awareness programs related to such infections. Moreover, if there is
a case of such infections, it should be treated properly to avoid serious complications.
CoNS growth in laboratories should not be dismissed as a result of contamination. It should be
investigated and ruled out by repeating tests. There is still a need to establish a relationship
between the role of CoNS as normal skin flora and their pathogenic nature. We need to have
more detailed studies involving investigations from the hospital environment in particular. One
such study could involve the isolation of CoNS from skin and nasal mucosa of medical workers,
ward surfaces, operation theatre surfaces and from infected patients. The novel HRMA
genotyping method described in this study has the potential to facilitate such investigations.
106
107
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