exploring humoral responses during staphylococcus aureus · sak – staphylokinase sa4ag –...
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I
Exploring humoral responses during Staphylococcus aureus
infection by immunoproteomics
Inaugural - Dissertation
zur
Erlangung des akademischen
Grades
Doktor der Wissenschaften in der Medizin
(Dr. rer. med.)
der
Universitätsmedizin
der
Ernst-Moritz-Arndt-Universität
Greifswald, 28.02.2017
vorgelegt von Nandakumar, Sundaramoorthy
geboren am 26.06.1980
in Cuddalore, Tamilnadu, India.
II
Aus der Abteilung für Funktionelle Genomforschung des Interfakultären Instituts für Genetik
und Funktionelle Genomforschung der Universitätsmedizin der Ernst-Moritz-Arndt-Universität
Greifswald
(Leiter Univ. Prof. Dr. Uwe Völker)
Dekan: Prof. Dr. Max P. Baur
1. Gutachter: Prof. Dr. Uwe Völker
2. Gutachter: Prof. Dr. Barbara Bröker
Ort, Raum: Seminarraum J04.33/34, Klinikum DZ 7, Greifswald
Tag der Disputation: 12.07.2017
III
Table of contents
Abbreviations ........................................................................................................................... VI
Introduction ............................................................................................................................... 1
Staphylococcus aureus ....................................................................................................................... 1
Human immune system .................................................................................................................... 2
Antibodies - adaptive immune system ........................................................................................ 3
Biological properties of human immunoglobulins ................................................................. 5
S. aureus nasal carriage .................................................................................................................... 6
Determinants of S. aureus nasal carriage ................................................................................... 8
S. aureus bacteraemia (SAB) ........................................................................................................... 9
Pathogenesis pattern of S. aureus – host-pathogen interaction ..................................... 11
S. aureus vaccine candidates ........................................................................................................ 14
Immunoproteomics: From individual sample analysis to high throughput .............. 19
Aim of the study ................................................................................................................................ 21
Thesis graphical abstract .............................................................................................................. 23
Materials and Methods ......................................................................................................... 24
Materials ............................................................................................................................................. 24
Materials required for overexpression ................................................................................... 24
List of chemicals ............................................................................................................................... 24
List of consumables ......................................................................................................................... 26
List of instruments .......................................................................................................................... 26
Media and antibiotics ..................................................................................................................... 26
Buffers and solutions ...................................................................................................................... 27
List of databases ............................................................................................................................... 29
List of software ................................................................................................................................. 29
List of recombinant S. aureus antigens .................................................................................... 30
Overview of study population ..................................................................................................... 30
Methods ............................................................................................................................................... 33
Overexpression of recombinant S. aureus antigens ............................................................ 33
IV
Mass spectrometry analysis of purified recombinant S. aureus antigens ................... 34
Mass spectrometry data analysis of the purified recombinant proteins .................... 36
Principle of FLEXMAP 3D® and workflow ............................................................................... 36
Covalent conjugation of S. aureus antigens to magnetic beads ....................................... 38
Evaluation of magplex bead-antigen conjugation ................................................................ 39
Pre-adsorption of sera with assay buffer containing E. coli lysate ................................ 39
Serological bead based assay method validation ................................................................. 40
Quantitation of antibodies by serological assay using FLEXMAP 3D® .......................... 40
Data analysis ...................................................................................................................................... 41
Calculating the intensity at half maximum by Clark’s theory .......................................... 41
Scaling of data ................................................................................................................................... 45
Mann-Whitney U test ...................................................................................................................... 45
Statistical analyses .......................................................................................................................... 45
Results ........................................................................................................................................ 46
Method optimization and calibration ....................................................................................... 46
Mass spectrometry based quantification of S. aureus and E. coli proteins ................. 47
Method validation ............................................................................................................................ 48
Stability of antigens immobilised on the magplex ............................................................... 52
Evaluation of magplex-antigen coupling (coupling control) ........................................... 53
Quantitation of antibody responses (antigen coupling) .................................................... 60
Study-1 – Humoral responses during S. aureus nasal colonisation ............................... 60
IgG responses during S. aureus nasal colonisation ............................................................................................ 60 Discrimination efficiency of antigens provoking different immune responses in carriers and
non-carriers ......................................................................................................................................................................... 63 Characteristics of immunogenic antigens .............................................................................................................. 64 Clonal complex influence in anti-TSST-1 IgG responses ................................................................................. 68 Summary of study-1 ........................................................................................................................................................ 71
Study-2 – Humoral responses during onset of S. aureus bacteraemia infection ....... 72 IgG responses during onset of S. aureus bacteraemia ...................................................................................... 72 Discrimination efficiency of antigens provoking different immune responses in control and
sepsis ...................................................................................................................................................................................... 74 Characteristics of immunogenic antigens .............................................................................................................. 78 Summary of study-2 ........................................................................................................................................................ 88
Study-3 – Specific serum IgG at diagnosis of S. aureus bloodstream invasion is
correlated with disease progression ........................................................................................ 89
V
IgG responses during S. aureus bacteraemia ........................................................................................................ 89 Validation of antigens provoking significantly different immune responses in patients with
sepsis and without sepsis .............................................................................................................................................. 91 Characteristics of immunogenic antigens .............................................................................................................. 95 Summary of study-3 ........................................................................................................................................................ 96
Discussion ................................................................................................................................. 97
Immunoproteomics – Method establishment and optimisation .................................... 97
Study-1: Immune responses during nasal colonisation ..................................................... 99
The influence of clonal complexes on sample specific IgG levels ............................................................. 101
Study-2: Immune responses during onset of bacteraemia ............................................. 102
Study-3: Immune responses during complicated bacteraemia .................................... 106
Conclusion and Outlook .................................................................................................... 108
Publications .......................................................................................................................... 110
References ............................................................................................................................. 112
Summary ................................................................................................................................ 135
Acknowledgements ............................................................................................................ 137
Appendix ................................................................................................................................ 139
Eidesstattliche Erklärung ................................................................................................. 140
VI
Abbreviations
1-D PAGE – One dimensional poly acrylamide gel electrophoresis
2-D PAGE – Two dimensional poly acrylamide gel electrophoresis
2-D IB – Two dimensional immunoblotting
Å – Angstrom
ABC – ATP binding cassette
Ab – Antibody
ACN – Acetonitrile
ADCC – Antibody-dependent cell-mediated cytotoxicity
Ag – Antigen
agr – Accessory gene regulator
AIP – Auto inducing peptide
AMP – Anti-microbial peptides
APC – Antigen presenting cell
Atl – Autolysin
Aur –Aureolysin
B cell – Bone marrow cell
BCR – B cell receptor
BSA – Bovine serum albumin
BSI – Bloodstream infection
CA-MRSA – Community-acquired MRSA
VII
CBS – Carboxy block store buffer
CBB – Coomassie brilliant blue
CC – Clonal complex
CCD – Colloidal coomassie dye
CCS – Colloidal coomassie solution
CD – Cluster of differentiation
CDC – Complement dependent cytotoxicity
CHIPS – Chemotaxis inhibitory protein
ClfA – Clumping factor A
ClfB – Clumping factor B
Cna – Collagen adhesin gene
Coa – Coagulase
CP – Capsular polysaccharides
CXCR – Chemokine receptor
DNA – Deoxyribonucleic acid
DTT – Dithiothreitol
Eap – Extracellular adherence protein
EB – Epidermolysis bullosa
E. coli – Escherichia coli
EDTA – Ethylendiaminotetra acetic acid
EDC – 1-Ethyl-3-[3-dimethylaminopropyl] carbodiimide hydrochloride
VIII
Efb – Extracellular fibrinogen binding protein
egc – Enterotoxin gene cluster
e.g. – For example
ELISA – Enzyme-linked immunosorbent assay
E-MRSA – Epidemic-MRSA
EntA – Enterotoxin A
Emp – Extracellular matrix binding protein
Eno – Enolase like protein or phosphopyruvate hydratase
EpiP – Intracellular serine protease
et al. – “Et alia: and others
FadB – 3-hydroxyacyl-CoA dehydrogenase protein
Fab region – Fragment antigen-binding region
Fc region – Fragment crystallisable region
Fb – Fibrinogen
Fbp – Fibrinogen binding protein
FC – Fold change
Fnbp – Fibronectin binding protein
Fn – Fibronectin
FPR – Formyl peptide receptors
FPRL-1 – Formyl peptide receptor-like 1 inhibitory protein
Fur – Ferric uptake regulator
IX
Geh – Triacylglycerol lipase
GlpQ – Glycerophosphoryldiester-phosphodiesterase
GraB – Truncated secreted von Willebrand factor-binding protein
GreA – Transcription elongation factor
GroEL – Chaperonin GroEL
HA-MRSA – Hospital-acquired MRSA
HCA-MRSA – Healthcare-acquired MRSA
HEK – Human embryonic kidney
HF – Healthy fellows
HIS – Histidine
Hla – Alpha-hemolysin
Hlb – Beta-hemolysin
Hld – Delta-hemolysin
Hlg – Gamma-hemolysin
H3PO4 – Ortho-phosphoric acid
HNP – Human neutrophil peptide
HysA – Hyaluronate lyase
IAA – Iodoacetamide
ICAM – Immunoglobulin-related cell adhesion molecules
i.e. – That is
IE – Infective endocarditis
X
IEF – Isoelectric focusing
Ig – Immunoglobulin
IL – Interleukin
IMAC – Immobilised metal affinity chromatography
IPTG – Isopropyl beta-D-thiogalactopyranoside
Isd – Iron surface determinant
Isa – Immunodominant staphylococcal antigen
IVIG – Intravenous immunoglobulin
Ka – Association constant
Kd – Dissociation constant
Keq – Equilibrium constant
kDa – Kilodalton
LB – Luria bertani
LCB – Low cross buffer
LC-MS – Liquid chromatography-mass spectrometry
LtaS – Lipo-teichoic acid synthase
LTQ – Linear trap quadrupole
Luk – Leukotoxin
LytM – Peptidoglycan hydrolase
MAP – Multi analyte profiling
Map-W – MHC class II analog protein
XI
MES – 2(N-Morpholino) ethanesulfonic acid hydrate
MFI – Median fluorescence intensity
MgCl2 – Magnesium chloride
MHC – Major histocompatibility complex
MntC – Manganese transport protein C
MMP – Matrix metalloproteinase
MODS – Multiple organ dysfunction syndrome
MRSA – Methicillin-resistant Staphylococcus aureus
MSCRAMM – Microbial surface components recognising of adhesive matrix molecules
MsrA2 – Peptide methionine sulfoxide reductase A
MsrB – Peptide methionine sulfoxide reductase B
MS – Mass spectrometry
MSSA – Methicillin susceptible Staphylococcus aureus
NA – Not applicable
NaCl – Sodium chloride
NaOH – Sodium hydroxide
NCTC – National clinical trials consortium
NEAT – Near iron transporters
NHS – N-hydroxysulfosuccinimide
Ni-NTA – Nickel nitrilotriacetic acid
No. – Numero
XII
Nuc – Thermonuclease
OatA – O-acetyltransferasse
PAGE – Polyacrylamide gel electrophoresis
Pbp2 – Penicillin-binding protein 2
PBS – Phosphate buffered saline
PCA – Principal component analysis
PE – Phycoerythrin
PFGE – Pulsed field gel electrophoresis
pH – Potentia hydrogenii (Latin)
pI – Isoelectric point
Plc – 1-phosphatidylinositol phosphodiesterase
PLS – Partial least square
PMN – Polymorphonuclear neutrophils
PrkC – Serine/threonine-protein kinase
PrsA – Foldase protein A
PSM – Phenol soluble modulins
PurA – Adenylosuccinate synthetase
PVDF – Polyvinylidene fluoride
PVL – Panton-valentine leukocidin
RIA – Radio-immunoassay
ROS – Reactive oxygen species
XIII
RPE – R-phycoerythrin
RPLC – Reverse phase liquid chromatography
Rpm – Revolution per minute
RSD – Relative standard deviation
RT – Room temperature
SAB – S. aureus bacteraemia
SABSI – S. aureus bloodstream infection
SAT – Suspension array technology
Sak – Staphylokinase
SA4Ag – Staphylococcus aureus 4-Antigen
SAg – Super antigen
SasG – Surface protein G
S. aureus – Staphylococcus aureus
SB – Super broth
Sbi – Immunoglobulin binding protein
SCIN – Staphylococcal complement inhibitor
SDS – Sodium dodecyl sulphate
Sdr – Serine-aspartate repeat
SE – Staphylococcal enterotoxin
SERAM – Secretable expanded repertoire adhesive molecules
SERPA – Serological proteome analysis
XIV
SigB – RNA polymerase sigma factor B
SIRS – Systemic inflammatory response syndrome
Sod – Superoxide dismutase
Spa – Staphylococcal protein A
Spl – Serine proteases
SsaA – Staphylococcal secretory antigen
Ssl – Staphylococcal superantigen like protein
SSTI – Skin and soft tissue infection
Ssp – Staphopain
STREP – Streptavidin
T cell – Thymus cell
TCA – Tricarboxylic acid cycle
TCR – T cell receptor
TFA – Trifluoroacetic acid
TH – Helper T cell
Tig – Trigger factor
TNF – Tumor necrosis factor
Tris – Tris (hydroxymethyl) aminomethan
Tsst-1 – Toxic shock syndrome toxin-1
Tuf – Elongation factor
VISA – Vancomycin intermediate Staphylococcus aureus
XV
VRSA – Vancomycin resistant Staphylococcus aureus
VH – Variable region of heavy chain
VL – Variable region of light chain
Vn – Vitronectin
vWF – Von Willebrand factor
WTA – Wall teichoic acid
(m/v)/ (v/v) – (mass to volume)/ (volume/volume)
XVI
1
Introduction
Staphylococcus aureus
Staphylococcus (in Greek, staphyle means grapes; coccus means grain or berry) was first
described by the Scottish surgeon Sir Alexander Ogston in 1880 [1,2]. In 1884, the
German physician Friedrich Julius Rosenbach observed two isolates of Staphylococcus
and differentiated the isolates based on the colour of colonies into Staphylococcus aureus
(S. aureus) in Latin aurum, gold and
Staphylococcus albus (S. albus) in Latin albus,
white [3]. Mainly, Staphylococcus has two
species Staphylococcus aureus and
Staphylococcus epidermidis. These organisms
are Gram-positive species, non-motile, non-
spore forming cocci and appear as clusters. S.
aureus is one of the most important pathogens
in humans, has a wide spectrum of clinical
manifestations (Figure 1) and remains a
frequent cause of morbidity and mortality.
S. aureus leads to infections ranging from
relatively mild skin infections and soft tissue
infections, to more severe diseases like
pneumonia, surgery-related and bloodstream
infections [4]. Despite hospital-acquired
infections, S. aureus also causes community-
acquired infections such as osteomyelitis,
septic arthritis and skin infections [5]. S.
aureus has been ranked as the second
pathogen among the nosocomial
hematogenous infections and increases the mortality, morbidity, hospitalisation, and
expenses [6–9]. In the last 50 years, various S. aureus clones were disseminated
worldwide and developed strong resistance to antibiotics. S. aureus has the ability to
Figure 1. Clinical manifestation of S. aureus.
Picture adapted from (Wertheim et al., 2005)
2
resist acid stress upon exposure to sub lethal acidic pH 4.5, exhibits thermo tolerance for
up to 48°C, and shows resistance responses to alkaline media [10]. Penicillin was the first
among the antibiotics discovered by Alexander Fleming, and at that time S. aureus had
exquisite susceptibility to it. However, within few years after penicillin discovery,
penicillinase-producing S. aureus clones were found in the 1950s and began spreading
around the world, causing hospital and community-acquired infections [11]. Later, first
semi-synthetic penicillinase-resistant beta-lactam methicillin was introduced in 1959,
methicillin-resistant S. aureus appeared in 1961 [12]. These methicillin-resistant clones
were disseminated in healthcare units throughout Europe until 1970s [13]. Vancomycin, a
glycopeptide type antibiotic is the last resort antibiotic for S. aureus treatment. Later, in
1997 partially vancomycin-resistant S. aureus clones were isolated [14,15]. S. aureus is
highly adaptable and developed strong resistance to all antibiotics developed over the last
decades. S. aureus is exceptional in its ability to develop resistance to any antibiotics due
to spontaneous mutation and horizontal gene transfer [16]. S. aureus possesses two types
of behaviours. Normally, S. aureus leads to commensal asymptomatic stage, which is
present in about 30% of humans, which are carriers of S. aureus in the anterior nares. On
the other hand in an acute stage, S. aureus invades tissue with ensuing pathogenesis
[4,17]. S. aureus nasal carriage is the major risk for the development of S. aureus
infections in various clinical backgrounds [15,18]. Majorly, these infections are of
endogenous origin in which hosts are infected with their own S. aureus isolates [19–21].
Human immune system
The immune system is the basic system among the diverse biological structures of the
human organism/body that protects against disease. The immune system must detect a
wide spectrum of foreign agents known as pathogens, and the events leading to the
development of immunity directed against pathogens are extremely complex. The
immune system can be classified as an innate immune system and an adaptive immune
system that act in concert as well as separately in the development of immunity. The
innate immune system provides the first line of defence against a pathogen. It is non-
specific, rapid, lacks immunologic memory generally of short duration. The adaptive
immune system is highly specific, slower in development, exhibits immunological
memory, and long lasting. Innate and adaptive immune systems are discrete systems but
3
interact at several levels to develop a complete defence against invading pathogens. Both
systems follow mechanisms to distinguish self from non-self, therefore in normal
situations they are not directed against the host’s tissues and cells. A variety of
communications occurring between the human immune system and foreign substances
co-amplify the particular response as a result and further the invading pathogen undergos
destruction and eradication [22].
Antibodies - adaptive immune system
The adaptive immune system is a parts of the overall immune system. It is also called
acquired immune system or specific immune system. In contrast to the innate immune
system, the adaptive immune system possesses immunological memory, by using of
which it can block/ prevent the repeated damage by a foreign antigen or substance. The
adaptive immune system has both humoral and cell-mediated immunity components. The
cells that imports adaptive immune response are white blood cells known as
lymphocytes. The adaptive immune system can be divided in a T cell-mediated cellular
component and a B cell-mediated humoral component, which provide unique specificity
for their target antigens by the antigen-specific receptors expressed on their surfaces [23].
T cells can recognise the antigens only when these are presented in the processed form by
the MHC molecules of infected/cancerous cells. Helper T cells and Killer T cells are the
main components of T cells. Helper T cells controls the mobilisation of other cells to
confine the source of infected or malignant cells. Killer T cells can trace and directly kill
the infected host cells by the secretion of cytotoxins that form pores in the membrane of
the affected cells. B cells can recognise the antigens directly by their surface-associated
antibodies, which can bind to the specific antigen without the need of antigen processing.
Each B cell tends to make one specific antibody [23]. The B- and T-lymphocytes antigen-
specific receptors mature by somatic rearrangement of germ-line gene components to
form the TCR genes and the receptor genes of immunoglobulins. This recombination
mechanism is inimitable which has unique antigen receptors. It provides fast and specific
immune responses during the later exposure of the same antigen with specific
immunological memory. B lymphocytes produce immunoglobulins (Ig) to bind
pathogens, and T lymphocytes use T-cell receptors (TCR) to respond to antigen in the
form of processed peptides bound to cell surface proteins referred to as major
4
histocompatibility complex (MHC). MHC are the essential surface exposed proteins that
play a vital role in the adaptive immune system which recognise the foreign molecules.
There are three classes of MHC molecules, MHC class I, MHC class II and MHC class
III. The MHC class I interacts with CD8 molecules on the surface of cytotoxic T cells, in
turn destructs the host infected or malignant cells. The MHC class II interacts with CD4
molecules on the surface of the helper T cells (TH), which facilitates the establishment of
specific immunity/adaptive immunity. For T lymphocytes, it requires two molecules to
recognise an antigen. First, the MHC molecule binds to the processed antigen peptides;
second, the TCR binds to the MHC molecule-antigen peptide complex. This exhibition of
MHC molecules complexed with processed antigens on their surface is known as antigen
presentation and mediated by antigen presenting cells (APC).
Co-stimulatory molecules
Long-lived plasma cells
Naive
B cell
CD4
Th2
cell
MHC class IIT cell receptor
Antigen internalization via BCR
Pathogen
Plasma B cell
CD40L CD40
B cell receptor
Maturation
Antigen-specific
antibody
αβ
Figure 2. T cell and B cell collaboration leads to the production of antibodies
Antibody production is controlled by the interaction between CD4+ helper T cells and B
cells that express rearranged antigen-specific immunoglobulin on their cell surface
(Figure 2). Antigen specific antibody production is the primary function of B
lymphocytes against invading pathogens. From the repertoire of a few hundred germ line-
encoded gene elements it is possible to design millions of antigen receptors. Antibodies
are encoded by the heavy (H)- and light (L)-chain immunoglobulin (Ig) genes where VH
(variable), DH (diversity), JH (joining), and CH (constant) are gene elements of heavy
chain and VL, JL, and CL are the gene elements of light chain [24]. Antibodies are
produced by B lymphocytes either extracellularly or on cell surface. Nevertheless, there
are five major classes of immunoglobulins IgM, IgG, IgA, IgD and IgE further, IgG has
5
four subclasses and IgA has two subclasses. The subclasses are mainly based on the
isotypes of the H chain and the main classes and subclasses have different functions
which has been listed as Table 1. Each antibody is produced either as a circulating form
or a stationary form and here the later one consist of a hydrophobic transmembrane
domain induces the B-cell membrane to make immunoglobulin to act as a B-cell receptor
[25]. The former one which is a circulating form constitutes a considerable proportion of
serum proteins. About 10–15% of the peripheral blood lymphocyte population are in the
B cell.
During an encounter of a specific pathogen, an antibody response is initiated and the
level of antigen-specific antibodies rises in serum over a short period of time. However,
immunological memory is persevered in the B cell for the immediate clonal expansion
upon re-exposure to the same antigen [26]. Enhancement of protective immunity for a
specific antigen requires cooperation between B and T lymphocytes. T helper cell
interaction is required for the development of high-affinity antibodies and protection. It
requires CD4+ T helper cell stimulation for the naïve B lymphocytes to undergo
proliferation and differentiation in response to antigens. B lymphocytes are also capable
of presenting antigens to T cells through their surface MHC class II protein. Antibodies
identify the tertiary structure of proteins and bind to specific epitopes of the antigen [27].
Upon infection, the immune response is triggered, and immunoglobulin formation
undergoes flipping from one isotype to another, typically from IgM to IgG and IgA or
IgE. During this process, antibodies with higher affinity for the antigens are developed
[28].
Biological properties of human immunoglobulins
The primary function of Ig is to inactivate or eliminate the pathogen and its products via
antigen binding. The structure of the Ig is well adapted to this function. Ig is comprised of
a head (variable region - Fab), which binds to an antigen on the invading pathogen, and its
tail (constant region - Fc), which mediates the effects. Mainly, the Fc region interacts with
complement and or with specific receptors (gamma receptors) on the neutrophil and
monocyte surfaces. Human immunoglobulin-antigen interactions take place between the
paratope, the Ig region at which antigen binds and the epitope which is the region of the
antigen that is bound [29]. The four subclasses of IgG possess unique Fc regions. Based
6
on the in vitro studies, IgG2 is less efficient than IgG1 in fixing complement [30]. The Ig
comprised of several domains and these domains are associated with different biological
functions. The biological function of Ig is classified as antigen-dependent and antigen-
independent function listed in the Table 1 [31].
Table 1: Summarised characteristics of biological functions of human immunoglobulins [31]
S. aureus nasal carriage
Based on histological studies, the nasal vestibule has a complex epithelial surface and can
be categorised into five distinct regions. The vestibule region of the nose has a thin
squamous epithelium that is covered at the nostrils by short stiff hairs acting as receptors
for mechanical stimuli and induce sneezing. The primary ecological niche of S. aureus in
Property Antigen
dependence Type of human Ig Domain localisation
Complement fixation
Classical pathway + IgM, IgG CH4 of IgM, CH2 of
IgG
Alternate pathway - IgA, IgE Unknown
Opsonic activity
Neutrophils, monocytes + IgG1, IgG3, IgA Unknown
Mast-cell and basophil binding - IgE Unknown
Macrophage binding - IgG1, IgG3 CH3 of IgG
Lymphocyte binding - IgG1, IgG3, IgM CH3 of IgG
Placental passage regulation - IgG1, IgG2, IgG3, IgG4 Entire Fc required
Intestinal passage regulation - None Unknown
Immunoglobulin catabolism - All Ig classes and its
subclasses CH2 of IgG
Passive cutaneous anaphylaxis - IgG1, IgG3, IgG4 CH3 or entire Fc
Protein A interaction - IgG1, IgG2, IgG4 Unknown
Antigenic target for rheumatoid
factors -
IgG1, IgG3, IgG4, IgM, IgA,
IgE
Most likely Fc
region
7
human are the anterior nares, and it seems that the anterior nares are the most consistent
region from which S. aureus can be isolated [32]. However, S. aureus can be isolated
from many sites like throat, axilla and perineum as well [33–36]. Nasal colonisation is a
well-known risk factor for staphylococcal infections in the community and hospital.
Nasal carriage plays a vital role in epidemiology and pathogenesis [37,38]. The human
population can be distinguished into persistent carriers, intermittent carriers and non-
carriers [39]. Based on epidemiological longitudinal studies the following proportions
have been determined: persistent carriers 10% to 35%, intermittent carriers 20% to 75%,
non-carriers 5% to 50% [21,39–46]. Persistent carriers have significantly higher S. aureus
density in the nasal region than intermittent carriers. This observation partially explains
their increased risk of endogenous infections and suggests that the basic determinants of
persistent and intermittent carriage are different [47–50]. Furthermore, persistent carriers
are frequently colonised with a single strain of S. aureus for a longer time, but the
intermittent carriers carry different strains over time [39,41,51–53]. It seems that
persistent carriage has a protective effect on the acquisition of other S. aureus strains
[54]. Upon decolonising of human persistent carriers and subsequent inoculation with
mixtures of S. aureus strains, it was observed that former non-carriers were not colonized
in the study group and persistent carriers chose their original colonising strain from the
strain mixture [55]. A higher rate of S. aureus faecal carriage including MRSA in healthy
infants due to mother-to-infant transfer was observed and suggests that transmission
through breast milk occurs and that S. aureus is able to colonise the gut [56]. Plausibly, S.
aureus may colonise the mother’s nipple, which enhances the transmission of S. aureus
to infants through breastfeeding [57]. Due to frequent contact with livestock such as cats,
dogs, pigs, and horses, a higher incidence rate of nasal carriage of CA-MRSA was
observed. This suggests that animals can be another vector for the spread of CA-MRSA
[58–62]. It seems that the mechanism behind the nasal carriage is multifactorial. Factors
such as bacterial proteins (extracellular and cell-wall anchored proteins) [63,64],
ecological factors (healthcare and crowding) [57,65,66] and host factors (gender, age)
[65,67] play a vital role in the nasal colonisation. Typically, high S. aureus carriage rates
occurred in men, in white people, in dialysis patients, in diabetics, and in acquired
immunodeficiency syndrome (AIDS) patients [68–71]. Due to the presence of potential
8
virulence factors and higher S. aureus counts at colonised sites, there is an increased
incidence rate of S. aureus infection in MRSA-colonised patients [9,72].
Determinants of S. aureus nasal carriage
S. aureus nasal colonisation is a well-known risk factor for community- and hospital-
acquired staphylococcal infections. It has been suggested that existence of haemoglobin
in nasal fluids stimulates S. aureus colonisation through the inhibition of the agr system
[73]. The presence of triclosan in the nasal fluids of healthy individuals influences S.
aureus colonisation in the nasal niche [74]. To establish a successful colonisation S.
aureus produces adhesins and immune evasion molecules and in parallel down-regulates
virulence factors and other toxins [75,76]. High levels of human defensins (HNP1 to
HNP3) found in the nasal secretions of persistent carriers point to involvement of
neutrophils in S. aureus colonisation [68]. Proteinaceous and non-proteinaceous adhesins
are the primary factor for colonisation and instigation for infection. The most important
factor in establishing nasal carriage/colonisation is the ability of S. aureus to adhere to
the human nasal epithelial region [77]. S. aureus adhesins can bind to a wide array of
human extracellular matrix proteins such as collagen (cna) or von Willebrand factor
(vWF), fibrinogen (Fb), fibronectin (Fn), vitronectin (Vn) and elastin [78]. S. aureus
adhesins are mostly sortase cell wall-anchored proteins and have been called microbial
surface components recognising adhesive matrix molecules (MSCRAMM). MSCRAMM
protein groups vary between clinical isolates [79]. MSCRAMMs typically have N- and
C-termini with LPXTG-motifs and hydrophobic domains which are involved in
anchoring of proteins to the cell wall [80]. S. aureus supposedly adheres to the mucous
membrane of the nose through several MSCRAMM proteins including fibronectin-
binding protein A and B, Surface protein G (SasG), Clumping factor B (ClfB) in human
[63,81–85]. Wall teichoic acid (WTA), Iron surface determinant A (IsdA) and Clumping
factor B (ClfB) promote nasal colonisation in mouse models [63,83,86]. The key receptor
for WTA is SREC-1 (F-scavenger receptor). WTA binds to EGF-domains 3 and 4 of
SREC-1 in a charge-dependent manner and facilitates the adhesion to nasal epithelial
cells [87]. The target receptor of ClfB is loricrin (cornified cell envelope) on the principle
of “dock, lock and latch”. In vivo studies confirmed that ClfB is the promoter and
mediator for nasal colonisation [84]. SdrC and SdrD (serine-aspartate repeat) play an
9
important role in adhesion to nasal desquamated epithelial cells [88,89]. Nevertheless,
Sdr (serine-aspartate repeat) proteins mediate the interactions with human extracellular
matrix proteins [90]. An in vitro study suggested that SdrC, D, E are mainly involved in
platelet adhesion through fibrinogen [91]. The iron surface determinants (Isd) were
regulated by environmental iron sources and by Fur regulator. About one to three NEAT
domains were present in every Isd protein and bound to iron-containing proteins such as
haemoglobin and transferrin [92]. Based on transcript analyses in a cotton rat model
(infected with human S. aureus nose isolates), two important regulators of S. aureus, agr
and sae, were inactive during the onset of nasal colonisation which suggests that S.
aureus in the cotton rat nose lean toward adhesion rather than toward dissemination.
Moreover, tagO and tagK messenger RNA levels were high in vivo, indicating a vital role
of WTA during nasal colonisation. An important gene controlled by the WalKR system is
staphylokinase (sak) which is mainly involved in immunomodulatory functions of S.
aureus. Transcript analysis demonstrated that sak expression is significantly increased
after 24 hours post-inoculation and further increases during nasal colonisation [93].
Another important gene of the walKR regulon is sceD, which was expressed in early
nasal colonisation and whose rate of transcription was higher at 96 hours post infection.
This result supports the influence of cell wall dynamics in the primary stage of nasal
colonisation [93]. SERAM proteins such as MHC class II analog protein (Map/Eap/P70)
and Emp bind to various host components such as Fg, Fn or Vn. Still, Emp expression
level is reduced during nasal colonisation in compared to infected isolates [94]. In
contrast, a emp mutant have shown reduced attachment to Fg and Fn [95]. Preclinical
trials of immunisation with purified IsdA and IsdH proteins in cotton rat models have
shown decreased nasal colonisation [96].
S. aureus bacteraemia (SAB)
S. aureus is the second most-common pathogen causing bloodstream infections (BSI)
worldwide and a leading cause of nosocomial BSIs in Europe, with an incidence rate
within the global population of 15-40 cases per 100,000 individuals [8,97–99]. The
increasing frequency of SAB is due to increased antibiotic resistance of S. aureus isolates
[100–102]. A retrospective analysis between 2000 to 2006 disclosed that CA-MRSA
leads to the onset of bacteraemia from 24% to 49% in the hospital settings [103]. The
10
mortality rate for untreated SAB is about 80% [104]. SAB leads to sepsis and
endocarditis and disease under antibiotic therapy is still associated with high mortality
[105,106]. There is a wide range of clinical manifestations of SAB from SIRS (Systemic
inflammatory response syndrome) to sepsis to severe sepsis to septic shock to Multiple
organ dysfunction syndrome (MODS). Sepsis is associated with either fever or low body
temperature, increased respiratory rate, elevated heart rate, and edema [107]. Intravenous
antibiotic therapy for MRSA bacteraemia should be carried out for at least 14 days and 4
to 6 weeks for uncomplicated bacteraemia [108]. The current antibiotic therapy is
insufficient for SAB mainly for MRSA bacteraemia. Apparently, preclinical studies on
prophylactic administration of anti-ClfA monoclonal antibody were protective when
sepsis was induced in mice [109]. S. aureus carriers have a higher probability of
acquiring SAB than non-carriers. Primarily, carriers acquire SAB from their colonising S.
aureus isolate with an enhanced chance of survival [9,21].
Importantly, each stage of infection involves the expression of different virulence factors.
Virulence factors such as surface proteins adhere to host material or cells, help to evade
the antibody-mediated host immune reaction, promote tissue damage, stimulate iron
uptake and further increase the severity of disease [110–112]. E-MRSA 15 and 16 were
predominant in MRSA bacteraemia during 2007 and 2009 in the UK, for instance in 2007
E-MRSA 15 (85%) and E-MRSA 16 (9%), in 2009 E-MRSA 15 (79%) and E-MRSA 16
(6%). This finding suggests that genetic variations among the lineages were partly
attributed to geographical areas dispersed exclusively among the particular lineage CC8,
CC22 and CC30 [113,114]. Specific sequence variation at specific loci throughout the
genome makes up the heterogeneity of S. aureus, and proteins situated at this loci might
play a vital role in the development and dissemination of major genetic lineages [115].
Despite of clinical isolates and gene resemblances by PFGE from SAB patients, the
antibody responses were dissimilar. This antibody response dissimilarity might be due to
the variance in the time of onset of bacteraemia (except catheter-related bacteraemia) and
proposes that each bacteraemia patient develops distinctive immune responses to SAB
[116]. PSM α gene-encoded peptides from CA-MRSA isolates heighten the virulence and
have a significant impact on SAB in animal models [117]. Distinctive patterns of immune
response were found for eleven immunogenic antigens (exoproteins) autolysin,
11
bifunctional autolysin, GlpQ, Hla, HlgA, HlgB, HlgC, SspP, Plc, Nuc, and LtaS during
exogenous and endogenous infections. Higher IgG responses were shown for toxins,
especially in samples from exogenous infections [118]. Remarkably, patient stratification
was established based on IgG response patterns specific to eight S. aureus antigens (Plc,
SspB, IsaA, Sem, GlpQ, HlgC, SACOL0444, SACOL0985) in sepsis and no sepsis
patients, which may potentially help during diagnosis of SAB [119]. A further study on S.
aureus infected patients complicated by bacteraemia from 4 hospitals described higher
antibody levels to Hla, Hld, PVL, SEC-1, and PSM α3 may well defend against sepsis in
patients who develop aggressive S. aureus infections. Moreover, higher antibodies titers
to SEB and PVL were protective against sepsis [120].
Pathogenesis pattern of S. aureus – host-pathogen interaction
The main ecological niche and the place where the largest pool of S. aureus in humans
can be found are the anterior nares. Further common habitats of S. aureus are skin [121],
perineum [122], axillae [121,122], vagina [123], and gastrointestinal tract [121]. Nasal
secretory substances contain shield components of the host against microbes. Research
documented that nasal secretions from non-carriers were bacteriostatic, while the nasal
secretions from carriers allowed the progression of S. aureus [124].
For an effective defence reaction, the infected cells and immune cells need to
communicate and these reactions are mediated by molecules called “Cytokines”.
Cytokines are secreted by professional and non-professional phagocytic cells upon
stimulation. S. aureus has the ability to internalise into both professional and non-
professional phagocytic cells, e.g. epithelial cells, endothelial cells, fibroblasts,
osteoblasts, and it survives in the intracellular milieu. This intracellular milieu protects S.
aureus from the host immune system and also from antibiotics [125]. Earlier studies have
shown the impact of S. aureus during the infection of mice and human cell lines.
Likewise, several cytokines displayed increased levels in a S. aureus pneumonia mouse
model 6 h post infection, for instance pro-inflammatory cytokines such as TNF-α, IL-1β,
IL-6 were induced and anti-inflammatory cytokines IL-10, IL-12p70 were not increased
significantly. Furthermore, alterations in the host proteome were observed including
proteins involved in the inflammation reaction and coagulation [126]. In an in vitro study,
12
different growth behavior and different expression of metabolic enzymes (threonine
degradation, level of fermentation enzymes and TCA cycle) was found for intracellular S.
aureus especially 6.5 h post infection in two human lung epithelial cell lines (S9 and
A549) and in HEK 293 cells [127]. In another proteomic study investigating host and
pathogen interaction after internalisation of S. aureus into A549 human epithelial cells
physiological adaptation reactions of S. aureus have been studied in detail and reduced
growth rate, induction of the stringent response, adaptation to micro-aerobic conditions
and cell wall stress have been described. The host displayed strong apoptosis signalling,
clathrin-mediated endocytosis signalling, calcium signalling and integrin signalling after
infection with S. aureus HG001 in comparison to the non-infected control cells [128].
Apparently, S. aureus targets the host cell adhesion molecules (cadherin, integrin or
ICAM) for the establishment of contact with host cells and tissues surfaces [129]. MRSA
in livestock’s aid as reservoirs for human colonisation, for instance S. aureus clonal
lineage ST398 from swine [130]. A microarray study revealed that animal lineages are
interrelated to human lineages [131]. S. aureus employs an array of virulence factors that
protect from the host’s immune system and enables crossing of mucosal barriers,
dissemination, and replication in distant organs. The virulence factors include teichoic
acid (non-protein molecules), peptidoglycan (non-protein molecules), capsular
polysaccharides (non-protein molecules), MSCRAMM proteins, super antigens,
enterotoxins, extracellular enzymes, proteases (serine proteases, cysteine proteases, and
metalloproteases), immune-evasion molecules and immunomodulators.
Despite of host evasion, survival of S. aureus within the host is influenced by the nutrient
procurement such as iron and manganese [132–134]. S. aureus produces high-affinity
iron-binding compounds such as aureochelin and staphyloferrin during iron deprivation
[135,136]. Manganese transport protein C (MntC) is a MSCRAMM protein, which
interacts with host factors such as collagen type IV, laminin, fibronectin, fibrinogen, and
plasminogen. MntC digests plasminogen to plasmin, which in turn degrades the host
fibrinogen as like staphylokinase [134]. S. aureus is not susceptible to lysozyme owing to
the cell wall modifying enzyme OatA along with WTA [137]. Clumping factor B (ClfB),
a surface located MSCRAMM protein adheres to the ligand cytokeratin 10 (glycine-
13
serine loop), the main component of desquamated human epithelial cells [138]. The other
MSCRAMM protein SdrC binds to β-neurexin which is primarily present in brain tissue
[139]. Staphylococcal protein A (Spa) has five sequential, homologous extracellular Ig-
binding domains, binds to the Fc region of the IgG and helps S. aureus to escape from the
host immune system [140,141]. Antibody-binding to S. aureus via the Fab region
facilitates the opsonisation and enhancing the killing by neutrophils, whereas binding via
Fc region restricts phagocytosis [142]. Staphylokinase (Sak), a serine protease-like
molecule inhibits and neutralises the bactericidal property of α-defensins (HNPs) and
catalyses the cleavage of plasminogen to plasmin, which is capable of digesting human
IgG and complement C3b/C3bi and prevents the opsonisation [143,144]. The immune
evasion clusters (IEC) such as SCIN and CHIPS act as an immune modulators and attach
to human complement proteins C5a and formyl peptide receptors (FPRs). As a result it
inhibits phagocyte recruitment [145–147]. An in vitro study revealed that serine-aspartate
repeat containing protein D (SdrD) enhanced the adhesion to keratinocytes via
Desmosomal cadherin 1 (Dsg1) [148]. Ssl7 are immune evasion molecules, which bind to
IgA and complement C5 and in turn inhibit the generation of C5a [149,150]. S. aureus
produces at least five molecules that prevent the action of serum complement
convertases. These convertases are important for opsonisation of bugs because activated
complement components such as C3b and iC3b stimulate phagocytosis [151]. Recently,
Laarman et al. revealed that the cysteine protease Staphopain A (ScpA/SspA) of S.
aureus cleaves the N-terminus of the CXCR2 and thereby likely prevents neutrophil
triggering and enrolment [152]. S. aureus produces various pore-forming toxins such as
α, β, γ, δ – Hemolysin, Panton-valentine leukocidin (PVL), phenol-soluble modulins
(PSM). These toxins play a vital role in target cell lysis such as erythrocytes, leukocytes
(neutrophils, monocytes and T-cells)[153–156]. The PSM peptides from CA-MRSA
strains were primarily sensed by the innate immune system, acting as a pro-inflammatory
agents, induce cytokines, they have cytotoxic activity towards neutrophils and, thus, lead
to escape from neutrophils [155,157]. It has been found that serum lipoproteins inactivate
PSMs [158]. LukG and H play primary roles in cytolytic activity and specifically lyse
neutrophils by forming an octameric pore [159,160]. PVLs are bi-component toxins
mostly associated with CA-MRSA clinical strains, whose contribution to S. aureus
14
pathogenesis are still unclear [161,162]. Figure 3 represents the interactions between host
and S. aureus; their outcome during the interaction.
S. aureus
Neutrophil Complement
T cell
Acquired
InnateAur, Sod,Sak,
Nuc,
Lysozyme &
ROS - AMP
Chips, Sak,
Scin, Efb
Oposonization
SAg, HlgB &C
(Toxins)CD4+
Immunoglobulins
(opsonizing,
neutralizing)
Proteases, spa,
sbi
B cell
Figure 3. Schematic representation of interaction (“Trade-off”) between S. aureus and human
immune system
S. aureus vaccine candidates
In general, bacteria possess less potential to develop resistance to a vaccine than to an
antibiotic, although some microbes tend to antigenic variation to escape from the
vaccine-induced immunity. S. aureus is an encapsulated bacterium, it is a major cause of
community and nosocomial infections. Globally, there is a swift increase in drug
resistance that has added urgency to the development of an effective vaccine [163]. An
effective S. aureus vaccine would provide great potential security and have many
universal benefits [164]. Nevertheless, efforts to generate an S. aureus vaccine have
failed so far [165–167]. Previous vaccine studies indicated that a single antigen approach
seems to provoke a lower likelihood of success than a multi-antigen approach. An
introduction of an effective vaccine for S. aureus will dramatically reduce the burden on
15
the health care institutions and will protect the individuals from the risk. Also, it is better
to have a mixture of multivalent antigens for a successful S. aureus vaccine, for example
ClfA, Isd family proteins and MntC, due to the high genetic variability of virulence
factors within staphylococcal isolates. Screening the efficacy of antigen candidates in
multiple preclinical models provides an opportunity for better understanding their
strength and weakness. The summary table of S. aureus active and passive vaccines
under clinical trials are listed in the Table 2 and Table 3. However, serum therapy for S.
aureus infections was less effective than for diphtheria and tetanus due to the complex
pathogenesis and virulence variability [168]. For an efficient vaccine development,
considering bacterial and host-related factors of carriage and characteristics of human`s
response to different episodes of S. aureus encounter is extremely important. The
investigation of humoral immune responses from patients carrying S. aureus will enable
the identification of S. aureus antigens that are immunogenic, and will help us to identify
potential vaccine candidates. Earlier S. aureus vaccines, which failed in clinical trials
were made of single or divalent antigens such as capsular polysaccharides (CP). These
single or bivalent antigen-based vaccines could not manage the multiple virulence
strategies of S. aureus. The CP5 and CP8 strengthen the virulence of S. aureus by
evading phagocytosis [169]. Li et al. revealed that immunisation of rabbits and mice with
ClfA produced functional antibodies which inhibit S. aureus binding to fibrinogen, but
which were not protective in murine models with surgical infections like catheter-related
endocarditis [170]. A prospective study of SA4Ag (Staphylococcus aureus 4-antigen)
vaccine antigen evaluation suggested that MntC genes were more highly expressed than
other genes (CP5, CP8 and ClfA) in nasal and wound samples. In parallel, antigen-
specific antibodies were monitored in serum with the result that most of the patients
generated responses to ClfA and MntC [171]. Active immunisation of S. aureus infected
mice with octavalent antigens (IsaA, LytM, Nuc, Atl and PSM α 1 - 4 peptides) did not
result in protection for mice against S. aureus bacteraemia and skin infections [172].
So far it is still unclear if the number of antigens selected (mono- or multivalent), the
patient diversity or the manufacturing problems are directed to the lack of positive results
[173]. Earlier preclinical evidence strongly suggested that a multi-antigen approach may
lead to a robust immune response against S. aureus in a murine model [174]. For
16
developing an ideal S. aureus vaccine, active and passive vaccine approaches should be
followed. Passive immunisation might be used as an adjunct to antibiotics in severe S.
aureus infections.
17
Table 2 : Active vaccination (antibody production) clinical trials
Product Composition Status Limitation Reference
StaphVAX™
CP5 and CP8
(Capsule
polysaccharide)
Phase III failed
Single antigen
and limited
efficacy.
[175–177]
V710 IsdB (LPXTG) Phase II/III
terminated
No efficacy, did
not produce
opsonising
antibodies and
single antigen
approach.
NCTC00735839,
[178]
PentaStaph
CP 5 and CP8, Wall
teichoic acid,
Panton-valentine
leukocidin (PVL),
Alpha-hemolysin
(Hla)
Phase I
completed for 2
antigens on 27
April 2011 and
further trails on
other antigens.
Trial not yet
completed. [179]
SA75
Killed S. aureus
(whole cell) mainly
contains GST-Cna
(collagen binding
protein), His-Clf
(clumping factor),
GST-D (fibronectin
binding protein),
and Eap
(extracellular
adherence protein)
Phase I
completed
Immunisation
with whole cell
may not induce
protective
antibodies.
[180]
GSK2392102A
Tetravalent vaccine
adjuvanted with
ASO3B (Hla, ClfA,
CP5 and CP8
Phase I
Higher antibody
levels with no
adjuvant effect.
[181],
NCTC01160172
STEBVax
SEB
(Staphylococcal
enterotoxin B)
Phase I
suspended
Limited SEB
expressing
strains.
NCTC00974935
SA4Ag CP5, CP8, ClfA and
MntC Phase II
Trial not yet
completed
NCTC02388165,
[171]
18
Table 3 : Passive vaccination (antibody providing) clinical trials
Product Composition Status Limitation Reference
Pagibaximab Anti-Lipoteichoic
acid antibody
Phase II/III
ongoing
Higher dose
(90mg/kg) enhance
the sepsis protection
for neonates and
less efficacy in
lower dose.
[182]
Tefibazumab
(Aurexis/Inhibitex)
Anti- ClfA
monolconal
antibody
Phase II
completed
Decreased relapses
and complications,
but not lower
mortality. No
efficacy during
bacteraemia
progression.
[183]
Altastaph
Pooled human
antibodies against
CP5 and CP8
Phase II
completed
Higher mortality
rate in neonates than
placebo.
[184,185]
Aurograb
Single chain
antibody variable
fragment against
ABC transporter
component GrfA
Phase III
completed
Due to the safety
measures and lack
of efficacy during
phase II, Novartis
terminated this
clinical trial.
[186–188]
Veronate (INH-
A21)
Pooled human IgG
against Fbp, ClfA,
and SdrG
(IgG derived from
donors with high
titers of antibody to
Fbp, ClfA and
SdrG)
Phase III
completed
Less efficacy and no
difference in
episodes of late-
onset
staphylococcal
sepsis (LOS).
[189,190]
19
Immunoproteomics: From individual sample analysis to high throughput
S. aureus as a commensal and opportunistic pathogen has distinct tactics to adapt to
different circumstances from colonisation to bacteraemia related infections. Upon S.
aureus invasion in the host, immune cells mount an immune response or antibody
responses. To decipher the complexity during disease progression, “Omics” technologies
play an important role to study the pathophysiology of S. aureus and unravel mechanism
of host-pathogen interaction. The current emerging tool in the field of proteomics widely
used to identify potential vaccine candidates is “immunoproteomics”, a term coined in
2001 by Jungblut [191]. The immunoassays can be performed using different analytical
technologies and Figure 4 represents the technology evolution during the past six
decades.
Different classical methods are available under immunoproteomics to study the antibody
responses such as agglutination, radio-immunoassay (RIA), enzyme-linked
immunosorbent assay (ELISA), immunochromatographic test (lateral flow test) and 2D-
immunoblotting, but the bottleneck of these techniques is the identification of proteins or
peptides.
The ELISA is a high-throughput method and the advantages are that many serum/plasma
samples can be analysed in parallel, instruments are inexpensive and easy handling in
terms of protocol and instruments. The process can be automated, making this technique
attractive in fully equipped laboratories that test large numbers of samples. However, the
limitations are separate assays for each antigen, requirement for high specificity
antibodies and that time to results still can take hours.
For developing countries with limited access to laboratory resources, a simple
immunochromatographic test would be the preferred method. This method requires only
a visual inspection of the antigen-containing lines and can be performed as a point-of-
care assay without laboratory equipment and this test can be performed within a few
minutes [192].
The other classical proteomic pillar is 2-D PAGE. The first two-dimensional
electrophoresis separation was achieved in human serum to resolve 15 human proteins
and further described by Farrel et al. in 1975 [193,194], this technique is used to separate
20
complex mixtures of proteins. The combination of 2-D PAGE with immunoblotting and
MS was used to isolate and identify the immunogenic proteins and this approach is called
SERPA [195]. In principle, the proteins are separated in terms of their mass and charge,
whereby during the first dimension the proteins are separated by its isoelectric point and
in the second dimesion by its molecular weight [196]. The main disadvantage of 2-D
PAGE are: i) cross contamination because single spots can contain several proteins, ii)
limited detection of low-abundance proteins, iii) poor resolution in the low molecular
weight region (< 10kDa) and limit ability of resolution of certain protein classes such as
membrane proteins. Protein array methods are an alternative to ELISA and 2-D PAGE.
Here, immobilisation of immunogenic antigens on the solid-phase slide surface is used in
order to detect the antibodies specific to immunogenic antigens, a variant that is called
reverse phase protein array. The other mode of protein array is inverse of reverse phase,
immobilisation of antibodies from patient samples on the solid-phase slide surface in
order to detect the immunogenic antigens/biomarkers. Protein arrays offer the possibility
to by-pass the limitations of ELISA, 2-D PAGE by allowing multiplexing (simultaneous
detection) and measurement of serum antibodies against multiple antigens.
Besides protein arrays several other gel-free approaches are available to identify
immunogenic antigens for instance particle-based flow assays and expression array. An
exciting emerging new technology platform is “Particle-based flow cytometric assays” or
“Suspension array technology” [197]. To date, Luminex, Becton-Dickinson and Diasorin
are offering this technology. The technology evolution in the field of immunological
methods is depicted in the Figure 4.
In order to further investigate the diagnostic potential of these immunoreactive antigens,
a robust and multiplexed assay system for large scale and high-output biology is needed.
Such assay systems allow screening the serological reactivity of hundreds of samples
against a multitude of antigens. The “particle-based flow cytometric assays” or
“suspension array technology” belongs to such multiplexed based assay systems.
Immunoproteomics is a powerful tool for identifying potential candidates for the vaccine
development. Many vaccine candidates were identified using immunoproteomics
approaches in respiratory pathogens such as Burkholderia pseudomallei, Neisseria
meningitidis, Bordetella pertussis, Streptococcus pneumoniae, Staphylococcus
21
epidermidis, Campylobacter concisus, Pseudomonas aeruginosa, Leptospira interrogans,
and Francisella tularensis [198]. Vytvytska et al. revealed 15 S. aureus antigens showing
strong antibody responses in healthy and S. aureus infected individuals using SERPA
(Serological proteome analysis) [199]. Holtfreter et al. observed the strong IgG binding
against Hla, SspB, SspA, Plc, SplB, and SplE in carriers using 2-D immuno-blotting (2-D
IB)[200]. Furthermore, a Staph-toxin-array revealed that IgG responses against IsaA,
SACOL0479 and SACOL0480 were significantly lower in non-carriers than carriers
[201]. With this 2-D immuno-blotting approach several promising S. aureus
immunogenic antigens could be identified [198,202].
Among the immunoproteomics approaches, the most promising technology is Suspension
Array Technology (SAT) – FLEXMAP 3D®
. It is a high throughput technique which can
multiplex up to 500 analytes with a wide dynamic range (105). SAT yields more
informative data in shorter analysis time and with lower sample volume than
conventional ELISA [203,204]. SAT has a high potential and represents a promising tool
for personalised medicine and for delivering details of a patient’s physiological and
pathological conditions.
1950 – 59
• RIA and 2D-
PAGE
1960 – 79
• ELISA
1980 - 89
• Automated ELISA system
1990 -2000
• Miniaturized spot array
and
Suspension array
technology (SAT)
Figure 4. Immunoassay technology milestone from 1950 till 2000. This figure shows the technology
evolution in the field of immunological methods.
Aim of the study
S. aureus is a life-threatening pathogen which leads to various infections such as
pneumonia, bacteraemia, endocarditis, septic arthritis, and osteomyelitis. There are no
22
prognostic (active vaccine) and diagnostic (passive vaccine) measures to prevent S.
aureus infections. Vaccines (Table 2 and 3) developed in the earlier decades failed during
preclinical and clinical trials. However, earlier immunisation studies in mouse models
revealed that antibodies directed against MSCRAMM proteins, pore forming toxins, CP5
and CP8 can indeed protect against S. aureus colonisation or infection [178–180]. Thus,
it is still unclear which antigen or group of antigens would constitute valid vaccine
candidates. For the antigen screening, several immunological methods are available and
there are several disadvantages in these classical methods due to their technical
limitations. To circumvent this classical technology limitations, new emerging
technology, particle-based flow cytometric assay or suspension array technology has
been introduced.
The first objective of this thesis was the technology transfer from planar microarray
technology and other classical methods (RIA, ELISA etc.) to the multiplexed bead-based
FLEXMAP 3D® (SAT) platform to allow high-throughput screening of clinical samples.
The second objective of this thesis was to elucidate the humoral responses (antibody
responses) during S. aureus colonisation/infection and, thereby, to discover potential
antigen candidates for S. aureus vaccine development as depicted in the Figure 5.
In order to achieve the main objective, we investigated serum and plasma samples from
different human S. aureus infected patients such as nasal carriage, bacteraemia, sepsis,
severe sepsis, and septic shock by using high-throughput suspension array technology.
23
Thesis graphical abstract
Literature search
Antigens from Immunology
in vitro studies
Antigen stratification
Immobilised antigenIgG – Patient Vs. ControlFluorescent labelled Anti-Human IgG
Antigen stratification Setting-up serological assay
using SAT
Application to clinical cohorts
Stu
dy
-1
Stu
dy
-2
Stu
dy
-3
Humoral
responses during
S. aureus nasal
colonisation
Humoral responses
during S. aureus
bacteraemia onset
Humoral responses
during S. aureus
bacteraemia
Identifying potential S. aureus antigens
Active vaccine
Passive vaccine
Patient stratific-
ation
Figure 5. Schematic representation of thesis abstract.
24
Materials and Methods
Materials
Materials required for overexpression
Research grade chemicals were purchased from Sigma-Aldrich, Merck, ROTH
and Bacto. Protease inhibitors, lysozyme, IPTG, and DnaseI were procured from
Roche and Thermo. Ni-NTA agarose, polypropylene columns, the penta histidine
and streptavidin tag antibodies were procured from Qiagen. Phycoerythrin anti-
human IgG and anti-mouse IgG antibodies were procured from Dianova. E. coli
expression plasmids for S. aureus antigens were constructed by Protagen AG. The
E. coli expression strain SCS1 carrying the helper plasmid pQE30 was
transformed with the ligation mixtures. The correct expression vectors were
confirmed by colony polymerase chain reaction and further quality controlled by
5’ DNA sequencing.
List of chemicals
Table 4: list of chemicals
Chemical name Grade/Quality Supplier Catalogue No.
1,4-dithiothreitol (DTT) NA Thermo R0862
Acetic acid NA Sigma 537020
Agar powder Molecular
biology
Sigma 5040
Ammonium chloride (NH4Cl) Molecular
biology
Sigma A9434
Ammonium sulphate Molecular
biology
Sigma A4418
Ampicillin sodium salt Molecular
biology
Roth K029.2
Bovine serum albumin (BSA) NA Sigma A7906
Bradford reagent NA Bio-Rad 5000006
Bromophenol blue NA Sigma B0126
Calcium chloride (CaCl2) Anhydrous Sigma 499609
Coomassie brilliant blue G-250 NA Bio-Rad 1610406
Di sodium hydrogen phosphate
dihydrate (Na2HPO4.2H2O)
NA Roth 4984
Dimethyl sulfoxide anhydrous NA Sigma 276855
DNAse I NA Roche 10104159001
EDC NA Sigma 22980
25
Ethanol absolute ACS Merck
Millipore
107017
Glucose (C6H12O6) NA Sigma G5400
Glycerol NA Sigma G6279
Guanidine hydrochloride (CH5N3 .
HCl)
Elite Sigma G4505
Imidazole (C3H4N2) Puriss. p.a. Sigma 56750
Isopropyl-beta-D-
thiogalactopyranoside (IPTG)
NA Thermo R0392
Kanamycin sulphate salt Premium Sigma K4000
LCB NA Candor 100050
Luria bertani (LB) broth Molecular
biology
Sigma L0322
Lysozyme NA Sigma L1667
Magnesium chloride hexahydrate
(MgCl2.6H2O)
ACS Sigma M9272
MES hydrate Elite Sigma M8250
Methanol ACS Merck
Millipore
106009
Monobasic sodium phosphate Molecular
biology
Sigma S3139
Nickel-nitrilotriacetic acid (Ni-NTA) NA Qiagen 30210
Phosphate buffered saline (PBS) Molecular
biology
Sigma P5368
Potassium dihydrogen phosphate
(KH2PO4)
ACS Sigma P0662
Proclin 300 Premium Sigma 48912-U
Protease inhibitor cocktail NA Roche 04693116001
Sodium chloride (NaCl) Molecular
biology
Roth 3957
Sodium hydroxide (NaOH) NA Roth 6771
Sulfo-NHS NA Sigma 24510
Tris (hydroxymethyl) aminomethane
hydrochloride
Elite Sigma T5941
Tryptone Molecular
biology
Bacto 211701
Trypsin Sequencing Promega V511A
Tween 20 NA Sigma P7949
Water LC-MS grade Merck
Millipore
115333
Yeast extract Molecular
biology
Sigma 92144
26
List of consumables
Table 5: List of consumables
Name Supplier Catalogue No.
96 well half area microplate Greiner 675101
96 well Polystyrene microplate Greiner 655095
Aluminium sealing tape, pierceable Sarstedt 95.1995
Carboxy functionalised standard xMAP
super paramagnetic beads
Luminex MC100xx
C18 Zip Tip columns Merck
Millipore
ZTC18S096
Goat anti-human IgG – RPE Dianova 109-116-098
Goat anti-mouse IgG – RPE Dianova 115-116-146
Magnetic separator unit (96 well) Lifesep 2501008
Micro insert, 0.1 mL MA, USA 548-0020
Micro vials VWR Intl. NSCAC4013-1
Penta His Antibody BSA free Qiagen 34660
nanoACQUITY UPLC 2G V/M trap column Waters, USA 186006527
nanoACQUITY BEH 130Å UPLC column Waters, USA 186003546
Sheath fluid Luminex 40-50000
Strep tag antibody Qiagen 34850
PVDF filters - Pore size 0.45µm and
Diameter 33 mm
Sigma Z355518
List of instruments
Table 6: List of instruments
Instrument Company
Centrifuge, 5810R Eppendorf
Electronic single and multi-
channel pipettes
Rainin – Mettler Toledo
FLEXMAP 3D® Luminex
corporation
LTQ-Orbitrap XL mass
spectrometer
Thermo Fischer Scientific Inc.
UV-Visible spectrophotometer VWR®
Well plate shaker Thermo Fischer Scientific Inc.
Media and antibiotics
LB - Medium
- 1 % (w/v) Tryptone; 0.5 % (m/v) Yeast extract; 0.5 % (m/v) NaCl in double
distilled water.
- Mixed contents was autoclaved.
27
LB Ampicillin and Kanamycin – Agar
- 1 % (m/v) Tryptone; 0.5 % (m/v) Yeast extract; 0.5 % (m/v) NaCl, 1.5 % (m/v)
agar in double distilled water.
- Mixed contents was autoclaved.
- Agar was cooled down to 45°C, then 1.0 mL of sterile ampicillin (100 mg/mL)
and kanamycin (15 mg/mL) were added.
- Agar plates were poured by transferring about 25 mL of the above mixture to each
agar plate and then plates were stored at 4°C.
SB – Medium
- Solution A: 3.2 % (m/v) Tryptone; 2.0 % (m/v) Yeast extract; 0.5 % (m/v) NaCl;
0.5% (v/v) 1N NaOH in double distilled water.
- Solution B (20x M9 salts): 12 % (m/v) Di sodium hydrogen phosphate dihydrate;
6 % (m/v) potassium di hydrogen phosphate, 2 % (m/v) Ammonium chloride,
0.006 % (m/v) calcium chloride in double distilled water.
- Solutions A and B were autoclaved.
- Autoclaved solution A and B were mixed in a proportion of 95:5, then 0.4 % (v/v)
sterile glucose was added.
Buffers and solutions
Protein overexpression (induction, lysis and purification)
- 1M IPTG sterilised solution: About 238.3 mg of IPTG was transferred in 1.0 mL
of double distilled water and filtered through PVDF 0.45µm filters.
- Protease inhibitor cocktail (25x) EDTA free: A tablet of protease inhibitor
cocktail was dissolved in 2 mL of double distilled water and Store at -20 ° C.
- Cell lysis buffer: 20 mM Di sodium hydrogen phosphate dihydrate, 20 mM
imidazole, 100 μg/mL lysozyme, 100 μg/mL DnaseI, 100 μg/mL MgCl2, 40
μl/mL Protease inhibitor cocktail (25x), 5 mM DTT were mixed and titrated to pH
7.4 with NaOH or H3PO4.
28
- IMAC denaturing wash buffer: 20 mM Di sodium hydrogen phosphate dihydrate,
20 mM imidazole, 6 M guanidine-HCl, 1 mM DTT in distilled water and titrated
to pH 7.4 with NaOH or H3PO4.
- IMAC elution buffer (denature): 20 mM Di sodium hydrogen phosphate, 500 mM
imidazole, 6 M guanidine-HCl, 1 mM DTT and titrated to pH 7.4 with NaOH or
H3PO4.
1D – SDS-PAGE
- 2x SDS sample buffer: 0.09 M Tris-HCl (pH 6.8), 2 % SDS, 25 % (v/v) Glycerol,
0.02 % (w/v) Bromophenol blue, 0.1 M DTT.
- Fixation solution: 50 % (v/v) double distilled water, 40 % (v/v) ethanol, 10 %
(v/v) acetic acid.
- Coomassie brilliant blue (CBB): 5.0 g (m/v) of coomassie brilliant blue G-250 in
100 mL of double distilled water.
- Colloidal coomassie dye (CCD): 50 g (m/v) of diammonium sulphate (NH4)2SO4,
6.0 mL of ortho phosphoric acid (85 %), 490 mL of distilled water, 10 mL of
CBB stock solution.
- Colloidal coomassie solution (CCS): 200 mL of CCD stock solution, 50 mL of
methanol were prepared freshly.
- Destaining solution: 20 % (v/v) methanol
Protein concentration determination
- BSA standard: 0.1 mg/mL
- Bradford reagent: Brilliant blue G-250 dye
Buffers and reagents for mass spectrometry analysis
- 20 mM Ammonium bicarbonate solution (ABC): About 0.160 g of ammonium
bicarbonate dissolved in 100 mL of LC-MS grade water.
- 25 mM Dithiothreitol (DTT): 0.375 g of DTT in 10 mL of 20 mM ammonium
bicarbonate solution.
29
- 100 mM Iodoacetamide (IAA): 0.18 g of IAA in 10 mL of 20 mM ammonium
bicarbonate solution.
- LC buffer A: 2 mL of acetonitrile in 100 mL of LC-MS grade water containing
0.1 mL of acetic acid.
- LC buffer B: 0.1 mL of acetic acid in 100 mL acetonitrile.
Serological assay
- Activation buffer: 100 mM sodium dihydrogen phosphate, pH 6.2.
- Coupling buffer: 50 mM MES, pH 5.0.
- Wash buffer: PBS, 0.05 % tween20.
- Block store buffer: PBS, 1 % BSA.
- Bead buffer: 50 % CBS, 50 % LCB.
- Assay buffer: 45 % CBS, 45 % LCB, 10 % E. coli lysate.
List of databases
- http://aureowiki.med.uni-greifswald.de/
- http://www.uniprot.org/
- http://www.ncbi.nlm.nih.gov/
- http://www.psort.org/psortb/
- http://wlab.ethz.ch/protter/#
- http://www.kegg.jp/
- http://web.expasy.org/
- http://www.proteinatlas.org/
- http://www.cbs.dtu.dk/services/SignalP/
- https://www.citavi.com/
List of software
- Graphpad prism® 5
- Genedata® (Analyst and Refiner MS)
- MeV : Multi experiment viewer
- R studio®
30
List of recombinant S. aureus antigens
The recombinant S. aureus antigens were listed in supplementary pages (supplementary 1
and 2). The S. aureus antigens comprised 12 cytoplasmic, 19 cell wall, 12 membrane, 66
extracellular, 16 unknown antigens and 8 auto-inducing peptides. The antigen constructs
were procured from Protagen AG, Germany. The antigens were expressed in E. coli
strain SCS1 and induced with 1 mM IPTG (final concentration), and purified under
denaturing conditions with Ni-NTA agarose recognising the His-tag. Qualitative analysis
was done by 1-D SDS-PAGE, which is described in the next sections.
Overview of study population
Three different studies were carried out in this thesis. Plasma/serum samples were
obtained from patients infected with S. aureus.
In study-1 (Table 7), plasma samples were obtained from healthy individuals of both
carriers and non-carriers from the residents of western Pomerania, where total 32 plasma
samples including 16 healthy carriers and 16 healthy non-carriers.
Table 7: Study-1
Characteristics of plasma samples from nasal carriage patients [205]
Study area Western Pomerania
Study population Healthy blood donors
Number of subjects 32
Time period February 2005 – February 2006
Mean age (yrs.) (±SD) 33.8 (10.7)
Male sex (%) 81.2
Number of non-carriers (%) 16 (50)
31
In study-2 (Table 8), serum samples were obtained from 49 S. aureus bacteraemia
patients and 43 control patients collected during routine diagnostics between 2011 - 2014
from the Akershus university hospital in Norway. SABs are defined by the positive blood
culture obtained earlier or later than 48 h after hospitalisation.
Table 8: Study-2
Characteristics of serum samples of bacteraemia patients from Akershus university hospital,
Norway (SABSI) [206]
Sepsis
(n = 49)
Control/No-sepsis
(n = 43)
Study benchmark
First S. aureus positive blood
culture (No prior antibiotic
therapy)
Healthy enough to undergo
elective orthopaedic surgery
and no S. aureus surgical site
infection within 1 year of
surgery.
Gender
Male 33 27
Female 16 16
Time period March 2011 – February 2014 March 2011 – June 2014
Age (yrs.) (±SD) 66.2 (16.2) 63.3 (18.8)
Acquired type (%)
Community acquired (CA) 28.5 NA
Hospital acquired (HA) 32.6 NA
Health-care associated (HCA) 38.8 NA
Nasal carriage (%)
Carriers 30.6 27.9
Non-carriers 69.4 72.0
In study-3 (Table 9), serum samples were obtained from 44 patients diagnosed with S.
aureus infection complicated by bacteremia.This observational study was approved by
the University of Maryland Baltimore institutional review board.
32
Table 9: Study-3
Characteristics of serum samples from S. aureus infected patients complicated by bacteraemia [207]
Sepsis
(n =19)
No-sepsis
(n = 21)
Odds ratio (CI) p-value
Demographics
Mean age (±) 59 ± 13 57 ± 18 0.75
Gender 0.99 (0.29 – 3.43) 1.0
Female 9 10
Male 10 10
Race 0.63
African
American
11 15
White 7 6
Unanswered 1 2
Earlier S. aureus infections 1.52 (0.34 – 6.76) 0.71
Yes 5 4
No 14 17
Earlier MRSA infection or
colonisation
0.71
Yes 7 6
No 10 11
No data 2 4
Dialysis patient 0.48 (0.12 – 1.81) 0.33
Yes 5 9
No 14 12
Diabetes mellitus patient 0.53 (0.13 – 2.23) 0.49
Yes 4 7
No 15 14
Bacteraemia type 5.23 (0.95 – 28.9) 0.069
Primary 17 13
Secondary 2 8
If secondary bacteraemia 1.0 (0.03 – 33.4) 1.0
SSTI 2 7
UTI 0 1
Nosocomial infection 2.31 (0.56 – 9.47) 0.31)
Yes 15 13
33
Methods
Overexpression of recombinant S. aureus antigens
E. coli expression clones were inoculated on LB ampicillin and kanamycin agar plates at
37°C. After overnight cultivation, a single colony was selected and transfected into the
baffled flask containing SB media, incubated at 37°C, 300 rpm. Cell density was
monitored at 600 nm every 60 min and 5 µL was sampled for SDS-PAGE every 60 min.
The expression of target genes was induced with 100 µL of 1M IPTG at OD600 = 5.0,
incubation was continued for 2 hours and then cells were harvested. Cells were
sedimented by centrifugation at 8500 rpm for 15 min, 4°C. Pellets were resuspended in
lysis buffer and incubated for 30 min in ice. The cells were disrupted by ultrasonication
with the micro-tip on ice using three 10 s bursts at the amplitude intensity of 60 %. The
disrupted cells were centrifuged at 8500 rpm for 15 min, 4°C and the supernatant
transferred to a new container and 5 µL of lysate sampled for SDS-PAGE. In case of
unsoluble proteins such as membrane proteins, the inclusion bodies were resuspended in
IMAC wash buffer and disrupted by ultrasonication with the micro-tip on ice using three
10 s bursts at the amplitude intensity of 60 %. Then the disrupted inclusion bodies were
centrifuged at 8500 rpm for 15 min at 4°C. The lysate was purified by immobilised metal
affinity chromatography (IMAC). Ni-NTA (Nickel-Nitrilotriaceticacid) agarose slurry
was resuspended and about 4.0 mL of Ni-NTA agarose slurry was filled in a 5.0 mL
polypropylene column. About 2.0 mL of double distilled water was then added to the
column to settle the resin by gravity. After settling of resin the storage solution was
gently aspirated. Columns were then washed with 10 column volumes of double distilled
water followed by 20 column volumes of IMAC wash buffer. Then, lysate was loaded
onto the column and the the flow through collected for SDS-PAGE. Columns were
No 4 8
Current MRSA 0.46 (0.12 – 1.79) 0.32
Yes 11 15
No 8 5
Days since presentation of
symptoms
0.9 ± 0.3
(n = 18)
1.3 ± 0.3 0.35
34
washed with 10 column volumes of IMAC wash buffer and the wash fractions collected.
Bound protein was eluted with IMAC elution buffer as four elution fractions (E1 to E4).
Total protein concentration was determined for the elution fractions by Bradford method
using BSA as standard. BSA standard concentrations ranged from 0.2 µg to 20 µg. For
determination of the protein content the elution fractions were diluted 2 to 10-fold based
on the absorbance range in the standard curve. Based on the concentration of protein
from Bradford assay, elution samples were prepared for 1D SDS-PAGE. About 2 µg to 5
µg of protein in elution fraction, flow-through, wash fractions and lysate fractions were
mixed with 2x SDS sample buffer. This mixture was incubated at 70°C for 10 min. The
SDS-PAGE was performed using NuPAGE®
4 - 12% Bis-Tris precast gels with a
constant voltage of 180 V, with MES running buffer. Gels were fixed with fixation buffer
for 2 x 15 min, stained with CCS solution overnight, destained with destaining solution
for 2 x 15 min and washed with water for 2 x 60 min.
Mass spectrometry analysis of purified recombinant S. aureus antigens
(Sample preparation was done by Mrs. Kirsten Bartels and sample measurements
by MS was performed by Dr. Vishnu Dhople)
The purified S. aureus antigens/proteins were analysed by mass spectrometry in detail to
study the composition and purity of the purified proteins. About 2 µg purified proteins
were taken for trypsin digestion. In order to dilute the guanidine HCl in the purified
protein sample, 20 mM ammonium bicarbonate was added to the samples to reach a final
volume of 20 µL. Dithiothreitol (DTT) was added to the final concentration of 2.5 mM,
and incubated for 60 min at 60°C. Subsequently, the volume was adjusted to 25 µL with
20 mM ammonium bicarbonate. To avoid the reformation of disulphide bridges,
iodoacetamide (IAA) was added to a final concentration of 10 mM and incubated for 30
min at 37°C under darkness. Further, trypsin was added in a ratio of 1: 25 (trypsin:
protein) and incubated overnight (16 – 18 h) at 37°C. The digestion was quenched with
acetic acid with the final concentration of 1%. Further, the peptides were purified and
enriched using zip-tips with C18 resin. For the digested peptides, a tip capacity of 2 µg
was used for the peptide purification and enrichment. As a first step in the peptide
purification and enrichment process, the column was stepwise equilibrated with 100%
acetonitrile (ACN), 80% (v/v) ACN solution, 50% (v/v) ACN solution, 30% (v/v) ACN
35
solution and 1% (v/v) acetic acid. The sample was loaded onto the column by pipetting
up and down 20 times. The column was washed with 50 µL of 1% (v/v) acetic acid. The
peptides were eluted first with 50% (v/v) ACN solution by pipetting up and down 10
times and then with 80% (v/v) ACN solution. The eluates were pooled in a micro vial and
then dried to remove the ACN in a speed vacuum centrifuge. The dried purified and
enriched peptides were dissolved in 10 µL of LC buffer A (2% acetonitrile in water with
0.1% acetic acid). In order to study the protein composition, the trypsinised peptides from
purified proteins were analysed using liquid chromatography-mass spectrometry (LC-
MS). The LC-MS analysis was performed on a nanoACQUITY UPLC (Waters
Corporation, USA) coupled to LTQ-Orbitrap XL mass spectrometer (Thermo electron
corporation, Germany) equipped with a nano-ESI source and installed with Picotip
Emmitter (New objective, USA). For LC separation, the digested peptides were first
enriched on a nanoACQUITY UPLC 2G-V/Mtrap symmetry C18 pre-column (2 cm
length, 180 µm inner diameter and 5 µm particle size) from waters corporation and
resolved the peptides using nanoACQUITY BEH 130Å C18 column (10 cm length, 100
µm inner diameter and 1.7 µm particle size from Waters corporation, USA). Peptides
were separated using gradient elution mode. The separation was achieved with the
formation of linear gradient of 36 minutes containing LC buffer A (2% acetonitrile in
water with 0.1% acetic acid) and LC buffer B (acetonitrile with 0.1% acetic acid),
gradient-1-5% buffer B in 2 min, 5-60% B in 25 min, 60-99% B in 28 min, 99-1% B in
36 min). The peptides were eluted at a flow rate of 400 nL/ min and analysed using a
mass spectrometer with a nano ESI source. The electrospray voltage was 1.54 kV and
without sheath and auxiliary gas flow. The eluted peptides were measured in positive
polarity mode with m/z range of 300 -1700, and the MS in profile mode at resolution of
60000 FWHM in Orbitrap with a target value of 1 * 106. Further, the MS/MS was
performed in centroid mode by CID fragmentation at 35% normalised energy in LTQ
with an isolation width of 2 Da triggering with a threshold count of 2000 with a target
value of 1 * 104 or with a maximum injection time of 100 ms activated for 30 ms. The
target ions already selected for MS/MS were dynamically excluded for 30 s. Only doubly
and triply charged ions were triggered for tandem ms analysis.
36
Mass spectrometry data analysis of the purified recombinant proteins
LTQ-Orbitrap XL mass spectrometer raw data was imported to Genedata Refiner MS. In
general, default parameters were used for each activity of Refiner MS. Firstly, the raw
data were binned into the Refiner MS workflow. After loading the raw data, the retention
time (RT) range was restricted with the RT minimum of 10 min and RT maximum was
not defined. The data was cleaned by subtracting the background noise by enabling the
chromatogram smoothing of 3 scans and disabled chemical noise smoothing. Further,
intensity thresholding, RT and m/z structure removal were done with default parameters.
Each individual time resolved data set was then independently aligned in retention time
direction by the pairwise alignment based tree scheme. Intensity thresholding, peak
detection, isotope clustering and MS/MS consolidation were done before MS/MS search.
MS/MS search was performed with a S. aureus_8325_iRT_BSA and E. coli_K12
specific FASTA protein database using the MASCOT search engine. The settings applied
for the MASCOT search engine were a peptide tolerance 10 ppm, a peptide isotope error
of 1, an ion fragment (MS/MS) tolerance of 0.2 Da, an ion charge of 2+ and 3+,
calculated the monoisotopic mass and performed decoy search. Oxidation of methionine
and carbamidomethylation of cysteine were defined as variable modifications. Further,
peptides were filtered based on a maximum false discovery rate (FDR) of 1% with a
deterministic target decoy method. Then, peaks were annotated with a m/z window of
0.01 Da and a RT window of 0.2 min. Further, identification and quantification of protein
was performed using protein inference with the deterministic method and Hi3
quantification with minimum peptide count of 2. Finally, intensities of the ranked
peptides with more hits were summed up and exported to Genedata analyst. The purity of
proteins was calculated and expressed in percentage.
Principle of FLEXMAP 3D®
and workflow
The FLEXMAP 3D® is basically as a flow cytometry designed by Luminex. The key
feature of this innovative technology is multiplexing of up to 500 different proteins in a
single well, and with wide dynamic range (~ 4.5 logs), fast analysis time, and low
amount of samples (~ 4.0 µL). In this study, we developed S. aureus multiplex assays
using FLEXMAP 3D®. The carboxylated magnetic beads (magplex) are 6.5 µm super
paramagnetic beads, with similar excitation and unique emission profiles which provide
37
unique spectral characteristics within individual magnetic regions and are internally
encoded with three spectrally different fluorochromes (red, infra-red, orange-red) [208].
The magplex beads are identical in size and differ in the stoichiometry of the internal
classification dyes. Each magplex bead region has similar excitation, but unique emission
spectra. Magnetic beads are interrogated by two lasers; red and green. The red laser
excites the internal fluorochromes at 635 nm and the emission from the internal dye is
detected by two avalanche photo diodes. Then, a green laser excites the surface attached
reporter phycoerythrin at 532 nm and emission is detected by a photo multiplier tube.
The internal dye emission is deconvoluted as region events by a high-speed digital signal
processor and quantitated as the reporter signal bound to protein-magnetic beads. The
resulting data is denoted by median fluorescence intensity (MFI). Batch setup and data
analysis were done using software xPONENT®
4.2. The Figure 6 represents the
workflow of a serological assay using a indirect detection method.
Antigen conjugation to magnetic beads
Generation of bead mix
Multiplexing in well plates
Serum/Plasma binding to bead
mix
Addition of phycoerythrin detection antibodyData acquisition in FlexMAP3D®
Data analysis and validation
Bead regions
MF
I
Figure 6. Immunoproteomics workflow
38
Covalent conjugation of S. aureus antigens to magnetic beads
The carbodiimide chemistry is a simple two-step process during which the carboxyl
groups of magnetic beads are first activated with EDC (1-Ethyl -3-[3-
dimethylaminopropyl] carbodiimide hydrochloride) reagent in the presence of Sulfo-NHS
(N-hydroxysulfosuccinimide) to form a sulfo-NHS-ester intermediates (Figure 7). The
reactive intermediate is then replaced by reacting with the primary amine of the coupling
molecule (antibody, protein, linker or peptide) to form a covalent amide bond.
Primary amines on purified antigens were covalently coupled to carboxylated
functionalised magplex beads with unique bead addresses. Briefly, 6.25 * 105 of the stock
magnetic sphere was transferred to a 96 well half area microplate and allowed to
equilibrate at room temperature. During the whole workflow, beads were protected from
light to avoid photobleaching. The plate was placed on a magnetic separator for 3 min
and the magnetic beads collected. Activation of beads was carried out with 150 µL of 100
mM monobasic sodium phosphate (pH 6.2). Subsequently, carboxy group activation was
initiated with EDC – Sulfo-NHS crosslinking master mix containing 15 µL of 50 mg/mL
sulfo-NHS in water free DMSO, 15 µL of 50 mg/mL EDC in activation buffer and 120
µL of activation buffer, incubated for 20 min, shaking at 900 rpm at RT. Activated beads
were washed thrice with 50 mM MES buffer (pH 5.0). About 125 µL of recombinant S.
aureus antigen (100 µg/mL) was covalently coupled to the activated beads, and incubated
for 2 hours, shaking 900 rpm at RT. The antigen coupled magnetic beads were washed
thrice with 100µL of wash buffer (PBS, 0.05% (v/v) tween20 pH 7.4). Finally, the
concentration of magnetic spheres was adjusted to 125 beads/µL with blocking-storage
buffer (PBS, 1 % (w/v) BSA, 0.05 % (v/v) proclin300 pH 7.4) and the magnetic
microspheres were stored in amber eppendorf tubes and stored at 4°C.
39
Step - 1 Step - 2
Figure 7. Reaction mechanism of carbodiimide coupling. It’s a two-step process, (step 1)
carboxylated magnetic beads coupled with Sulfo-NHS and EDC, form a semi-stable amine reactive
intermediate ester. In the step 2, the reactive ester intermediate is replaced by the primary amine of
protein.
Evaluation of magplex bead-antigen conjugation
To ensure the efficiency of bead-antigen conjugation, antigen coupled beads were
verified with the antibody directed against the recombinant tagged antigen. Briefly,
antigen coupled magnetic beads were vortexed and sonicated for 3 min, 50 µL of antigen
coupled bead master mix was transferred to well plates and co-incubated with 50 µL of
Penta HIS/Anti-STREP tag antibody (final concentration 10 µg/mL) for 45 min, shaking
900 rpm at RT. Penta HIS/Anti-STREP tag coupled beads were washed thrice with
100µL of wash buffer (PBS, 0.05 % (v/v) tween20 pH 7.4). The beads were resuspended
in 50µL RPE-conjugated goat anti-mouse IgG (final concentration 5 µg/mL), incubated
for 30 min, shaking 900 rpm at RT and the unbound substrates were washed thrice with
100 µL of wash buffer (PBS, 0.05 % (v/v) tween20 pH 7.4). The beads were resuspended
in 100 µL of sheath fluid. Measurements were performed on the FLEXMAP 3D®
analyser system with the following conditions: a sample size of 80 µL, sample timeout 60
s, bead count 10000, and gate settings 7500-15000 under standard PMT. The assay was
performed twice; MFI values reflected the binding efficiency of antigen coupled beads.
After the successful conjugation of magplex-antigen, all the beads were pooled to
generate a master mix (multiplex).
Pre-adsorption of sera with assay buffer containing E. coli lysate
Pre-adsorption of E. coli lysate with sera reduces the cross-reactivity of E. coli protein
specific antibodies. For the preparation of E. coli lysate, a BL21 E. coli strain was
obtained from the Department of Microbiology, University of Greifswald. Overnight
40
cultures of E. coli (BL21) were grown in Luria-bertani medium at 30°C with shaking at
180rpm. After centrifugation the pellet was resuspended with TE lysis buffer (10mM
Tris, 1mM EDTA, pH 8.0). Resuspended cells were lysed by ultrasonication with the
micro-tip on ice using five 10 s bursts at the amplitude intensity of 60 %. Serum/plasma
samples were prepared subsequently in seven dilutions (50, 500, 1000, 10000, 50000,
100000, 200000 folds) using assay buffer containing E. coli lysate (10% (v/v), 45% PBS,
1% (m/v) bovine serum albumin pH 7.4, 45% (v/v) LCB).
Serological bead based assay method validation
To study the influence of E. coli lysate, magplex-antigen beads were bound to the pooled
human serum (PHS) diluted with assay buffer containing two different compositions
(panel 1 and 2). The composition 1 (panel 1) contains 10% E. coli lysate (v/v), 45% PBS,
1% (m/v) bovine serum albumin pH 7.4, 45% (v/v) LCB and the composition 2 (panel 2)
contains 50% PBS, 1% (m/v) bovine serum albumin pH 7.4, 50% (v/v) LCB. The PHS
samples were prepared subsequently in seven dilutions (50, 500, 1000, 10000, 50000,
100000, 200000 folds) using two different compositions (panel 1 and 2).
For the complete study, a negative control sample has been used. We collected the serum
samples from two healthy fellows (HF). The HF samples were prepared subsequently in
seven dilutions (1:50, 500, 1000, 10000, 50000, 100000, 200000) using assay buffer
containing E. coli (10% E. coli lysate (v/v), 45% PBS, 1% (m/v) bovine serum albumin
pH 7.4, 45% (v/v) LCB).
Quantitation of antibodies by serological assay using FLEXMAP 3D®
A serological bead-based array by indirect detection was developed for the detection of
human antibodies against S. aureus antigens. The suspension array technology (SAT) by
FLEXMAP 3D® is a miniaturised and high throughput technology which allows the
parallel sample processing during the assay. Serum/plasma samples were stored at -80°C
and then brought to room temperature for sample preparation. 50µL of diluted sera
samples were co-incubated with 125µL of antigen-coupled master mix beads (125 beads
per region) and this mixture was incubated for 18 to 20 h (overnight), orbital shaking 900
rpm at 4°C. The plates were washed thrice with 100µL of wash buffer (PBS, 0.05% (v/v)
tween20 pH 7.4). To visualise the bound human antibodies, the beads were resuspended
41
in 50µL of RPE-conjugated goat anti-human IgG (final concentration 5 µg/mL),
incubated for 60 min on the orbital shaker (900 rpm). The unbound substrates were
washed thrice with 100µL of wash buffer (PBS, 0.05% (v/v) tween20 pH 7.4) and finally
resuspended in 100µL of sheath fluid, vortex for 2 min. The measurements were
performed on FLEXMAP 3D® with the following conditions: sample volume of 80 µL,
sample timeout 60 s, bead count 100, and gate settings 7500 – 15,000 under standard
PMT. The assay was performed in duplicate.
Data analysis
All measurements were background corrected by subtracting the MFIs of sample beads
(incubated with serum/plasma) from MFIs of control beads (incubated with assay buffer).
The resulting background normalised MFI values were used for calculating the intensity
at half maximum using Clark´s theory interaction model, a non-least square algorithm (R
script for calculating the intensity at half maximal was developed by Dr. Stephan
Michalik). The product of the half-maximal MFI and the corresponding serum/plasma
dilution was calculated as this reflects the antigen binding intensity of antibodies
contained in each serum/plasma sample. Calculations were performed using a R package
(R 3.0.1).
Calculating the intensity at half maximum by Clark’s theory
Antigen-Antibody relations are similar to enzyme-substrate interaction. For any chemical
reaction, the reaction proceeds predominantly in one direction, but the reverse rate
steadily increases until the forward and reverse speed are identical. At this stage, the
reaction is said to have reached its equilibrium. The association between an antigen and
antibody involves various noncovalent forces between the antigenic determinant or an
epitope of the antigen and the variable region of the antibody (VH/VL). The non-covalent
forces behind the antigen-antibody interaction are as follows: hydrogen bonds, ionic
bonds, hydrophobic interactions, van der Waals forces. The noncovalent interactions
between antigen-antibody are weak compared to covalent interactions. These interactions
happen within the short distance of about 1 angstrom (Å). The combined strength of the
noncovalent interactions between a single antigen-binding site on an antibody and a
single epitope is the affinity of the antibody for that epitope. Antibodies with low affinity
42
bind the antigen weakly and they dissociate readily. Antibodies with high affinity bind
the antigen strongly and bind for longer time and this antibody affinity is a quantitative
measure of binding strength. The association between the antibody (Ab) and an antigen
(Ag) can be described in the following equation 1.
(Equation 1)
Where the rate constant for the forward reaction (association) is Ka and the rate constant
for the reverse reaction (dissociation) is Kd. The ratio of Ka/Kd is equal to the
equilibrium constant Keq and can be written as Ka/Kd = Keq, measure of affinity. Keq is
the equilibrium constant for the above reaction and can be calculated from the ratio of the
molar concentration of bound Ag-Ab complex to the molar concentration of unbound
antigen and antibody during equilibrium and equation 2 can be written as follows [209].
(Equation 2)
Further the above equation 2 can be rewritten as follows
(Equation 3)
Where AB free and AG free are free antibody and antigen concentration, B is the bound
antigen: antibody complex concentration and the above equation 3 can be rewritten as
follows.
(Equation 4)
(Equation 5)
43
The equation 5 is the equation for a rectangular hyperbola with horizontal asymptote
corresponding to 100% saturation of AGtotal, such that [bound] = [antigen]. Figure 8
depicts classical hyperbolic saturation curve. The ratio B/AGtotal is also referred to as F
(fractional occupancy). Keq is defined as the concentration of free antibody at which 50%
of the antigens are occupied (i.e., fractional occupancy = 0.5). The equation 5 can be
rewritten as follows (Equation 6). In order to derive the intensity at half maximum, the
equation 6 can be rewritten as equation 7.
Antibody freeKeq
B
AG total F=1
Figure 8. The classical hyperbolic binding curve, expressed at the fractional occupancy (f) of the
antigens.
(Equation 6)
(Equation 7)
Where MFI = B = Antigen-Antibody complex, MFImax = AGtotal = Saturated antigen-
antibody complex, Dil. at MFImax/2 = Kd = concentration of free antibody at which 50%
44
of the antigen are occupied. For instance; the below Figure 9 depicts the hyperbolic
model for seven serial dilutions which leads to saturation in our experimental setup.
Dilution
MFI
1:2
00000
1:1
00000
1:5
0000
1:1
000
0
1:1
000
1:5
00
1:5
0
Antibody concentration
Figure 9. Hyperbolic model in our experimental setup.
Figure 10 represents the saturation curve for one antigen (LukF-PV) in two different
samples. The ratio between two different samples is about 4.25 fold. Based on this
mathematical model (Clark`s theory), intensities at half maximum were calculated.
Figure 10. Hyperbolic curve obtained from our mathematical model using Clark`s theory, for one
antigen (LukF-PV) in two different sample. Red dotted lines represent the concentration at half
maximum.
45
Scaling of data
Data scaling involves the transformation of axis in such a way as to underline the features
or trends in the data that might be obscured by dominant signals/responses. To represent
the data in different graphical methods, the raw MFI value (calculated intensity) of each
antigen was log10 transformed to generate data values.
Mann-Whitney U test
The Mann-Whitney U test is also known as Wilcoxon rank sum test, tests for difference
between two groups on a single, ordinal variable with no specific distribution [210]. The
non-parametric Mann-Whitney U test can be used for testing the null hypothesis that two
samples come from the same population. Although non-parametric, the Mann-Whitney U
test still assumes that the distributions of the datasets to be compared are similar in shape.
The major difference between the Mann-Whitney U test and Student T test is that the
latter involves the concept of normal distribution.
Statistical analyses
Statistical analyses were performed using the Genedata AG - Analyst™
and
GraphPadPrism5 packages. The resulting antigen binding intensity (calculated intensity
at half maximum) values based on clack`s theory were imported into Genedata analyst™
.
The raw MFI values (calculated intensity) were transformed to log10 and normalised with
central tendency median and exceptional in study-1, i.e. only transformed to log10. We
performed the significance test in order to determine the probability of obtaining the
value of a test static assumed that the null hypothesis is true. From the p-value, it suggests
that the probability of rejecting the null hypothesis although it is true. When the p-value
is below the cutoff range, then it is termed as statistically significant. In clinical studies,
the significant cutoff is 0.05 and lower p-value, the more acceptable is the null
hypothesis. Further statistical elements such as Wilcoxon test, clustering or classification
using principal component analysis (PCA), partial least square (PLS) and other prediction
analysis were accomplished using Genedata analyst™
.
46
Results
Method optimization and calibration
Serological assay is performed to study antigen–antibody responses and as a starting
point S. aureus antigen library was generated. For that, recombinant S.aureus antigens
were overexpressed using E. coli vectors in LB media and further the overexpressed
antigens were purified using NiNTA affinity chromatography. The purity was evaluated
by running SDS PAGE.
Figure 11. SDS-PAGE-based qualitative analysis of the purified S. aureus antigens. Ten µg of
purified antigens were loaded on the lanes and it corresponds to ClfA (1), Cell wall hydrolase (2),
Cna domain 2 (3), Chips (4), IsdA (5), IsdH (6), LukF (7), Atl (8), Fbp related (9), Cna domain 1 (10),
Enolase (11), SceD (12), HysA (13), LukE (14), LukS (15), Plc (16), SasG (17), uncharacterised
protein – SAOUHSC00106 (18), LytM (19), SCIN (20), SdrD domain 2 (21), Ssl7 (22), Spa (23), Ssl1
(24), EpiP (25), Ssl3 (26), IsdB (27), SdrD domain 1 (28). The molecular weight of marker proteins is
indicated (in kDa). The gel and the figure were provided by Protagen AG.
The purity of the antigens was defined based on the coomassie staining of the SDS gels.:
The above Figure 11 depicts the purity of the isolated antigens in the respective lanes
against marker proteins (M). The obtained yield for clumping factor A (lane 1) was less
compared to other antigens. Secondly, multiple bands were identified for the antigens
IsdH (lane 6) and HysA (lane 13).In principle, histidine tagging had been used during the
plasmid construction so that the targeted antigens were expected to be eluted for sure.
The multiple bands detected for these antigens were unexpected and needed to be further
investigated by mass spectrometry.
47
Mass spectrometry based quantification of S. aureus and E. coli proteins
In order to distinguish if the additional bands are fragments of the target protein or host
cell proteins, we performed an identification of the proteins contained in the sample and
MS based Hi3 quantification for the subset of proteins. The detected peptides were
searched against a mascot database containing S. aureus_8325 and E. coli_K12 and
quantified based on the Hi3 method using area normalisation. The purity of the proteins
was expressed in percentage (%). The MS results of the purified proteins confirmed
purity greater than 90.0 % in spite of the presence of degradation bands in the SDS
PAGE. For instance, hyaluronate lyase (HysA) and intracellular serine protease (EpiP)
have a purity of above 95% as shown in Figures 12 and 13 (mass spectrometry). Thus,
most of the additional bands likely contain degradation products of the proteins produced
from E. coli. In the Figure 13, two lanes of the 1-D SDS PAGE belongs to the marker
(M) and purified recombinant EpiP and the MS results also confirms the purity of EpiP
HysAMkDa
Mass spectrometry1-D SDS PAGE
96.11
1.050.660.070.800.350.270.220.170.29
% P
urit
y
Identified proteins
Hyaluronate lyase/HysA
Elongation factor/Tu
Peptidyl-prolyl cis-trans
isomerase
Pyruvate formate lyase
Tryptophanase
Acyl carrier protein
Chaperone protein DnaK
Protein disaggregation
chaperone
4-deoxy-L-threo-5-
hexosulose-uronate ketol-
isomerase
Figure 12. Recombinant hyaluronate lyase (HysA) overexpressed in E. coli and its analysis by 1-D
SDS PAGE and mass spectrometry.
48
kDa M EpiP
1-D SDS PAGE Mass spectrometry
98.64
0.14 1.040.04 0.02 0.06
% P
urit
y
Identified proteins
Intracellular serine
protease/EpiP
Elongation factor/Tu
Tryptophanase
Chaperone protein DnaK
Protein disaggregation
chaperone
4-deoxy-L-threo-5-
hexosulose-uronate ketol-
isomerase
Figure 13. Recombinant Intracellular serine protease (EpiP) overexpressed in E. coli and its analysis
by 1-D SDS PAGE and mass spectrometry.
Method validation
The serological method was validated with pooled human serum samples. In this
validation study, about 115 multiplexed antigens were incorporated and they were
conjugated to the magplex-beads.
To study the influence of E. coli lysate in the reduction of cross binding with Anti-E. coli
antibodies, human samples were pooled and prepared seven serial dilutions using assay
buffer containing two different buffer constituents (constituent 1 and constituent 2). The
constituent 1 contains 45% (v/v) LCB, 45% (v/v) CBS with 10% (v/v) of E. coli lysate
and the constituent 2 contains 50% (v/v) LCB, 50% (v/v) CBS. The prepared samples
were incubated with 115 antigen coupled magplex and then detected using PE anti human
IgG. The samples were analysed with FLEXMAP 3D® and the MFIs from seven serial
dilutions were further calculated for intensity at half maximum using Clark’s theory. The
calculated intensities were further evaluated between the two different conditions and the
ratios (constituent 1/constituent 2) were calculated and depicted in Figure 14. For
instance, Tuf, Antitoxin MazE, SigB, Autolysin – amidase domain, Seo, MsrA2, LytN,
Eno, MsrB, GroEL, alpha-hemolysin (Hla), trigger factor (Tig) shows high signal for the
49
constituent 2 and their ratios were less than 0.5. The signals detected by human IgG were
significantly reduced by the presence of E. coli lysate in the assay buffer, indicating the
potential interference of E. coli proteins, probably because they contain similar epitopes
as the corresponding S. aureus proteins (e.g. Tuf).
50
0 0.2 0.4 0.6 0.8 1 1.2 1.4
Tuf
Antitoxin MazE
SigB
PknB
Atl - AM domain
SsaA2
Seo
MsrA2
LytN
Eno
MsrB
GroEL
Hla
AIP - Typ1 - Thiolacton
Tig
Pbp2
HysA
Atl
HlgA
FadB
Fbp related
GraB
SAOUHSC_00106
Ssl2
IsaA
Aur
PurA
EssC - Domain 1
EntA
SACOL0480
CitC
Luk F PV
SAOUHSC_02241
HlgB
EssC - Domain 1
LukD
Sak
HlgC
GreA
SspB
SACOL1788
SplB
Sod
AIP - Typ4
Flr
IsdB
AIP - Typ4 - Thiolacton
EsxA
SplA
ClfB
EpiP
Coa
GlpQ
SACOL0479
IsaB
Ssl1
Ssl10
SdrD Domain 2
SACOL1802
Seb
Map-W
Sec
LukE
Sel
LukS
Scn
Sem
Nuc
Sec
Tsst-1
SdrF Domain 2
SACOL0444
Ssl7
SACOL2197
Hld
AIP - Typ2 - Thiolacton
SplD
Sek
Emp
Ear
Geh
SplC
IsdH
Plc
SdrE
Atl - GL domain
AIP - Typ1
SdrD Domain 1
Chips
IsdA
SceD
PrsA
AIP - Typ3
Hlb
Seq
SdrC
sdrG
SasG Domain 2
LytM
Truncated Map-W
Ssl11
Cna dm1
Sei
Lip
Cna Domain 2
SACOL0985
Set15
SdrF Domain 1
SasG Domain 1
Ssl3
AIP - Typ2
Efb
Ssl5
Ratio
An
tigen
s
Ratio (With E. coli lysate/Without lysate)
Figure 14. Influence of E. coli lysate in the reduction of binding with anti-E. coli antibodies using
human pooled serum samples. The ratio between the MFIs from two different buffer constituents
were calculated.
51
In the validation part range has been determined using the serum samples from healthy
fellows. Serum dilution range was optimised to eliminate the false positive or negative
results. The serum samples from healthy individuals were obtained and they were serially
diluted to 22 successive dilutions (2, 4, 8, 10, 20, 40, 50, 100, 200, 300, 400, 500, 600,
700, 800, 900, 1000, 5000, 10000, 50000, 100000, 200000 fold) using assay buffer
containing (10% (v/v) E. coli lysate, 45% (v/v) CBS, 45% (v/v) LCB). The diluted
samples were incubated with 121 multiplexed antigens, PE anti human IgG was used to
detect the bound antibodies. As an example, Figure 15 and 16 shows the range (curve) of
serum samples of 22 dilutions for two antigens FPRL-1 inhibitory protein and foldase
protein (PrsA). In the Figure 15 and 16, X-axis represents the inverse of dilution (1/x
fold) and Y-axis represents the MFI. In the Figure 15, X axis scale has been transformed
to log10 scale and in the Figure 16, both X and Y axis are in the linear scale. The
correlation (r2) for the dilution range C1 which includes 22 dilutions excluding raw serum
for the FPRL-1 inhibitory protein is -0.3745 whereas for the foldase protein PrsA is
0.9720. In general, a linear dilution trend is seen between 40fold and 200000fold (C6).
There is a loss of correlation (r2) when lower fold dilutions were included in the series (2,
4, 8, 10, 20 folds) and this correlation is highly antigen dependent. Figure 17 shows the
overlay of seven correlations (C1, C2, C3, C4, C5, C6 and C7) for 121 antigens. On
average, the correlation of C6 (40 fold to 200000 fold) is about 0.9121 and the correlation
of C7 (50 fold to 200000 fold) is about 0.8189 for 121 antigens.
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
0.000001 0.00001 0.0001 0.001 0.01 0.1 1
MF
I
Serum dilution
FPRL-1 inhibitory protein
Figure 15. Serum dilution range for FPRL-1 inhibitory protein. For better visualisation, X axis scale
has been transformed to log10. The correlation for 22 dilutions (C1) excluding raw sample is -0.3745.
52
0
10000
20000
30000
40000
50000
60000
70000
80000
0 0.1 0.2 0.3 0.4 0.5 0.6
MF
I
Serum dilution
Foldase protein (PrsA)
Figure 16. Serum dilution range optimisation for foldase protein (PrsA). The correlation for 22
dilutions (C1) excluding raw sample is 0.9720.
-0.60
-0.40
-0.20
0.00
0.20
0.40
0.60
0.80
1.00
1.20
-1 19 39 59 79 99 119CO
RR
EL
AT
ION
ANTIGENS
C1 C2 C3 C4 C5 C6 C7
Figure 17. Serum dilution optimisation in healthy individuals and its correlation C1, C2, C3, C4, C5,
C6 and C7; where the C1 – Correlation of serum dilution excluding raw serum, C2 - Correlation of
serum dilution excluding raw serum and 2 fold, C3 - Correlation of serum dilution excluding raw
serum, 2 and 4 fold, C4 - Correlation of serum dilution excluding raw serum, 2, 4 and 8 fold, C5 -
Correlation of serum dilution excluding raw serum, 2, 4, 8 and 10fold, C6 - Correlation of serum
dilution excluding raw serum, 2, 4, 8, 10 and 20 fold, C7 - Correlation of serum dilution excluding
raw serum, 2, 4, 8, 10, 20 and 40 fold.
Stability of antigens immobilised on the magplex
For a large set of samples, a bulk quantity of magplex-antigen bead mix is required. To
study the stability of magplex coupled with antigen, stability of the magplex-antigen
beads was monitored over a time period of 28 months in order to attain comparable
results between different sample batches. The histidine/streptavidin tag of 72
recombinant antigens immobilised on the magplex-beads was assayed with anti-His/Strep
mouse IgG and further conjugation with a RPE labelled anti-mouse antibody for the
detection. The intensities obtained from the first measurement (T0) were taken as a
reference. In the Figure 18 X-axis represents the antigens and Y-axis represents the
53
relative intensities (%) of 72 antigens in different time points like T1 (8months), T2
(18months), T3 (22months), and T4 (28months). We observed some variations in the %
relative intensity between the time intervals for proteins like MsrA2 and this is due to the
technical variations. We sorted the recombinant antigens based on the tags. Among the
72 antigens; 40 carry histidine and 32 streptavidin tags, respectively. For histidine tag
antigens, the range of relative intensities was 9% - 122% (mean 63%), for the streptavidin
tag antigens, the range of relative intensities was 30% - 102% (mean 77%).
1.00
10.00
100.00
0 10 20 30 40 50 60 70
% R
EL
AT
IVE
IN
TE
NS
ITY
ANTIGENS
T1 (8 months) T2 (18 months) T3 (22 months) T4 (28 months)
Histidine tag Streptavidin tag
Figure 18. Stability monitoring of antigen immobilised magplex beads during the different time
intervals.
Evaluation of magplex-antigen coupling (coupling control)
The magplex-antigen coupling was evaluated using mouse monoclonal anti-histidine/
anti-streptavidin antibodies. Here, we determined the background intensity of magplex-
antigen complex (without mouse monoclonal anti-histidine/anti-streptavidin antibodies).
In this experiment, 72 antigens were incorporated to study the background MFI. The
background signal was minimised by the use of magnetic microspheres and various
formulated buffers such as low cross buffer (LCB) in combination with carboxy block
store buffer (CBS). This cocktail buffer significantly reduced the background signal in
magplex-antigen complex suspensions (Figure 19). Low cross buffer composition was
not disclosed by the manufacturer (Candor) and carboxy block store buffer contains PBS,
pH 7.4 + 1.0% BSA. A majority of magplex-antigen complex suspension have shown a
low background intensity with an average MFI value of about 25 median fluorescence
intensity (MFI) (Figure 18). Higher background intensity of about 400 MFI was observed
for Sle1 (N acetylmyl-L-alanine amidase) (Figure 19). Further, size of the protein (Da)
was correlated with the MFI of the background, and it is depicted in the Figure 20.
54
1 10 100 1000
Cna Domain1
LukEv
EpiP
LytN
MsbA
Ssl7
SdrF Domain2
Aureolysin
SplE
Hld
SdrH
SigB
SbnG
SdrD Domain2
SceD
GlpQ
Bbp Domain2
Ssl2
LytM
SdrG
SAOUHSC00106
Scin
LukDv
SdrF Domain1
EC Fbp
HlgA
SdrC
SplF
IsdE
SdrE
LukS
ClfB
GraB
Ssl5
Spa
SdrD Domain1
EftB
Flr
EftA
ClfA
FnBP A
Hyaluronate lyase
LukF
IsdB
Ssl10
SspA
SspB2
FnBP B
Fibrinogen binding protein related
Protein 2160 esterase
Surface proteinG Domain1
Ferrichrome BP
Ssl12
Surface proteinG Domain2
Bbp Domain1
Autolysin
GapDH
IsdC
Ssl3
Cna Domain2
Hlb
LukS PV
HlgA
Enterotoxin type A
Eno
Scc
IsdH
MazE
IsdA
LukF PV
3 oxoacyl ACP reductase
Sle1
MFI
An
tig
ens
Mean of Background (MFI)
Figure 19. Magplex-antigen complex incubated with PE-Anti mouse IgG and its mean of background
MFI.
55
0
1
10
100
1000
0 20000 40000 60000 80000 100000 120000 140000 160000 180000 200000
MF
I
Protein size (Da)
Correlation - Protein size vs. MFI
Figure 20. Correlation of protein size (Da) with background MFI for 72 antigens.
To study the influence of low cross buffer (LCB) in the reduction of background MFIs,
two experiment panels were designed. In the first panel, magplex-antigen complex in the
presence of LCB and CBS, and in the second panel magplex-antigen complex in the CBS
only. This experiment was performed with three replicates per panel and Figure 21
represents the calculated ratio (LCB+CBS/CBS) of mean MFIs for each antigen. Most of
the antigen shows the ratio of 1.0 indicating that the background MFIs of the two panels
were similar.
The very most important factor for the accuracy of the data are the bead counts. Based on
the luminex expert’s comments, the minimum number of beads required to measure the
stable MFI is 35 bead counts.
56
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4
PVL SAOUHSC01381
EC Fbp
LytM
SdrD Domain1
3 oxoacyl ACP reductase
SdrF Domain1
Protein 2160 esterase
SspA
Bbp Domain1
ClfA
PVL SAOUHSC01382
SdrE
SAOUHSC00106
SdrF Domain2
Gamma hemolysin comp A
EftB
Surface proteinG Domain2
ClfB
Glutamyl endopeptidase
Ferrichrome BP
FnBP B
SAOUHSC00384
IsdA
Autolysin
Hld
EftA
Eno
Hyaluronate lyase
Enterotoxin type A
Fibrinogen binding proien related
Spl IC
SplE
SAOUHSC01124
IsdB
SceD
IsdE
IgG binding proteinA
Bbp Domain2
HlgA
ATP bind protein GrfA
Aureolysin
Surface proteinG Domain1
SdrD Domain2
GlpQ
PhospholipaseC
SdrG
LukDv
SAOUHSC02243
SplF
FnBP A
SAOUHSC00386
IsdC
VWbp
FPRL1
SAOUHSC02241
LytN
Cna Domain1
IsdH
LukEv
Scin
SdrC
Fbp precursor SAOUHSC01115
SdrH
SAOUHSC00390
GapDH
SAOUHSC00395
Cna Domain2
MazE
SAOUHSC00392
RNA polymerase sigma factor
SbnG
N acetamylLalanine amidase
Ratio
An
tig
ens
Ratio (LCB + CBS/CBS)
Figure 21. Influence of low cross buffer in the reduction of background MFIs between two panels,
Panel 1 – LCB + CBS and Panel 2 – CBS only. About 72 antigens were included in this experiment
and the ratio between two panels were depicted thru dot plots.
57
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
LytM
PVL SAOUHSC01381
Eno
SAOUHSC00386
SdrF Domain1
IgG binding proteinA
Gamma hemolysin comp A
MazE
SbnG
Aureolysin
HlgA
SdrC
3 oxoacyl ACP reductase
IsdC
SAOUHSC00392
ClfB
RNA polymerase sigma factor
SdrF Domain2
Scin
EftB
Fbp precursor SAOUHSC01115
N acetamylLalanine amidase
SdrG
PVL SAOUHSC01382
SspA
GapDH
SAOUHSC00106
Protein 2160 esterase
Fibrinogen binding proien related
SAOUHSC01124
SAOUHSC00395
ATP bind protein GrfA
IsdA
Cna Domain2
Ferrichrome BP
SAOUHSC02241
SAOUHSC02243
SdrD Domain1
Surface proteinG Domain2
VWbp
SAOUHSC00384
SplE
Glutamyl endopeptidase
Enterotoxin type A
Cna Domain1
FPRL1
GlpQ
Bbp Domain2
FnBP B
PhospholipaseC
SdrE
FnBP A
SAOUHSC00390
Spl IC
EC Fbp
LukEv
SceD
Hld
IsdH
SdrD Domain2
EftA
Bbp Domain1
LukDv
IsdE
ClfA
LytN
SplF
Hyaluronate lyase
SdrH
Autolysin
Surface proteinG Domain1
IsdB
Bead counts
An
tig
ens
Bead counts - Background Vs Anti-histidine
Background
Anti-histidine
Figure 22. Evaluation of bead consistency throughout the background and control coupling
measurements during 72 antigen multiplexing. The bar plot represents the mean amount of beads
during the analysis of background (green bar) and anti-histidine (blue bar).
To verify the consistency of bead counts throughout the assay, bead counts were
monitored. Bead counts were measured in background and control coupling
measurements in order to verify the loss the beads. The Figure 22 represents the amount
of beads encountered during background without anti-tag (green bar) and with anti-tag
(blue bar) measurement. Here, on average 200 beads were observed for every antigen
which enhance the quality of data set.
58
When the histidine tag recombinant antigens were bound to the magplex-beads, a master
mix of about 72 antigens were multiplexed. The coupling efficiency was evaluated using
monoclonal anti-histidine antibodies from mouse and further conjugation with
phycoerythrin anti-mouse detection antibody. The mean MFIs of the amount of antigens
bound to the magplex-beads (control coupling) are depicted in the Figure 23. Antigens
such as Serine protease E, formyl peptide receptor-like 1 inhibitory protein (FPRL-1),
SdrF, Glutamyl endopeptidase were shown less than 5000 MFI. We established that the
antigens with MFI above 10000 were considered for further serological assay and 35
bead counts as the minimum requirement for the data accuracy and the method was
validated. The median coefficient of variation (% CV) for coupling efficiency was 1.17
ranging from 0.30 to 4.80 and the overall %CV for intra-assay was below 5.0%. During
the data analysis, the amount of antigen bound to beads was always considered by
antigen-specific normalisation of the sample (serum/plasma), serial dilution MFI values
to their corresponding coupling control values.
59
0 5000 10000 15000 20000 25000 30000 35000 40000 45000
SplEFPRL1
SdrF Domain2Glutamyl endopeptidase
SplFSAOUHSC01124
Hyaluronate lyaseFnBP A
SAOUHSC00392SdrD Domain2
Surface proteinG Domain1FnBP B
ClfBBbp Domain2
EftAATP bind protein GrfA
SdrCAureolysin
SdrGFbp precursor SAOUHSC01115
SbnGClfAIsdA
Cna Domain2SdrE
HldIsdH
PhospholipaseCCna Domain1
SAOUHSC00395Autolysin
SdrHIsdC
SAOUHSC00106Surface proteinG Domain2
SAOUHSC00390IgG binding proteinA
IsdESAOUHSC00384
Bbp Domain1N acetamylLalanine amidase
GlpQVWbpSpl IC
SAOUHSC02241SAOUHSC00386
EC FbpScin
SceDLukDvLukEv
IsdBEnterotoxin type A
Ferrichrome BPEftB
HlgALytN
RNA polymerase sigma factorSAOUHSC02243
SdrD Domain1GapDH
SdrF Domain1Gamma hemolysin comp A
Fibrinogen binding proien relatedProtein 2160 esterase
PVL SAOUHSC01382PVL SAOUHSC01381
SspAMazE
Eno3 oxoacyl ACP reductase
LytM
MFI
An
tig
ens
Control coupling
Figure 23. Amount of antigens bound to the magplex beads. This bar plot plotted against MFI on the
X axis and the antigens on the Y axis.
60
Quantitation of antibody responses (antigen coupling)
The serum/plasma samples were diluted and coupled to master mix (magplex-antigen
beads) and the antibody responses were measured by a serological indirect assay with
FLEXMAP 3D® (refer materials and methods for sample preparation, method
parameters). Intensity at half maximum was calculated based on the Clark`s theory
mathematical model and the hyperbolic curve for all sample sets was obtained using R
studio (R script for calculating the intensity at half maximal was developed by Dr.
Stephan Michalik). The final outcome was calculated intensity (intensity at half
maximum) which represents the antibody responses of each sample to every antigen. The
obtained datasets were further evaluated with statistical analysis using Genedata –
Analyst™
and Graph pad. Based on the above strategies, the humoral responses (antibody
responses) during S. aureus infection was investigated.
Study-1 – Humoral responses during S. aureus nasal colonisation
IgG responses during S. aureus nasal colonisation
A total of 32 plasma samples was obtained from healthy individuals from western
Pomerania with defined clinical characteristics (Table 7). Among the 32 healthy
individuals, 16 were identified as carriers (twice positive) and 16 were identified as non-
carriers (without S. aureus) based on a earlier study [211].
The aim of this study was exploring the knowledge of humoral responses (antibody
responses) and correlation of clonal lineages during S. aureus nasal colonisation in well-
defined persistent carriers and non-carriers against 12 cytoplasmic antigens, 12
membrane antigens, 19 cell wall antigens, 66 extracellular antigens, 16 conserved
antigens, 8 auto inducing peptides respectively (list of antigens in Supplement 1). These
localisations were predicted based on psortb [212].
The IgG responses of carriers and non-carriers of S. aureus for 133 antigens are depicted
in Figure 24 thru a box-whisker plot. This plot represents the distribution of IgG levels in
carriers and non-carriers. The line in each box plot represents the median of the
individual. Each dot belongs to one antigen. The IgG against S. aureus antigens was
heterogeneous and their levels largely varied from individual to individual. The bar plots
61
were plotted with log10 transformed and non-normalised values of calculated intensity
(Figure 24).
Non-carrierCarrier
Lo
g1
0 c
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in
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sity
Figure 24. IgG responses of healthy individuals to S. aureus antigens, carriers (n=16), non-carriers
(n=16). Red line in the box whiskers plot represents the median (4.02) of all carriers healthy
individuals and blue line represents the median (3.88) of all non-carriers healthy individuals.
To unravel the complexity of data sets, we carried out a Mann-Whitney U test
(nonparametric method) to identify significantly antigen-specific IgG levels
discriminating between carriers and non-carriers (Figure 25). To identify the significant
antigens, fold change (log2) and p-values (-log10) were calculated and they were plotted
against log2 (fold change) and –log10 (p-value) thru volcano plot. Antigens depicted in red
colour elicited a higher IgG response in carriers than in non-carriers and antigens
depicted in blue colour elicited a higher IgG response in non-carriers than in carriers. The
horizontal dotted lines in the volcano plot represent the p-value threshold of 0.05. For 16
S. aureus antigens, a significantly different immune response (p < 0.05) was detected
between carriers and non-carriers: Geh (triacylglycerol lipase), esterase protein, Hld
(delta-hemolysin), IsdB (iron-regulated surface determinant B), LytM (peptidoglycan
hydrolase), SACOL0480, IsdA (iron-regulated surface determinant A), HlgA (gamma-
hemolysin A), HlgB (gamma-hemolysin B), HlgC (gamma-hemolysin B), SigB (RNA
62
polymerase sigma factor), SspA (glutamyl endopeptidase), Ssp (extracellular matrix
binding protein), auto inducing peptide thiolacton form 4, ClfB (clumping factor B) and
Ssl12 (staphylococcal superantigen like protein 12). Antigen-specific responses to foldase
protein (PrsA) and coagulase (Coa) were observed with high fold changes but without
passing the significance threshold of 0.05.
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
-3 -2 -1 0 1 2 3
P. V
alu
e (-
log10)
Fold change (log2)
Geh
P = 0.05
Esterase like protein
HldIsdB
SACOL0480 LytM
IsdA
HlgAAIP Thiol acton4 HlgB
HlgCSigB
SspASspClfB
Ssl12
Higher in carrierHigher in non-carrier
Figure 25. A Wilcoxon test was performed to evaluate the reactivity of antibodies against antigens in
carriers and non-carriers.
63
Discrimination efficiency of antigens provoking different immune responses in
carriers and non-carriers
To explore the efficiency of discrimination, a principal component analysis (PCA) was
performed. At first, all 133 antigens were used for the PCA (Figure 26). Later, the 16
antigens provoking significantly different immune responses in carriers and non-carriers
resulting from the volcano plot have been used for PCA analysis and the discrimination
efficiency between carriers and non-carriers was shown in Figure 27.
Non-carrier
Carrier
Figure 26. Principal component analysis of antibody responses between carriers (n=16) and non-
carriers (n=16) for 133 antigens.
64
Non-carrier
Carrier
Figure 27. Principal component analysis of antibody responses provoking significantly different
immune responses between carriers (n=16) and noncarriers (n=16) for 16 antigens.
Characteristics of immunogenic antigens
The immunoproteome was investigated based on the subcellular localisation of antigens
during S. aureus nasal colonisation. It is a key property of S. aureus that its proteins vary
in their accessibility by the immune system and that subsequently the antibody response
and function are affected. Antigens eliciting distinct IgG responses were represented by
dot plots. The levels of IgG specific to these antigens were significantly different in
carriers and non-carriers based on volcano plot (Figure 25). The dot plots represent the
calculated intensities (without log-transformation and normalisation) and they have been
sorted based on their intensity range in comparison between the carriers and non-carriers.
The more frequently reacting antigens that were associated with carriers were
predominantly extracellular and cell wall components of S. aureus (Figure 28), and the
reactivity that was associated with non-carriers were directed against clumping factor B
(ClfB) and staphylococcal super-antigen like protein Ssl12 (Figure 29). In carriers, LytM
with log2 fold change (FC) of 1.60, SACOL0480 with FC 1.40, esterase like protein with
FC 1.03, IsdA with FC 1.50, SspA with FC 1.33, AIP-type 4-thiolacton with FC 0.54,
triacylglycerol lipase (Geh) with FC 1.63, HlgA with FC 0.68, HlgB with FC 1.02, IsdB
65
with FC 0.90, Hld with FC 1.52, Ssp (extracellular matrix binding protein/plasma binding
protein) with FC 0.66, HlgC with FC 1.01 and SigB with FC 1.12 were shown to result in
significantly higher IgG responses (Figure 28). On the other hand, significantly higher
IgG responses were found in non-carriers against clumping factor B with FC -0.44 and
staphylococcal super-antigen like protein Ssl12 with FC -0.81 (Figure 30).
Other antigens such as truncated MHC class II analog protein (Map-W) with FC -1.31,
lipase with FC -1.02, peptide methionine sulfoxide reductase B (MsrB) with FC -0.56
were shown higher IgG responses in non-carriers than in carriers but did not pass the
significance threshold of 0.05.
66
SACOL0480
Carrier Noncarrier
100
1000
10000
100000 **
Peptidoglycan hydrolase LytM
Carrier Noncarrier
100
1000
10000
100000
1000000 **
IsdA
Carrier Noncarrier
1000
10000
100000
1000000 *
Glutamyl endopeptidase
Carrier Noncarrier
1000
10000
100000
1000000 *
LytM SACOL0480
Glutamyl endopeptidase (SspA)IsdA
Esterase like protein
Carrier Noncarrier
100
1000
10000 **
AIP - Typ4 - Thiolacton
Carrier Noncarrier
1000
10000
100000 *
Esterase like protein
AIP – Type4 – Thiolacton
Ca
lcu
late
d i
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tyC
alc
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in
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sity
F.C 1.60 F.C 1.40 F.C 1.03
F.C 0.54F.C 1.33F.C 1.50
Triaclyglycerol lipase (Geh)
Carrier Noncarrier
1000
10000
100000
1000000 **
HlgA
Carrier Noncarrier
1000
10000
100000
1000000 *
HlgB
Carrier Noncarrier
1000
10000
100000
1000000 *
Geh HlgA HlgB
IsdB
Carrier Noncarrier
104
105
106
107 **
Delta-hemolysin
Carrier Noncarrier
104
105
106
107 **
Ssp (Extracellular matrix binding protein)
Carrier Noncarrier
104
105
106
107 *
IsdB Hld Ssp (ECM)
Calc
ula
ted
in
ten
sity
Calc
ula
ted
in
ten
sity
F.C 1.63 F.C 0.68 F.C 1.02
F.C 0.66F.C 1.52F.C 0.90
HlgC
Carrier Noncarrier
10000
100000
1000000 *
SigB
Carrier Noncarrier
10
100
1000
10000 *
HlgC SigB
Calc
ula
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in
ten
sity
F.C 1.01 F.C 1.12
67
Figure 28. Reactivity of immunogenic antigens associated with carriers. A Mann-Whitney
(nonparametric), two sided test was performed for these data sets. Red coloured are data of carriers
and green coloured are data of non-carriers. Lines were drawn at the median. Log2 median fold
changes were calculated and represented as FC. Level of significance is represented by asterisks (* -
p-value less than 0.05; ** - p-value less than 0.01; *** - p-value less than 0.001; **** - p-value less
than 0.0001).
Antigens such as foldase protein (PrsA) and coagulase were statistically insignificant, but
they have shown higher log2 fold change. PrsA is membrane protein and it plays a vital
role in virulence of S. aureus. The PrsA-specific IgG levels were higher in carriers with
FC 2.65. Coagulase is an extracellular protein and an important virulence factor in S.
aureus infections. The coagulase-specific IgG were higher in non-carriers with FC -2.60.
Though antibody responses to these two antigens (PrsA and Coa) have shown higher fold
changes, they were statistically insignificant (Figure 30).
Clumping factor B
Carrier Noncarrier
1000
10000
100000 *
ClfB
Ca
lcu
late
d i
nte
nsi
ty
F.C -0.44
Superantigen-like protein_Ssl12
Carrier Noncarrier
10000
100000
1000000 *
Ssl12
F.C -0.81
Figure 29. Reactivity of immunogenic antigens associated with non-carriers. Level of significance is
represented by asterisks (* - p-value less than 0.05; ** - p-value less than 0.01; *** - p-value less than
0.001; **** - p-value less than 0.0001) and log2 median fold change were calculated and represented
as FC.
68
PrsA
Carrier Noncarrier
100
1000
10000
100000
1000000ns
Coagulase
Carrier Noncarrier
103
104
105
106
107
108 ns
PrsA
F.C 2.65
Coagulase
F.C -2.60
Ca
lcu
late
d i
nte
nsi
ty
Figure 30. Immunogenic insignificant antigens foldase protein (PrsA) associated with carriers and
coagulase (Coa) associated with non-carriers. Level of significance is represented by asterisks (* - p-
value less than 0.05; ** - p-value less than 0.01; *** - p-value less than 0.001; **** - p-value less than
0.0001) and log2 median fold change were calculated and represented as FC.
Clonal complex influence in anti-TSST-1 IgG responses
In this study, there are five different clonal complexes of S. aureus genotypes (CC5, CC8,
CC15, CC25, and CC30) present among the carriers. The strains of the 16 carriers
displayed the following distribution among clonal complexes: CC30 (50%), CC5
(12.5%), CC8 (12.5%), CC15 (12.5%), CC25 (12.5%). The IgG responses to S. aureus
antigens expressed in different clonal complexes were investigated during nasal
colonisation. Very interestingly, anti-TSST-1 IgG have shown differences in the antibody
responses upon classifying the clonal complexes. Figure 31 shows the IgG responses
against TSST-1 from the three groups such as carriers with CC30, carriers with non-
CC30 (other clonal complexes CC5, CC8, CC15, CC25) and non-carriers. Here, carriers
with CC30 clones have shown pronounced IgG responses against TSST-1. While
performing a Mann -Whitney U test, it shows significant difference between CC30 and
non-carriers with log2 FC of 1.41 and it shows insignificant difference between carriers
with non-CC30 (CC5, CC8, CC15, and CC25) and non-carriers with higher IgG response
against TSST-1 in non-carriers.
Besides CC30 of S. aureus, carriers with CC15 clone type have shown higher IgG
responses to antigens such as PrsA, IsdA, IsdH (heme binding proteins), SceD
(transglycosylase), SACOL0480 and pore forming toxins HlgA, HlgB, HlgC and Hld.
69
Though carriers with CC15 clone have shown higher IgG responses to these antigens, the
group size of CC15-clone carriers was restricted (12.5% - 2/16). Among 16 carriers, the
CC15 clone type was present in 2 healthy individuals and, therefore, statistical analysis
could not be performed for this group. As a simplistic visualisation of clonal complex
influence on the IgG responses against S. aureus antigens, a heat map of log2 transformed
average values of clonal complexes for 132 antigens was created and red square denotes
the higher IgG responses against the antigens in CC15 (Figure 32).
TSST-1
Ca
lcu
late
d i
nte
nsi
ty
F.C – 1.41
Tsst-1
Carrier CC30 Carrier Non CC30 Noncarrier
103
104
105
106
107*
ns
Figure 31. Clonal complex 30 influence on the Anti-TSST-1 IgG responses. Level of significance is
represented by asterisks (* - p-value less than 0.05; ** - p-value less than 0.01; *** - p-value less than
0.001; **** - p-value less than 0.0001) and log2 median fold change were calculated and represented
as FC.
70
Figure 32. Simplistic visualisation of
clonal complex influence on the IgG
responses against 132 antigens. log2
mean values of carriers group with
different clonal complexes are depicted
in the heat map and red squared regions
show higher IgG responses in CC15.
71
Summary of study-1
- In this study, we investigated the humoral responses (antibody responses) and
clonal complex correlation of S. aureus nasal colonisation using high-throughput
suspension array technology.
- The overall IgG responses against 133 antigens are highly heterogeneous between
the carriers and non-carriers.
- Overall in trend, carriers shows higher IgG responses than non-carriers.
- About 16 antigens were identified as candidates displaying different IgG
responses between the two groups based on the volcano plot and they further used
in the principal component analysis to test the discrimination efficiency.
- Among the 16 antigens, 14 antigens show significantly higher IgG responses in
carriers and 2 antigens revealed significantly higher IgG responses in non-
carriers.
- Most of the 16 candidate antigens belong to the extracellular and cell wall
component of S. aureus.
- Moreover, few antigens show higher FC but are not statistically significantly
different in level.
- In the correlation of clonal complex with IgG responses, mainly we observed
CC30 based higher IgG responses against TSST-1, whereas non-CC30 (CC5,
CC8, CC25, CC30) and non-carriers shows lower IgG responses against TSST-1.
- Besides CC30; CC15 associated S. aureus nasal colonisation shows pronounced
IgG responses against foldase protein (PrsA), delta-hemolysin (Hld), gamma-
hemolysin A, C (HlgA, HlgC), GroEL, IsdC, esterase like protein, and
staphylococcal secretory antigen (SsaA) than other clonal complexes (CC5, CC8,
CC25, CC30).
72
Study-2 – Humoral responses during onset of S. aureus bacteraemia infection
IgG responses during onset of S. aureus bacteraemia
The aim of this study was to explore the humoral responses during S. aureus bloodstream
infection using high-throughput suspension array technology. This study was conducted
as a 3 year population based prospective study between 2011 and 2014. About 303
patients were involved in this study at Akershus university hospital, Oslo, Norway. The
clinical characteristics of bacteraemia patients are listed in the Table 8. In this study, 4
dominant clonal lineages (CC5, CC15, CC30 and CC45) have been observed in
bacteraemia patients. Among the 303 patients, serum samples were obtained from 92
individuals comprising 43 controls (without S. aureus) and 49 sepsis samples (first
episode of S. aureus or one blood culture positive for S. aureus and without pre-antibiotic
therapy).
The IgG responses were studied against 12 cytoplasmic antigens, 12 membrane antigens,
19 cell wall antigens, 66 extracellular antigens, 16 conserved antigens, 8 auto inducing
peptides (list of antigens in Supplement 1) and their localisations were predicted based on
psortb [212].
The IgG distribution among the two groups of control and sepsis samples against 133 S.
aureus antigens are represented by a box whiskers plot in Figure 33. The calculated
intensities were transformed to log10 and non-normalised. The log10 transformed
calculated intensities were used for plotting and each dot belongs to the IgG response
against one antigen. The horizontal lines represent the median values of each individual.
Overall median values of each group were represented by green and red lines. The green
line represents the median of the control patients and the red line represents the median of
the sepsis patients. The log10 median value of the control patients is 3.93 and the log10
median value of the sepsis patients is 3.67.
73
Log10 C
alc
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in
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sity
Control Sepsis
Figure 33. Log10 transformed and non-normalised IgG responses of control and bacteraemia patients,
control (n=43), sepsis (n=49). The green line in the box whiskers plot represents the median (3.93) of
the control patients and the red line represents the median (3.67) of the sepsis patients.
To identify candidate antigens which discriminate between control and sepsis, a Mann-
Whitney U test/Wilcoxon test (non-parametric method) was performed. The log2 fold
change and –log10 p-values were calculated and the significant immune responses to
antigens were represented by the volcano plot in the Figure 34. The volcano plot
represents the significant reactivity of antibodies against antigens in control and sepsis
patients. About 72 antigens were statistically significant based on the Wilcoxon test.
Most of the antigens have shown higher antibody responses in the control. The Table 10
shows the list of 72 antigens against which significantly different immune responses were
observed between the control and the sepsis group. They have been sorted in ascending
order based on their level of significance.
74
-0.5
1.5
3.5
5.5
7.5
9.5
11.5
13.5
15.5
17.5
-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5
P. V
alu
e (-
log
10
)
Fold change (log2)
P = 0.05
Higher in ControlHigher in Sepsis
Figure 34. A Wilcoxon test was performed to evaluate the reactivity of antibodies against antigens in
control and sepsis patients. The dotted line represents the p-value threshold of 0.05. Antibodies
against antigens below the p-value 0.05 threshold were statistically significant. Lower fold changes
represent the higher antibody responses in sepsis and higher fold changes represents the higher
antibody responses in control.
Discrimination efficiency of antigens provoking different immune responses in
control and sepsis
In order to display the discrimination efficiency, a principal component analysis (PCA)
was performed. At first, all 133 antigens were used in the PCA (Figure 35) to test the
discrimination efficiency. Further, the 72 candidate antigens provoking significantly
different immune responses in control and sepsis resulting from volcano plot have been
used for PCA analysis and the discrimination efficiency was shown in the Figure 36.
75
Control
Sepsis
Figure 35. Principal component analysis (PCA) of antibody responses for control (n=43) and sepsis
(n=49) patients against 133 S. aureus antigens to visualize the discrimination efficiency.
Control
Sepsis
Figure 36 Principal component analysis (PCA) of antibody responses for control (n=43) and sepsis
(n=49) patients against 72 candidate S. aureus antigens to visualize the discrimination efficiency.
76
Table 10. List of antigens against which significantly different immune responses were observed
between control and sepsis patients. Level of significance was calculated by Wilcoxon non parametric
method and they have sorted in descending order based on the -log10 p-value.
Antigens Log2 Median FC - Log10 (p-value)
AIP - Typ1 3.2449 15.7084
AIP - Typ3 3.0820 15.7084
AIP - Typ2 3.3014 15.6518
AIP - Typ4 2.2328 14.9505
ESAT-6 family virulence protein 1.9402 10.1719
Staphopain B 1.5431 9.9689
3-hydroxyacyl-CoA dehydrogenase protein 1.7635 9.8570
AIP - Typ4 – Thiolacton 1.2560 9.8347
Penicillin-binding protein 2 3.1232 9.5033
Peptide methionine sulfoxide reductase A 2.1288 9.3723
Immunoglobulin-binding protein -0.9142 9.0896
Peptide methionine sulfoxide reductase B 1.9226 8.6481
Uncharacterised protein_SACOL1788 2.1032 7.1565
Trigger factor 1.7263 6.7833
Transcription elongation factor (GreA) 1.6822 5.9297
Enterotoxin O 1.6051 5.6919
Super antigen-like protein_Ssl11 2.1449 5.6751
Serine/threonine-protein kinase 1.8191 5.0876
Chaperonin GroEL 2.3555 5.0086
Superoxide dismutase 1.3648 4.8371
Hyaluronate lyase 2.0106 4.8371
Clumping factor B 1.2790 4.5631
Adenylosuccinate synthetase 1.1525 4.5481
Serine protease B 1.2908 4.4440
Immunodominant staphylococcal antigen B 1.8972 4.3950
AIP - Typ1 – Thiolacton 1.1256 4.3355
Truncated secreted von Willebrand factor-binding protein 1.9616 4.2683
Collagen adhesion 1.3154 4.1106
DNA segregation ATPase and related proteins 1.5016 3.8590
Elongation factor 0.9143 3.7498
Uncharacterised protein_SACOL0908 1.5543 3.6555
Isocitrate dehydrogenase 1.2604 3.5231
77
Enterotoxin type A 1.5605 3.2405
Fibrinogen-binding protein like protein -1.3434 3.1904
Uncharacterised protein_SACOL1802 0.9962 3.1532
Serine-aspartate repeat-containing protein D 0.6045 3.1408
Autolysin 0.9982 3.0670
Phosphopyruvate hydratase 1.6840 2.9701
Staphopain A/staphopain thiol proteinase 0.8806 2.9581
Super antigen-like protein_Ssl3 0.7279 2.7810
Enterotoxin C 0.8485 2.7462
Antitoxin MazE 0.7483 2.6774
Foldase protein A 1.3744 2.6660
Super antigen-like protein_Ssl2 0.6288 2.6320
AIP - Typ2 – Thiolacton 0.6467 2.5647
Serine-aspartate repeat protein F 0.7427 2.4218
Aldolase_hypothetical aldolase family protein 0.7074 2.2309
3-oxoacyl-[acyl-carrier-protein] reductase 0.7319 2.1080
Peptidoglycan hydrolase LytM 0.7248 2.0281
Zinc metalloproteinase aureolysin 1.6922 2.0182
Thermonuclease 1.4447 1.9497
Super antigen-like protein_Ssl1 1.0246 1.9303
1-phosphatidylinositol phosphodiesterase 1.0620 1.8728
MHC class II analog protein 0.8209 1.8162
Coagulase 0.9041 1.7604
Serine-aspartate repeat-containing protein C 0.7820 1.7604
Truncated MHC class II analog protein 1.3417 1.7512
Staphylococcal secretory antigen 0.6978 1.7420
RNA polymerase sigma factor SigB 0.9325 1.7055
Super antigen-like protein_Ssl10 0.3806 1.6248
Super antigen-like protein_Set15 1.3410 1.6072
Super antigen-like protein_Ssl5 0.6174 1.6072
Phospholipase C/truncated beta-hemolysin 0.6693 1.5984
Fibronectin binding protein -0.4094 1.5547
Esterase-like protein 0.5388 1.5202
Surface protein G 1.2161 1.4608
Fibronectin Binding protein B -0.4561 1.4524
Intracellular serine protease 1.0975 1.4271
78
Glycerophosphoryl diester phosphodiesterase 0.5743 1.4025
Extracellular enterotoxin L 1.1430 1.4025
Iron-regulated surface determinant protein H 0.8575 1.3293
Uncharacterised protein/fibrinogen-binding protein precursor -1.0462 1.3133
The differences in the immune responses against the listed antigens (Table 10) were
highly significant in this study. 72 antigens caused significantly different immune
responses in control and sepsis patients. Among the 72 significant antigens, 67 antigens
have shown higher antibody responses in control and 5 antigens have shown higher
antibody responses in sepsis patients. These 72 antigens belong to the different cellular
components of S. aureus; 11 belong to the cytoplasm, 6 belong to membrane, 10 belong
to cell wall, 38 belong to extracellular space fraction and 7 were of unknown localisation.
Mainly, extracellular components were significantly different in antibody response
comprising quorum sensing molecules such as auto inducing peptides including thiol-
Acton and linear form, super antigens (Ssl2, Ssl3, Ssl5, Ssl10, Ssl11), extracellular
enzymes (Coa, Nuc, Plc, GlpQ, SspB, SspA, Atl) and toxins. In the case of cell wall
components, MSCRAAM proteins were significant such as IsdH, Cna, SasG, SdrC,
SdrD, SdrF, ClfB, FnbpB, and FnbpA.
Characteristics of immunogenic antigens
The immunogenic antigens obtained from this study are listed in the Table 10. About 72
antigens were identified as candidate antigens in this study. The antibody responses
against the immunogenic antigens were depicted through dot plots; antibody responses
were plotted between patients and its calculated intensity; they were sorted based on its
cellular components (cytoplasmic, membrane, cell wall, extracellular and unknown) and
its characteristics.
Figure 37 represents the antibody responses against staphylococcal super antigens (Ssl1,
Ssl2, Ssl3, Ssl5, Ssl10, Ssl11, and Set15). They have been plotted with calculated
intensities in log10 scale; asterisks denote the level of significance, calculated using
Wilcoxon test. The antibody responses against super antigens were significantly higher in
control than in sepsis patients. The log2 median fold change (FC) values were calculated,
79
for instance 2.14 for Ssl11, 1.34 for Set 15, 1.02 for Ssl1, 0.72 for Ssl3, 0.62 for Ssl2,
0.61 for Ssl5, and 0.38 for Ssl10.
Superantigen-like protein_Ssl1
Control Sepsis
103
104
105
106
107 *
Ssl1
Superantigen-like protein_Ssl2
Control Sepsis
1000
10000
100000
1000000 **
Ssl2
Calc
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sity
Super antigen-like protein_Ssl3
Control Sepsis
1000
10000
100000
1000000 **
Ssl3
Superantigen-like protein_Ssl5
Control Sepsis
1000
10000
100000
1000000 *
Calc
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sity
Ssl5
Superantigen-like protein_Ssl10
Control Sepsis
1000
10000
100000
1000000 *Ssl10
Superantigen-like protein_Ssl11
Control Sepsis
10
100
1000
10000
100000
1000000 ****Ssl11
Super antigen-like protein_Set15
Control Sepsis
10
100
1000
10000
100000
1000000 *
Calc
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Set15
F.C 1.02 F.C 0.62 F.C 0.72
F.C 0.61 F.C 0.38 F.C 2.14
F.C 1.34
Figure 37. Antibody responses against S. aureus super antigens comparing control and sepsis
patients. Level of significance is represented by asterisks (* - p-value less than 0.05; ** - p-value less
than 0.01; *** - p-value less than 0.001; **** - p-value less than 0.0001) and log2 median fold change
were calculated and represented as FC.
Figure 38 represents the antibody responses against staphylococcal enterotoxins (Sec,
Seo, Sea, and Sel). They have been plotted with calculated intensities in log10 scale;
asterisks denote the level of significance, calculated using Wilcoxon test. The antibody
responses against enterotoxins were significantly higher in control than in sepsis patients.
The log2 median fold change values were calculated; for instance 1.60 for enterotoxin O
(Seo), 1.56 for enterotoxin A (Sea), 1.14 for enterotoxin L (Sel), and 0.84 for enterotoxin
C (Sec).
80
Enterotoxin C
Control Sepsis
10
100
1000
10000 **
Enterotoxin O
Control Sepsis
10
100
1000
10000 ****
Enterotoxin type A
Control Sepsis
100
1000
10000
100000 ***
Extracellular enterotoxin L
Control Sepsis
10
100
1000
10000
100000
1000000 *
Enterotoxin C
Ca
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d i
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Enterotoxin O
Ca
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late
d i
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nsi
ty
Enterotoxin A Enterotoxin L
F.C 0.84 F.C 1.60
F.C 1.56 F.C 1.14
Figure 38. Antibody responses against S. aureus enterotoxins comparing control and sepsis patients.
Level of significance is represented by asterisks (* - p-value less than 0.05; ** - p-value less than 0.01;
*** - p-value less than 0.001; **** - p-value less than 0.0001) and log2 median fold change were
calculated and represented as FC.
Figure 39 represents the antibody responses against extracellular antigens such as Plc,
Atl, GlpQ, Antitoxin MazE, Coa, Esat-6, HysA, IsaB, LytM, SspA, SspB, Hlb, SplB,
EpiP, SsaA, Sod, GraB – Truncated Willebrand binding protein, Aur, NucI. Dot plots
were plotted with calculated intensities in log10 scale; asterisks denote the level of
significance calculated using Wilcoxon test. The antibody responses against the
extracellular antigens were significantly higher in control than in sepsis patients. The log2
median fold change values were calculated, for instance 1.06 for Plc, 0.99 for Atl, 0.57
for GlpQ, 0.74 for Antitoxin MazE, 0.90 for Coa, 1.94 for Esat-6, 2.01 for HysA, 1.89 for
IsaB, 0.72 for LytM, 0.88 for SspA, 1.54 for SspB, 0.66 for Hlb, 1.29 for SplB, 1.09 for
EpiP, 0.69 for SsaA, 1.36 for Sod, 1.96 for truncated Willebrand binding protein (GraB),
1.69 for Aur, 1.44 for NucI.
81
Glycerophosphoryl diester phosphodiesterase
Control Sepsis
1000
10000
100000
1000000 *
1-phosphatidylinositol phosphodiesterase
Control Sepsis
100
1000
10000
100000 *
Antitoxin MazE
Control Sepsis
10
100
1000
10000 **
Coagulase
Control Sepsis
1000
10000
100000
1000000 *
Autolysin
Control Sepsis
10
100
1000
10000
100000 ***
ESAT-6 family virulence protein
Control Sepsis
100
1000
10000
100000
1000000 ****
Calc
ula
ted
in
ten
sity
Plc
F.C 1.06
Autolysin
F.C 0.99
Calc
ula
ted
in
ten
sity
GlpQ
F.C 0.57
Antitoxin MazE
F.C 0.74
Coagulase
F.C 0.90
ESAT-6
F.C 1.94
Hyaluronate lyase
Control Sepsis
100
1000
10000
100000
1000000 ****
Immunodominant staphylococcal antigen B
Control Sepsis
100
1000
10000
100000
1000000 ****
Peptidoglycan hydrolase LytM
Control Sepsis
100
1000
10000
100000
1000000 **
Calc
ula
ted
in
ten
sity
HysA
F.C 2.01
IsaB
F.C 1.89
LytM
F.C 0.72
Intracellular serine protease
Control Sepsis
10
100
1000
10000
100000 *
Phospholipase C/truncated beta hemolysin
Control Sepsis
100
1000
10000
100000
1000000 *
Serine protease B
Control Sepsis
100
1000
10000
100000 ****
Staphopain B
Control Sepsis
100
1000
10000
100000 ****
Staphopain A/staphopain thiol proteinase
Control Sepsis
10
100
1000
10000
100000 **
Staphylococcal secretory antigen
Control Sepsis
100
1000
10000
100000 *
Superoxide dismutase
Control Sepsis
10
100
1000
10000
100000 ****
Truncated secreted von Willebrand factor-binding protein (Coagulase) VWbp
Control Sepsis
100
1000
10000
100000 ****
Zinc metalloproteinase aureolysin
Control Sepsis
100
1000
10000
100000
1000000 **
Thermonuclease
Control Sepsis
10
100
1000
10000
100000
1000000 *
SspA
Calc
ula
ted
in
ten
sity
Calc
ula
ted
in
ten
sity
Calc
ula
ted
in
ten
sity
Calc
ula
ted
in
ten
sity
F.C 0.88
SspB
F.C 1.54
Hlb
F.C 0.66
SplB
F.C 1.29
EpiP (IC Spl)
F.C 1.09
SsaA
F.C 0.69
Sod
F.C 1.36
GraB (VWbp)
F.C 1.96
Aur
F.C 1.69
NucI
F.C 1.44
Figure 39. Antibody responses against S. aureus extracellular antigens between control and sepsis.
Level of significance was represented by asterisks (* - p-value less than 0.05; ** - p-value less than
0.01; *** - p-value less than 0.001; **** - p-value less than 0.0001) and log2 median fold change were
calculated and represented as FC.
82
Figure 40 represents the antibody responses against linear and thio-lactone forms of auto
inducing peptides (AIP) such as AIP – Type I – linear and thio-lactone, AIP – Type II –
linear and thio-lactone, AIP – Type IV – linear and thio-lactone, and AIP – Type III –
linear form. Dot plots were plotted with calculated intensities in log10 scale; asterisks
denote the level of significance calculated using Wilcoxon test. The antibody responses
against auto inducing peptides were significantly higher in control than in sepsis patients.
Majorly linear forms of AIP have shown higher antibody responses in control patients.
The log2 median fold change were calculated, for instance 3.24 for linear AIP – type I,
1.12 for thio-lactone form of AIP – type I, 3.30 for linear AIP – type II, 0.64 for thio-
lactone form of AIP – type II, 2.23 for linear AIP – type IV, 1.25 for thio-lactone form of
AIP – type IV, and 3.08 for linear AIP – type III. Significantly higher antibody responses
were found to linear AIPs than to thio-lactone form were observed.
83
AIP - Typ1 - Thiolacton
Control Sepsis
100
1000
10000
100000 ****
AIP - Typ1
Control Sepsis
100
1000
10000
100000 ****
AIP - Typ2
Control Sepsis
10
100
1000
10000 ****
AIP - Typ2 - Thiolacton
Control Sepsis
1000
10000
100000 **
AIP - Typ3
Control Sepsis
100
1000
10000
100000 ****
AIP - Typ4
Control Sepsis
100
1000
10000
100000 ****
AIP - Typ4 - Thiolacton
Control Sepsis
1000
10000
100000 ****
Calc
ula
ted
in
ten
sity
Calc
ula
ted
in
ten
sity
Calc
ula
ted
in
ten
sity
Calc
ula
ted
in
ten
sity
AIP – Type 1 (Linear)
F.C 3.24
AIP – Type 1 (Thio-lactone)
F.C 1.12
AIP – Type 2 (Linear)
F.C 3.30
AIP – Type 2 (Thio-lactone)
F.C 0.64
AIP – Type 4 (Linear)
F.C 2.23
AIP – Type 4 (Thio-lactone)
F.C 1.25
AIP – Type 3 (Linear)
F.C 3.08
Figure 40. Antibody responses against S. aureus linear and thio-lactone forms of auto inducing
peptides between control and sepsis. Level of significance was represented by asterisks (* - p-value
less than 0.05; ** - p-value less than 0.01; *** - p-value less than 0.001; **** - p-value less than
0.0001) and log2 median fold change were calculated and represented as FC.
Figure 41 represents the antibody responses against cell wall antigens such as clumping
factor B (ClfB), collagen adhesin (Cna), iron surface determinant protein H (IsdH),
serine-aspartate repeat containing protein C, D, F (SdrC, SdrD, SdrF), surface protein G
(SasG), and SACOL1788. Dot plots were plotted with calculated intensities in log10
scale; asterisks denotes the level of significance calculated using Wilcoxon test. The
antibody responses against the cell wall antigens were significantly higher in control than
in sepsis patients. The log2 median fold change values were calculated, for instance 1.27
84
for ClfB, 1.31 for Cna, 0.85 for IsdH, 0.78 for SdrC and SdrD, 0.74 for SdrF, 1.21 for
SasG, and 2.10 for SACOL1788.
Clumping factor B
Control Sepsis
1000
10000
100000
1000000 ****
Collagen adhesin
Control Sepsis
1000
10000
100000
1000000 ****
Iron-regulated surface determinant protein H
Control Sepsis
103
104
105
106
107 *
SdrC
Control Sepsis
100
1000
10000
100000 *
SdrD
Control Sepsis
1000
10000
100000
1000000 ***
SdrF
Control Sepsis
1000
10000
100000
1000000 **
Surface protein G
Control Sepsis
102
103
104
105
106
107 *
Uncharacterized protein_SACOL1788
Control Sepsis
100
1000
10000
100000
1000000 ****
Calc
ula
ted
in
ten
sity
ClfB
F.C 1.27
Cna
F.C 1.31
IsdH
F.C 0.85C
alc
ula
ted
in
ten
sity
SdrC
F.C 0.78
SdrD
F.C 0.78
SdrF
F.C 0.74
Ca
lcu
late
d i
nte
nsi
ty
SasG
F.C 1.21
SACOL1788
F.C 2.10
Figure 41. Antibody responses against S. aureus cell wall antigens between control and sepsis. Level
of significance was represented by asterisks (* - p-value less than 0.05; ** - p-value less than 0.01; ***
- p-value less than 0.001; **** - p-value less than 0.0001) and log2 median fold change were calculated
and represented as FC.
Figure 42 represents the antibody responses against S. aureus cytoplasmic antigens such
as adenylosuccinate synthetase (PurA), hypothetical aldolase family protein (SbnG),
GroEL protein, elongation factor (Tuf), esterase like protein, isocitrate dehydrogenase
(CitC), peptide methionine sulfoxide reductase B (MsrB), Phosphopyruvate hydratase
(Eno), RNA polymerase sigma factor (SigB), transcription elongation factor (GreA),
trigger factor (Tig). Dot plots were plotted with calculated intensities in log10 scale;
asterisks denotes the level of significance calculated using Wilcoxon test. The antibody
responses against the cytoplasmic antigen were significantly higher in control than in
sepsis patients. The log2 median fold change values were calculated, for instance 1.15 for
85
PurA, 0.70 for SbnG, 2.35 for GroEL, 0.91 for Tuf, 0.53 for esterase like protein, 1.26 for
CitC, 1.92 for MsrB, 1.68 for Eno, 0.93 for SigB, 1.68 for GreA, and 1.72 for Tig.
Adenylosuccinate synthetase
Control Sepsis
10
100
1000
10000 ****
Aldolase_hypothetical aldolase family protein
Control Sepsis
10
100
1000
10000 **
Chaperonin GroEL
Control Sepsis
10
100
1000
10000
100000 ****
Elongation factor Tu
Control Sepsis
100
1000
10000
100000 ***
Esterase-like protein
Control Sepsis
100
1000
10000
100000 *
Isocitrate dehydrogenase
Control Sepsis
10
100
1000
10000
100000 ***
Peptide methionine sulfoxide reductase B
Control Sepsis
10
100
1000
10000
100000 ****
Phosphopyruvate hydratase/Enolase
Control Sepsis
10
100
1000
10000
100000 **
RNA polymerase sigma factor sigB
Control Sepsis
10
100
1000
10000 *
Transcription elongation factor GreA
Control Sepsis
10
100
1000
10000
100000 ****
Trigger factor
Control Sepsis
10
100
1000
10000
100000 ****
Calc
ula
ted
in
ten
sity
Calc
ula
ted
in
ten
sity
Calc
ula
ted
in
ten
sity
Calc
ula
ted
in
ten
sity
PurA
F.C 1.15
SbnG
F.C 0.70 F.C 2.35
GroEL
Tuf
F.C 0.91
Esterase like protein
F.C 0.53
CitC
F.C 1.26
MsrB
F.C 1.92
Eno
F.C 1.68
SigB
F.C 0.93
GreA
F.C 1.68
Tig
F.C 1.72
Figure 42. Antibody responses against S. aureus cytoplasmic antigens between control and sepsis.
Level of significance was represented by asterisks (* - p-value less than 0.05; ** - p-value less than
0.01; *** - p-value less than 0.001; **** - p-value less than 0.0001) and log2 median fold change were
calculated and represented as FC.
Figure 43 represents the antibody responses against S. aureus membrane antigens such as
DNA segregation related proteins (EssC), foldase protein (PrsA), MHC class II analog
protein (Truncated Map-W), penicillin binding protein 2 (Pbp2), Serine/threonine-protein
kinase (PrkC), and truncated MHC class II analog protein (Map-W). Dot plots were
86
plotted with calculated intensities in log10 scale; asterisks denote the level of significance
calculated using Wilcoxon test. The antibody responses against the membrane antigen
were significantly higher in control than in sepsis patients. The log2 median fold change
were calculated, for instance 1.50 for EssC, 1.37 for PrsA, 0.82 for Truncated Map-W,
3.12 for Pbp2, 1.81 for PrkC, 1.34 for Map-W.
DNA segregation ATPase and related proteins
Control Sepsis
100
1000
10000
100000 ***
Foldase protein A
Control Sepsis
10
100
1000
10000
100000
1000000 **
MHC class II analog protein
Control Sepsis
104
105
106
107
108 *
Penicillin-binding protein 2
Control Sepsis
100
1000
10000
100000
1000000 ****
Serine/threonine-protein kinase
Control Sepsis
100
1000
10000
100000
1000000 ****
Truncated MHC class II analog protein
Control Sepsis
100
1000
10000
100000
1000000 *
Calc
ula
ted
in
ten
sity
EssC
F.C 1.50
PrsA
F.C 1.37
Truncated Map-W
F.C 0.82
Calc
ula
ted
in
ten
sity
Pbp2
F.C 3.12
PrkC
F.C 1.81
Map-W
F.C 1.34
Figure 43. Antibody responses for S. aureus membrane antigens between control and sepsis. Level of
significance was represented by asterisks (* - p-value less than 0.05; ** - p-value less than 0.01; *** -
p-value less than 0.001; **** - p-value less than 0.0001) and log2 median fold change were calculated
and represented as FC.
Figure 44 represents the antibody responses against unknown localisation S. aureus
antigens such as 3-hydroxyacyl-CoA dehydrogenase protein (FadB), 3-oxoacyl-[acyl-
carrier-protein] reductase (FabG), peptide methionine sulfoxide reductase A (MsrA),
SACOL0908, and SACOL1802. Dot plots were plotted with calculated intensities in
log10 scale; asterisks denote the level of significance calculated using Wilcoxon test. The
antibody responses against this set of unknown antigens were significantly higher in
control than in sepsis patients. The log2 median fold change values were calculated, for
instance 1.76 for FadB, 0.73 for FabG, 2.12 for MsrA, 1.55 for SACOL0908, 0.99 for
SACOL1802.
87
3-hydroxyacyl-CoA dehydrogenase protein
Control Sepsis
100
1000
10000
100000 ****
3-oxoacyl-[acyl-carrier-protein] reductase
Control Sepsis
100
1000
10000
100000 **
Peptide methionine sulfoxide reductase A
Control Sepsis
100
1000
10000
100000
1000000 ****
Uncharacterized protein_SACOL0908
Control Sepsis
10
100
1000
10000
100000 ***
Uncharacterized protein_SACOL1802
Control Sepsis
1
10
100
1000
10000 ***
FadB
Calc
ula
ted
in
ten
sity
F.C 1.76
FabG
F.C 0.73
MsrA
F.C 2.12
Calc
ula
ted
in
ten
sity
SACOL0908
F.C 1.55
SACOL1802
F.C 0.99
Figure 44. Antibody responses for S. aureus unknown antigens between control and sepsis. Level of
significance was represented by asterisks (* - p-value less than 0.05; ** - p-value less than 0.01; *** -
p-value less than 0.001; **** - p-value less than 0.0001) and log2 median fold change were calculated
and represented as FC.
Furthermore, we observed some higher antibody responses in the sepsis patients in
comparison to the control patients. Among the 72 significant antigens, 67 antigen-
specific IgG responses were higher in control patients and 5 antigen-specific IgG
responses were higher in sepsis patients. Figure 45 represents the higher antibody
responses in sepsis patients against fibronectin binding protein A and B (FnbA and
FnbB), immunoglobulin binding protein (Sbi), fibrinogen-binding protein like protein
(Efb-1), and uncharacterised protein or fibrinogen-binding protein precursor (Scc). Dot
plots were plotted with calculated intensities in log10 scale; significant test performed
using Wilcoxon test and the asterisks denotes the level of significance. The log2 median
fold change values were calculated, for instance -0.40 for FnbA, -0.45 for FnbB, -0.91 for
Sbi, -1.34 for Efb-1, and -1.04 for Scc.
88
Fibronectin Binding protein B
Control Sepsis
103
104
105
106
107 *
Fibronectin binding proteinA
Control Sepsis
103
104
105
106
107 *
Fibrinogen-binding protein like protein
Control Sepsis
100
1000
10000
100000
1000000 ***
Uncharacterized protein/fibrinogen-binding protein precursor
Control Sepsis
103
104
105
106
107 *
Immunoglobulin-binding protein
Control Sepsis
107
108
109
101 0 ****
Calc
ula
ted
in
ten
sity
FnbA
F.C -0.40
FnbB
F.C -0.45
Sbi
F.C -0.91
Calc
ula
ted
in
ten
sity
Efb-1
F.C -1.34
Scc
F.C -1.04
Figure 45. Higher antibody responses for S. aureus antigens in sepsis. Level of significance was
represented by asterisks (* - p-value less than 0.05; ** - p-value less than 0.01; *** - p-value less than
0.001; **** - p-value less than 0.0001) and log2 median fold change were calculated and represented
as FC.
Summary of study-2
- In this study, we investigated the humoral responses during S. aureus bloodstream
infection using high-throughput suspension array technology.
- In terms of overall IgG responses against 133 antigens, control samples generally
display higher IgG responses than those from sepsis patients.
- About 72 antigens were identified as candidates based on the volcano plot and the
discrimination efficiency was tested using principal component analysis.
- Of all the 72 candidates causing significantly different immune responses 50%
belongs to the extracellular components of S. aureus.
- Among the 72 antigens, 67 antigens have shown significantly higher IgG
responses in controls and 5 antigens have shown significantly higher IgG
responses in the sepsis patients.
- Fibrinogen and fibronectin family proteins of S. aureus have shown higher IgG
responses in sepsis patients compared to controls.
89
Study-3 – Specific serum IgG at diagnosis of S. aureus bloodstream invasion is
correlated with disease progression
IgG responses during S. aureus bacteraemia
The aim of this study was to elucidate the protection efficiency of the adaptive immune
system during S. aureus bloodstream infections (SABSI). A prospective study of 44
patients with S. aureus bloodstream infections was performed. The clinical characteristics
of 44 patients are listed in the Table 9. About 64 recombinant S. aureus antigens were
incorporated into the study of antibody specificities associated with the protection against
sepsis. These 64 S. aureus antigens are listed in the Supplementary Table 2. Antigens
encompassing localisation in extracellular space, cell wall, cytoplasm and with unknown
cellular localisation were used in this study (list of antigens in Supplementary 2).
The IgG responses among these SABSI were depicted in box plots using non-normalised,
log10-transformed intensities (Figure 46). Dots around the box plots represents the single
antigens and their corresponding IgG responses. Thick coloured horizontal line represents
the median of all patients, red horizontal lines belongs to sepsis patients and green
horizontal line belongs to no-sepsis patients. The log10 median values of no-sepsis
patients were 4.21 and the log10 median values of sepsis patients were 3.95. IgG
responses among the groups were heterogeneous and inter-individual responses varied
within the group.
Mainly, patients were grouped into no-sepsis, uncomplicated sepsis, and severe sepsis or
septic shock and results were subjected to a partial least square analysis (PLS) which is
depicted in Figure 47A.
Later, the uncomplicated sepsis, severe sepsis or septic shock groups were grouped
together and the PLS was performed again based on the two groups of bacteraemia
patients developing or not developing sepsis. This resulted in a separation between most
bacteraemia patients without sepsis and with sepsis although some patient data sets were
still visible among the data points of the opposite group (Figure 47B).
90
No sepsis Sepsis
Log10 C
alc
ula
ted
in
ten
sity
Figure 46. IgG responses of bacteraemia patients without sepsis (n=21) and with sepsis (n=19) against
64 S. aureus antigens. The red line in the box whiskers plot represents the median of all sepsis
patients (3.95) and green line represents the median of all without sepsis (4.21).
Figure 47. Bacteraemia without sepsis, with uncomplicated sepsis and with severe sepsis/septic shock
grouped together in a partial least square analysis (A). Decent discrimination between sepsis
(uncomplicated sepsis, severe sepsis and septic shock) and no-sepsis were shown in PLS (B).
91
Validation of antigens provoking significantly different immune responses in
patients with sepsis and without sepsis
In order to validate the results, support vector machine was performed. The support
vector machine revealed the best discrimination between patients with and without sepsis
applying Fischer linear discriminant analysis to the calculated intensities of IgG binding
to the top 8 most discriminating antigens listed in the Table 11. These 8 antigens are
phospholipase (Plc), staphopain B (SspB), immunodominant staphylococcal protein A
(IsaA), staphylococcal exotoxin M (SEM), glycerophosphoryl diester phosphodiesterase
(GlpQ), gamma- hemolysin component C (HlgC) and two proteins of unknown function
SACOL0444 and SACOL0985.
Further, to identify antigen candidates, we performed a Wilcoxon test (nonparametric
method). The log2 fold change and –log10 p-value were calculated and they have been
displayed using a volcano plot (Figure 48). About 20 S. aureus antigens have shown
significantly higher IgG responses in no-sepsis patients and they are listed in the Table
11. Most of these antigens belonged to the extracellular components of S. aureus. They
comprise known toxins, other virulence factors and also antigens of unknown functions.
The above results were also tested using principal component analysis (PCA) with serum
IgG binding to the top eight most discriminating antigens. Figure 49 shows the degree of
separation between patients with and without subsequent sepsis.
By using Fischer linear discriminant analysis, sepsis was correctly predicted in 16 of 21
patients and 17 of 23 patients without sepsis were correctly assigned to the non-
complicated group. Thus, the prediction was correct in 75% of the patients (sensitivity
76%; specificity 74%).
92
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
4
-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5
P. V
alu
e (-
log
10
)
Fold change (log2)
P = 0.05
Higher in nosepsis
Plc
EapGlpQ
SplB
SspBSACOL0444
IsaASemAIP type 4 Thiolacton
CoaSakHlgC
HlgBSplC LukF - PVEfb
SACOL0480 Atl – GL domain
Figure 48. Wilcoxon test was performed to evaluate the reactivity of antibodies against antigens in
bacteraemia patients with and without sepsis. The dotted line represents the p-value threshold of
0.05. Red labelled antigens were significantly (p-value < 0.05) higher in patients without sepsis than
in patients with sepsis.
Figure 49. Patient stratification using principal component analysis (PCA) with eight most
discriminating S. aureus antigens.
93
Table 11: Top 20 S. aureus antigens predicting the patient stratification (Table
adapted from Stentzel et al. 2015 [207])
Abbrevi
ation Description Gene locus
GI-
Number Protein localisationa
Psortb LocateP
Signal
P
TM
Domain
Plc
1-phosphatidyl-
inositol phosphor-
diesterase
SAUSA300
_0099 87128097 Extracellular
N-terminally
anchored yes 1
SspB Staphopain B SA0900 15926634 Extracellular Secretory yes 1
IsaA Probable
transglycosylase
SAOUHSC
_02887 88196515 Extracellular
N-terminally
anchored yes 0
Uncharacterised
protein
SACOL044
4 57652631
Cytoplasmic
Membrane
Lipid
anchored yes 0
Sem Staphylococcal
enterotoxin M SA1647 15927403 Extracellular
N-terminally
anchored yes 0
Surface protein,
putative
SACOL098
5 57650173
Cytoplasmic
Membrane
N-terminally
anchored yes 0
GlpQ
Glycerophos-
phoryl-diester
phosphor-
diesterase
SAUSA300
_0862 87126873 Extracellular
N-terminally
anchored yes 0
HlgC Gamma-hemolysin
component C
SACOL242
1 57650965 Extracellular Secretory yes 0
SplB Serine protease
SplB
SAOUHSC
_01941 88195635 Extracellular
N-terminally
anchored yes 1
Sak Staphylokinase,
putative
SAOUHSC
_02171 88195848 Extracellular Secretory yes 0
Atl Bifunctional
autolysin
SAOUHSC
_00994b
88194750 Extracellular Secretory yes 0
Efb-c Fibrinogen-binding
protein
SAOUHSC
_01114 88194860 Extracellular
N-terminally
anchored yes 0
Uncharacterised
protein
SACOL048
0 57651321
Cytoplasmic
Membrane
N-terminally
anchored yes 1
GreA Transcription
elongation factor SA1438 15927190 Cytoplasmic Intracellular no 0
94
a Protein localisation was predicted with different algorithms using the Aureowiki
database and S. aureus COL as reference strain (http://www.protecs.uni-
greifswald.de/aureowiki/Main_Page; November 2014)
b N-terminal part of the protein
LukF.PV LukF-PV SAUSA300
_1381 87126598 - - - -
Tsst-1 Toxic shock
syndrome toxin-1 SA1819 15927587 Extracellular Secretory yes 1
HlgB Gamma-hemolysin
component B
SACOL242
2 57650966 Extracellular Secretory yes 0
Coa Coagulase SAOUHSC
_00192 88194002 Extracellular Secretory yes 0
Lip Triacylglycerol
lipase 1
SAUSA300
_2603 87126156 Extracellular
N-terminally
anchored yes 1
Uncharacterised
protein
SACOL090
8 57651598
Cytoplasmic
Membrane
N-terminally
anchored yes 0
95
Characteristics of immunogenic antigens
IgG binding to these eight antigens provoked a significantly different immune response in
the groups of bacteraemia patients with and without sepsis. The calculated intensities of
patient groups were depicted through dot plots in Figure 50. Dot plots were plotted with
calculated intensity in log10 scale and the line represents the median. Here, the level of
significance (p-value) were represented with asterisks and its log2 fold change for 8
antigens were shown in the Figure 50. Apparently, higher S. aureus-specific IgG serum
levels were found with patients who never experienced earlier episodes of sepsis. The
immunoproteome signature of eight S. aureus antigens had driven the disease course of
bacteraemia patients with a confidence level of 75% accuracy (76% sensitivity and 74%
specificity).
Plc
Res
po
nse
Nosepsis Sepsis
102
104
106
108 ***
SspB
Res
po
nse
Nosepsis Sepsis
102
104
106
108 **
IsaAR
esp
on
se
Nosepsis Sepsis
102
104
106
108 *
Sem
Res
po
nse
Nosepsis Sepsis
102
104
106
108 *
GlpQ
Res
po
nse
Nosepsis Sepsis
102
104
106
108 **
HlgC
Res
po
nse
Nosepsis Sepsis
102
104
106
108 *
SACOL0444
Res
po
nse
Nosepsis Sepsis
102
104
106
108 **
SACOL0985
Res
po
nse
Nosepsis Sepsis
102
104
106
108 **
Plc
Ca
lcu
late
d i
nte
nsi
ty
SspB IsaA Sem
Ca
lcu
late
d i
nte
nsi
ty
GlpQ HlgC SACOL0444 SACOL0985
F.C 2.34 F.C 1.22 F.C 2.05 F.C 2.18
F.C 1.08 F.C 1.59 F.C 2.10 F.C 1.73
Figure 50. Quantified median IgG binding differences to these eight antigens in comparison between
bacteraemia patients without and with sepsis. Open circles indicate patients without sepsis and black
filled circles indicate patients with sepsis. Lines are drawn at median. Level of significance is
represented by asterisks (* - p-value less than 0.05; ** - p-value less than 0.01; *** - p-value less than
0.001; **** - p-value less than 0.0001) and log2 median fold change were calculated and represented
as FC.
96
Summary of study-3
- In this study, we investigated the protection efficiency of the adaptive immune
system during SABSI using high-throughput suspension array technology.
- Overall IgG responses against 64 antigens were highly heterogeneous between
no-sepsis and sepsis patients at time of admission already.
- In general, the patients who will not develop sepsis have shown higher IgG
responses than patients later developing sepsis.
- The top 20 S. aureus proteins listed in the table 11 mainly contribute to the
discrimination of no-sepsis and sepsis. As expected, all of these proteins belonged
to the extracellular proteome of S. aureus, and they have shown pronounced IgG
binding.
- About 8 antigens were identified as candidates, and they were used for predicting
the progression of SABSI.
- S. aureus proteins are grouped according to their cellular components. Based on
the normalised median serum IgG binding intensity, extracellular and cell wall
antigens were recognised by the serum antibodies.
97
Discussion
S. aureus is an important life threatening pathogen, which can lead to soft skin tissue
infection (SSTi), wound infections, pneumonia, and blood stream infections (BSI) in the
community and hospital sites. The S. aureus infection rate due to community and hospital
acquired strains is increasing steadily. S. aureus produces a wide array of virulence
proteins and immune evasion factors that increase the incidence of S. aureus infections.
However, treatment strategies are really challenging due to the increase of multi-drug
resistance of clinical S. aureus isolates. The rapid emergence of S. aureus methicillin and
other antibiotic resistant strains further exaggerates the condition and thereby increases
the mortality rate. To date, the mortality rate due to S. aureus is higher than the mortality
rate due to HIV, viral hepatitis, tuberculosis or influenza [213] and there are no new
classes of antibiotics to treat S. aureus related infections over the past decades too.
However, vaccines would be appropriate preventive measures to treat the S. aureus
related infections. Until now, all S. aureus vaccine trials failed in different phases (see
Table 2 & 3). For an effective vaccine development, it is beneficial to understand the in
vivo behaviour of S. aureus and the host immune responses during disease progression.
The foremost challenging task during vaccine development is the identification of
antigens (single or multi-antigens) that will stimulate the most effective immune
responses against S. aureus. Few antigens have been listed in the literature based on
previous findings using classical methods and in the current thesis suspension array
technology (SAT) has been used to identify additional S. aureus antigens.
Immunoproteomics – Method establishment and optimisation
A serological multiplex assay has been developed for the indirect detection of IgGs in
human serum/plasma samples obtained from S. aureus colonised and infected
individuals. For the indirect detection of specific IgGs through multiplexing, recombinant
S. aureus proteins were required and the purity of these proteins is a very important factor
for this assay in order to avoid any interbacterial cross reactivity during the antibody-
antigen interaction.
Antibodies against E. coli in human serum/plasma samples as well as non-specific
binding and other matrix effects may lead to false positive or false negative results.
98
Therefore, the serological assay was optimised and validated during the method
development stage. In the first step of method development, the quality of the
recombinant S. aureus proteins was evaluated by 1-D SDS PAGE and LC-MS/MS.
During the purification of S. aureus proteins, some cytoplasmic E. coli proteins were co-
purified and from the 1-D SDS PAGE and in the LC-MS/MS experiments, those proteins
were identified. Nevertheless, the purity of the over-expressed S. aureus proteins was
above 90%. The co-purified E. coli proteins were mainly the peptidyl-prolyl cis-trans
isomerase and the elongation factor, but the level of these two proteins was only in the
range of 2 to 4 % (Figure 12 and 13). The remaining cross-reactivity of E. coli specific
antibodies in the serum/plasma was reduced by using a 10% of E. coli lysate as a pre-
adsorbent to the human serum/plasma samples. This pre-adsorbent decreased the cross
reactivity of E. coli specific antibodies to make sure that the responses are highly specific
to S. aureus antibodies. To obtain consistent, reliable and accurate data, preliminary
validation has been carried out during the method development stage. The basic
validation included determination of the range, linearity and selectivity/specificity. As a
result, pre-adsorption with E. coli lysate improved the specificity/selectivity of the assay.
Furthermore, the serum/plasma sample dilution range was optimised. Hence, 22 different
dilutions were tested for 121 antigens and the optimal range was determined from 40 fold
to 200000 fold dilution. In addition, an assay buffer supplemented with 45% low cross
buffer (LCB), 45% carboxy block store buffer (PBS + 1.0% BSA), 10% E. coli lysate
was applied [214–216]. For these experiments we have used magplex beads throughout
as it has already been shown better sensitivity, with better signal intensity than microplex
beads (non-magnetic beads) and low cross reactivity [217].
Another critical point in the experimental design was the stability of the magplex-antigen
complexes, because the MFI-data from individual experiments, measured at different
time points, were later combined. As a result of these test experiments we could prove
that antigens immobilised on the magplex beads were highly stable over a time period of
18 months. Moreover, streptavidin tag containing antigens in combination with magplex
were highly stable over a time period of 22 months and more stable than the histidine tag
containing antigens. Furthermore, individual characteristics of each antigen seem to play
99
a critical role. Thus, unstable conjugated antigen-magplex beads should be excluded from
the panel or must be re-coupled (fresh coupling) for an assay.
The serological assay is well suited for the antibody detection against a large set of
recombinant S. aureus antigens in human serum/plasma samples by indirect detection.
This method has the potential to detect 500 proteins/antigens per well in short time (60
s/well), with a small volume of sample (~4.0 µl) and a wide dynamic range (105).
Consequently, this suspension array technology represents a perfect tool for the rapid and
efficient antibody profiling in human body fluids such as serum, plasma and sputum, as
well as samples from throat swabs and nasal polyps.
Study-1: Immune responses during nasal colonisation
In this study-1 we have investigated the antibody responses in S. aureus nasally colonised
healthy individuals to classify immunogenic S. aureus antigens against IgG. Using the
suspension array technology, the antibody responses of persistently nasal colonised
(carriers) and intermittently nasal colonized (non-carriers), healthy individuals were
profiled for the IgG response to 133 S. aureus antigens. The anti-staphylococcal IgG
levels showed divergent responses between carriers and non-carriers, with strong inter-
individual variability within the groups. This potentially indicates a variable number of
previous encounters with S. aureus strains (Figure 24). The overall IgG response of
carriers and non-carriers is represented by median of all carriers and non-carriers
individuals and carriers show a higher median than the non-carriers. Significantly
stronger responses were found for carriers specific IgGs directed against LytM (P <
0.01), SACOL0480 (P < 0.01), esterase like protein (P < 0.01), Geh (P < 0.01), IsdB (P <
0.01), Hld (P < 0.01), IsdA (P < 0.05), SspA (P < 0.05), AIP-type 4 Thiolacton (P <
0.05), HlgA (P < 0.05), HlgB (P < 0.05), Ssp (P < 0.05), HlgC (P < 0.05), SigB (P <
0.05) (Figure 28). These findings are all in line with earlier studies from Kolata et al.,
Holtfreter et al. and Kloppot et al. [218–220].
However, the immune responses are highly strain dependent during colonisation;
USA300, for instance, either as an infecting or colonising strain triggered systemic
immune responses with greater magnitude than other S. aureus strains [221].
Colonisation is a multifactorial process, to which likely many bacterial components
100
contribute causing detrimental effects onto the human immune system. Components like
bacterial adhesins, such as cell wall associated factors ClfB, bind to the cell cornified
envelop immobilised protein loricrin and this binding is highly ClfB expression
dependent (in vivo – mouse model) [222]. Interestingly, our study showed significantly
higher IgG response against ClfB (P < 0.05) in non-carriers than carriers (Figure 29).
This result is in line with earlier studies from Dryla et al and Clarke et al., where they
have shown higher IgG levels against autolysin, IssA, Map-W, Hla, IsdH, IsdA and ClfB
in non-carriers as well [223,224]. Furthermore, a study from Verkaik et al. showed the
maternally derived IgG from placenta does not protect against nasal colonisation and
antigens expressed in vivo like CHIPS, Efb, IsdA and IsdB play a major role in nasal
colonisation of young children [225]. Supporting this argument the study of Wertheim et
al. revealed that a ClfB mutant S. aureus strain (DU5997) was not able to persist in the
human nose after 2 weeks. To conclude, ClfB is a major determinant during nasal
colonisation [226] and implies that higher IgG responses against ClfB in non-carriers
could be of protective value against S. aureus bacteraemia.
Staphylococcal super antigens likely modulate nasal colonisation but the mechanism of
this contribution is still unclear. Staphylococcal super antigen 12 (Ssl12) shows
significantly higher IgG responses in non-carriers (Figure 29). Staphylococcal
enterotoxins (SE) and super antigens (SAgs) are acting as potent pro-inflammatory agents
[227]. Holtfreter et al. have already shown that neutralising antibodies against super
antigens, expressed by their colonising strains, enhanced protection against S. aureus
bacteraemia [228]. Perhaps even these non-carriers were transiently in contact with
different S. aureus strains and experienced minor staphylococcal skin infections.
Few antigens showed higher log2 median fold change between carriers and non-carriers,
but they were insignificant. Examples of such antigens are PrsA, which displayed a 2.65
fold higher IgG response in carriers, and vice versa Coa showed a 2.60 fold higher IgG
response in non-carriers (Figure 30). Supporting the importance of antibodies directed
against PrsA, previous animal studies have shown that higher IgG responses against PrsA
increased the survival time during bacteraemia in a mouse model [229].
101
The influence of clonal complexes on sample specific IgG levels
Healthy carriers analysed in this study were carrying different clonal complexes (CC) of
S. aureus, such as CC5, CC8, CC15, CC25 and CC30. We investigated the IgG responses
against the S. aureus antigens based on the clonal complexes. Interestingly, we observed
pronounced IgG responses against TSST-1 for the CC30 group carriers compared to non-
CC30 and non-carriers (Figure 31). An earlier study has already shown that individuals
colonized by a TSST-1 producing strains had significantly higher levels of antibody
against TSST-1 than individuals who carried strains without TSST-1 and that repeated
exposure to tst carrying strains triggered specific immunity or immune responses [230].
In the study presented here, 50% (8/16) of the healthy carriers were carrying the CC30
clonal complex with the tst gene and they have shown higher IgG responses against
TSST-1 and this result suggested that humans with higher IgG antibodies against TSST-1
may not develop toxic shock syndrome [225,231,231,232]. Still, we observed minimal
IgG response against TSST-1 in non-carriers and non-CC30 carriers, which indicates that
they might have been exposed to S. aureus during their lifetime and in turn developed an
immune response against TSST-1, which seemed stable. A study from Blomfeldt et al.
shows that the CC30 lineage is associated with a high fatality rate (36.4%), which is
higher in comparison to other clonal complexes [233]. Clonal complexes based genetic
variations of S. aureus have also been observed by Holtfreter et al., where nasal swabs
were obtained from 3891 adults in the population-based study of health in Pomerania
(SHIP). 75% of the nasal swabs were found to contain 7 major clonal complexes (CC7, 8,
15, 22, 25, 30 and 45), thus suggesting that there is a strong association between the
virulence gene pattern and clonal complexes. Moreover, in this study population CC30
was the most common clonal complex where the tst gene was found predominantly [22].
In earlier studies, the CC30 lineage was shown to be associated with nasal colonisation
[234–236], hematogenous infections, infective endocarditis, osteomyelitis and persistent
bacteraemia [237–240]. The S. aureus isolate USA200, a CC30 clone, has a greater
likelihood to cause infective endocarditis (IE), but is less likely to cause lethal sepsis in
rabbit models [241]. Other interesting parts of this study, besides CC30, are further clonal
complex types also influencing the IgG response. For instance CC15 showed higher IgG
102
responses compared to other clonal complexes against PrsA, Hld, HlgA, HlgC, GroEL,
IsdC, Esterase like protein and ssaA2 antigens (Figure 32)
As stated before, it is known that carriers have a higher risk of developing nosocomial
related bacteraemia than non-carriers. However, carriers with bacteraemia had a lower
risk of bacteraemia-related death [242]. Passive immunisation by recombinant ClfA, IsdB
and HlgB proteins in mice reduced the risk of bacteraemia [243]. Our results corroborate
these earlier findings and suggest that the carriers have higher level of antibodies against
virulence factor repertoires of S. aureus, which reduces the level of risk and mortality rate
during the bacteraemia compared to non-carriers. The antibodies against TSST-1, IsdA,
IsdB, HlgA, HlgB, HlgC, Hld, SACOL0480, SspA, SspP, SigB, esterase like protein,
Geh and LytM showed higher responses in carriers. These antibodies are likely connected
to the lower risk of death in carriers with bacteraemia than non-carriers with bacteraemia
[242].
Study-2: Immune responses during onset of bacteraemia
In study-2, we have profiled pre-existing antibodies specific to S. aureus during the onset
of S. aureus bloodstream infection (SABSI) by collecting serum samples from
bacteraemia and control patients. Antibody responses were studied against 133 antigens
using suspension array technology. Distinct S. aureus antigen specific immune responses
were observed between control and bacteraemia patients (Figure 33). The distinct
immune responses and the inter individual variability are in line with earlier findings
[244–247]. Diversity in the immune responses occurs due to the following aspects: (1)
genetic diversity of S. aureus isolates, (2) differential S. aureus protein expression and
the selective protein recognition by the immune system in different patients, (3) time
difference in the onset of bacteraemia, (4) colonisation status, condition of preceeding
infections and immune status [248,249].
In this study, we have identified 72 potential candidates reflecting the characteristic of
each group (Table 10). Significantly higher IgG responses against 67 antigens were found
for control patients and only 5 for sepsis patients (Table 10). Higher responses for control
patients were majorly directed against extracellular and cell wall components of S. aureus
(Figure 51) such as GlpQ, Plc, AIP linear form (Type1–4), AIP thiolacton form (Type 1,
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2, 4), MazE, Atl, Coa, SE – A, C, L, O, ESAT-6, esterase like protein, Efb-1, HysA,
IsaB, intracellular serine protease, LytM, Hlb, SplB, SspB2, SspB, SsaA, Set15, Ssl – 1,
2, 3, 5, 10, 11, Sod, Nuc, GraB (Truncated VWbp) and Aur. The biological
characteristics of few of these immunogenic antigens are described below.
Staphopain SspB2 and SspB are cysteine proteinases (with high virulence potential) and
involved in biofilm remodelling, the modification of the clotting cascade, fibrinogen and
collagen damage, the induction of vascular leakage, the blocking of phagocytosis, and the
killing of neutrophils and monocytes [250–253].
14%
14%
54%
8%
10%
Localisation based IgG response
Cell wall
Cytoplasm
Extracellular
Membrane
Unknown
Figure 51. IgG responses against localisation based 72 immunogenic antigen candidates.
The 1-phosphatidylinositol phosphodiesterase (Plc), an extracellular enzyme, promotes
the survival of S. aureus in human whole blood and neutrophils [254]. Hlb (Beta-
hemolysin) a cytolytic toxin, mainly involved in skin colonisation, is cytotoxic for human
keratinocytes [255]. Another extracellular exo-enzyme, hyaluronate lyase (HysA),
degrades hyaluronic acid (HA) in vivo, a major component of intercellular ground
substance of human and animal connective, epithelial and neural tissues. HysA also
104
penetrates the tissue in order to increase the dissemination of virulence factors and
increases S. aureus accessibility to deeper tissues [256]. A recent study from Buhren et
al. demonstrated that hyaluronidase has been using as a therapeutic choice for ophthalmic
and dermatosurgery [257]. HysA is controlled by the CodY regulator in S. aureus. The
other virulence factors regulated by CodY are Hla, Hlb, SspA, and SspB [258].
Also the membrane foldase protein A (PrsA) showed higher IgG responses for control
samples, which matches to the earlier study from den Reijer et al. who studied
bacteraemia patients and suggested that this antigen is in vivo expressed mainly in SAB
patients [259].
Also the immune modulation/immune evasion associated antigens showed higher IgG
responses in the control group. For instance staphylococcal super antigens (Ssls), which
are belonging to the non-egc family, are widely involved in the immune evasion by direct
inhibition of immune receptors. Ssls display similar structural features and, occasionally,
share common interaction partners (Ssl3/Ssl4 [260], Ssl5/Ssl11 [261,262] and Ssl1/Ssl5
[263]). Ssl5 & Ssl11 bind to P-selectin glycoprotein ligand 1 (PSGL-1) to inhibit
neutrophil rolling on epithelial cells [262]. Ssl 1 & 5 act as potent neutrophil MMP
(Matrix metalloproteinase) inhibitors, where MMP are important host factors, fine tuning
the immune responses in the defence against pathogens. Ssl 1 & 5 also prevent the
potentiation of important neutrophil IL-8 and restrict the MMP-mediated neutrophil
migration through collagen [263]. Ssl10 inhibits complement activation [264]. Apart
from Ssls, another extracellular protein Efb-1 blocks C3/C5 conversion by convertases
(complement evasion) [265]. A further exo-enzyme is aureolysin (a zinc metallo
protease), which is expressed inside the phagocytic vacuole of S. aureus [266] and
resistant to the anti-microbial peptide LL37 [267]. It is also able to cleave C3 to C3b
[268].
Perhaps higher IgG responses against these immune evasion antigens in control patients
may increase the protection efficiency towards S. aureus bacteraemia. An earlier study
demonstrated that neutralising antibodies against superantigens in nasal carriers increase
the risk of septicemia and the outcome is much better in the case of sepsis with reduced
mortality rate in compare to non-carriers [269].
105
The enterotoxins SEA, SEC, SEL and SEO are also superantigens belong to enterotoxin
gene cluster (egc) gene family and polyclonally activate the T cell at pico molar
concentrations. A superantigen binds to both MHC class II and V beta region of T cell
receptor and leads to the activation of both antigen-presenting cells and T lymphocytes.
These interactions lead to the over production of pro-inflammatory cytokines (IL – 2, IFN
– γ, TNF – α) [270]. In the healthy community, there are antibodies against SEA, SEB,
SEC, SED and TSST-1 [271–274], which neutralise egc-encoded SAg and stop their
proliferative effects on T cells [275]. Unexpectedly, neutralising antibodies against SAg
are very unusual [275]. Interestingly, treating streptococcal TSS patients with IVIG from
a pool of human plasma containing anti-SAg antibodies, a reduced cytokine production,
bacterial load, and mortality were observed [276,277].
Significant higher antibody responses against auto inducing peptides (AIP) were found in
control patients for the first time. In general, AIPs are regulated by the accessory gene
regulatory (agr) system, a quorum-sensing regulatory system [278,279]. Agr up-regulates
virulence factors essential for the disease progression in animal models of acute infection
including skin and soft- tissue infections, infective endocarditis, pneumonia, septic
arthritis and osteomyelitis [280–285]. Agr enhances the disease severity by enhancing
expressing of virulence factors such as toxins and degradative exoenzymes [286]. A
study from Shopsin et al. showed that agr functionality is not an essential for the nasal
colonisation in humans [287]. In another study Malachowa et al. demonstrated by
transcriptomic analysis of human blood infected with S. aureus a total lacking of agr
expression in an agr functional strain [288]. A likely reason for this lacking activity is
probably blocking of AIP by apolipoprotein B (apoB) in serum, which might on the other
hand enhance the binding activity with the sensor kinase agrC [289,290]. Due to lack of
agr activity, the expression of immune evasion molecules immunoglobulin protein A
(Spa) and FnBPs is promoted to disseminate into the bloodstream [288,291,292].
Our study on SpA, FnBPs, Sbi and Efb-1 showed stronger IgG responses for sepsis
patients which is in line with an earlier study from Edwards et al.. In this study FnBPs
were associated with systemic inflammation, weight loss, and mortality in murine model
[292,293]. There is clear evidence that FnbA is adequate to trigger S. aureus invasion of
106
cells via Fn bridging to α5β1 integrins [294,295]. Another study revealed a strong
antibody binding to FnBP complexed with Fn compare to non-complexed FnBP, i.e.
without Fn [296].
In summary, this study confirms the inverse correlation between specific antibodies and
severity of infection which matches to earlier findings from Stentzel et al., Adhikari et
al., and Kolata et al.. In our study a total of 72 antigen candidates have been identified,
which may have potential in patient stratification based on S. aureus-specific antibodies
during the diagnosis of S. aureus bacteraemia. Technically, compared to the classical
methods, suspension array high-throughput technology provided further insights of
humoral responses during the S. aureus bacteraemia progression.
Study-3: Immune responses during complicated bacteraemia
In this study, we have elucidated the protection efficiency of the adaptive immune system
during S. aureus bloodstream infections (SABSI). The IgG antibody responses were
studied against 64 S. aureus antigens and seem to be inversely proportional to the risk of
sepsis development (Figure 46). Eight immunogenic antigens can be used to estimate the
progression of disease in S. aureus bacteraemia patients with 75% accuracy (76%
sensitivity and 74% specificity). These immunogenic proteins comprised conserved
extracellular S. aureus proteins except SEM (superantigen) and all these proteins are
belong to the core genome of S. aureus [297]. Among these eight immunogenic antigens,
HlgC a bicomponent pore forming toxin, shows higher IgG responses in no-sepsis
patients. HlgC corresponds to the Hlg genes and was highly up regulated in USA300
during culture in human blood [298]. There is a high degree of sequence similarity
between PVL and Hlg that leads to the neutralisation of Hlg by anti-PVL antibodies
further contributing to the reduced severity of the infection [299]. Another exoprotein
regulated by the agr quorum sensing system among the immunogenic antigens is Plc (1-
phosphatidylinositol phosphodiesterase). Plc promotes the survival of S. aureus in human
blood and in PMNs [300]. Another known immunogenic antigen is IsaA, an extracellular
component of S. aureus, which showed higher IgG responses in uncomplicated sepsis
patients. A study on passive vaccination of anti-IsaA (1D9) showed an effective
107
prophylaxis treatment of S. aureus MSSA related bacteraemia in a mouse model
[301,302].
Another study from Holtfreter et al. has demonstrated experimentally that nasally
colonised volunteers showed higher antibody responses against Hla, SspB, Plc, SplB,
SplE, and SspA [303]. These results corroborates earlier findings of S. aureus carriers,
which are showing higher levels of antibodies than non-carriers, but lower mortality
[304–306]. Similarly, risk of sepsis is significantly lower in patients with higher IgG
levels against Hla, Hld, PVL, SEC-1, and PSM-α3 [299]. Furthermore, epidermolysis
bullosa (EB) patients colonised with S. aureus showed higher anti-staphylococcal IgG
against IsdA, SasG, IsaA, SCIN, Nuc, LytM, SEM,SEN, SEO than the controls and
further did not develop systemic S. aureus infection [307]. On the other hand, children
with low pre-existing IgG levels against Hla and PVL are more prone to invasive S.
aureus disease [308].
In summary, the current study extends the knowledge of humoral responses during
SABSI and provided first hints for potential diagnostic patient stratification, based on S.
aureus-specific antibody screening. In general uncomplicated sepsis patients showed very
strong S. aureus-specific antibody binding, compared to complicated sepsis patients.
Besides the patient stratification according to their risk of sepsis, this study also provides
new and promising candidates for vaccination.
108
Conclusion and Outlook
- In this thesis, serological assays using suspension array multiplexed technology
were developed, optimised, validated and established for the detection of human
antibodies in serum/plasma of controls and patients with S. aureus infection.
- This methodology further explores the in vivo progression of human adaptive
immune system during S. aureus colonisation and infection.
- In this novel high-throughput method, the assay can be performed in a fast way
with low volume of sample, with reliable and reproducible results. The method is
suitable for detecting the antibody levels against wide range of S. aureus proteins
by multiplexing.
- For the reliability of the developed assay, a control sample is always required.
Here we used pools of serum/plasma samples as the quality control for the better
reliability.
- Proteins coupled to magnetic beads (magplex) are highly stable over a period of
18 months.
- Screening of hundreds of human serum/plasma samples, revealed a group of
antigens which was able to discriminate between the groups from various
episodes of S. aureus infection.
- From the S. aureus nasal colonisation study, it was suggested that IgG against the
16 immunogenic potential antigen candidates may aid for the development of
vaccines.
- For the first time, we observed clonal complex (CC) based IgG responses and
mainly for CC30 that IgG responses against TSST-1 (p-value < 0.05) were higher
than the other CCs. This suggest that antibodies against TSST-1 may protect the
individuals from the toxic shock related infections. Moreover, CC30 are majorly
found in the hematogenous/bloodstream related infections.
- The other interesting result in this study is that for CC15 were shown stronger IgG
responses against extracellular proteins of S. aureus were shown . Due to the
restricted number of CC15 in the healthy carriers group, we were not able to
perform the statistical analysis. Further studies need to be done on clonal complex
based IgG responses.
109
- From the SABSI study (study-2), in total 72 antigens were significantly different
in IgG-responses between sepsis and control patients. Among the 72 antigens,
IgG responses against 67 antigens were higher in control and IgG responses
against 5 antigens were higher in sepsis patients. This suggests that antibodies
against these 67 antigens may protect the individuals from S. aureus associated
bacteraemia.
- The group of antigens identified from this studies can potentially be used for
patient stratification. For instance the mortality rate of S. aureus bacteraemia is
high and this patient stratification may reduce the mortality rate of S. aureus
bacteraemia patients.
- The most prominent proteins expressed during colonisation and infection (in vivo)
which elicit protective immune responses in human are derived from the
extracellular and cell wall components of S. aureus. Further studies are required
for the antigen stratification (single or multivalent antigen) for the development of
active/passive vaccines.
- The antibodies against this set of antigens might be associated with the risk of
developing S. aureus infections. The humoral responses provoked for this set of
antigens might be responsible for the lower risk of mortality observed in S. aureus
carriers with bacteraemia than in S. aureus non-carriers with bacteraemia.
- Our findings are expected to facilitate the future immunoproteomics analysis with
the inclusion of a higher number of immunogenic antigens in the assay panel.
These findings might aid for the development of new generation vaccines against
S aureus.
- The assay described here can be used as excellent research tool to gain further
insight into the endemic and demographic properties of the disease.
110
Publications
1. Specific serum IgG at diagnosis of Staphylococcus aureus bloodstream invasion
is correlated with disease progression. (Pubmed ID: 26155744, DOI:
10.1016/j.jprot.2015.06.018)
Sebastian Stentzel, Nandakumar Sundaramoorthy, Stephan Michalik, Maria
Nordengrün, Sarah Schulz, Julia Kolata, Peggy Kloppot, Susanne Engelmann, Leif Steil,
Michael Hecker, Frank Schmidt, Uwe Völker, Mary-Claire Roghmann , and Barbara M.
Bröker.
2. Characterization of human and Staphylococcus aureus proteins in respiratory
mucosa by in vivo- and immunoproteomics. (Pubmed ID: 28099884, DOI:
10.1016/j.jprot.2017.01.008)
Frank Schmidt, Tanja Meyer, Nandakumar Sundaramoorthy, Stephan Michalik,
Kristin Surmann, Maren Depke, Vishnu Dhople, Manuela Gesell Salazar, Gabriele
Holtappels, Nan Zhang, Barbara M Bröker, Claus Bachert, Uwe Völker.
3. Omics approaches for the study of adaptive immunity to Staphylococcus aureus
and the selection of vaccine candidates. (Pubmed ID: 28248221,
DOI:10.3390/proteomes4010011)
Silva Holtfreter, Julia Kolata, Sebastian Stentzel, Stephanie Bauerfeind, Frank Schmidt,
Nandakumar Sundaramoorthy and Barbara M. Bröker.
4. Laboratory mice are frequently colonized with host-adapted S. aureus and mount
a systemic immune response – note of caution for in vivo infection experiments. (Pubmed
ID: 28512627, DOI: 10.3389/fcimb.2017.00152)
111
Daniel Schulz, Dorothee Grumann, Patricia Trübe, Kathleen Pritchett-Corning, Sarah
Johnson, Kevin Reppschläger, Janine Gumz, Nandakumar Sundaramoorthy, Stephan
Michalik, Sabine Berg, Jens van den Brandt, Richard Fister, Stefan Monecke, Benedict
Uy, Frank Schmidt, Barbara M. Bröker, Siouxsie Wiles and Silva Holtfreter
5. Proteomics and immunoproteomics profiling of serum proteins and antibodies
from patients with Staphylococcus aureus induced bloodstream infection - Manuscript
under progress.
112
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Summary
Staphylococcus aureus (S. aureus) is the leading cause of serious diseases in human both
from hospital and community associated infections. Some clinical manifestations of S.
aureus infections are infective endocarditis (IE), osteoarticular infections, skin and soft
tissue, pleuropulmonary, and device-related infections. In Germany, S. aureus is the
second most common cause of hospital-acquired (HA) infections. About 16.7% of these
nosocomial infections are caused by HA-MRSA clinical isolates. It has been a huge
threat for the clinicians/scientists to control the emergence of such infections caused by S.
aureus. S. aureus exhibits increasing virulence and resistance to various antibiotics,
complicating prevention and treatment of infections. Eventually, active and passive
vaccines might be the alternative strategy to deal with S. aureus related diseases. An
effective S. aureus vaccine would provide great potential security and many societal
benefits. However, so far vaccine trials have failed often due to limited number of
available antigen candidates (monovalent/single antigen) in the clinical trials. Efforts to
develop not only S. aureus vaccine but also prognosis or diagnosis tools are challenging
tasks. That was the motivation point for the current thesis to identify potential antigen
candidates for the aid of vaccine development using immunoproteomics approaches.
From the earlier studies, passive immunisation with CP5, CP8, PNAG, ClfA, SdrG,
alpha-hemolysin and active immunisation with IsdB, SEB, ClfA, CP5, CP8 were
examined during preclinical trials and found to be the best examples for potential vaccine
candidates. The antibody responses against S. aureus infections are heterogenous, still it
is possible to identify the antibody signatures to a number of corresponding S. aureus
antigens, whose abundance and presence could correlate to the disease state and may
predict treatment outcome. To support this hypothesis, goals were set to develop and
validate serological assay by indirect detection using suspension array technology (SAT).
During the study, an antigen library of 140 recombinant S. aureus antigens was
generated. Further serological assay were developed and validated to monitor the insights
of antibody mediated humoral responses during S. aureus infection from various episodes
of S. aureus infection. As an outcome, potential immunogenic antigen candidates were
identified which may be used as candidates in active/passive vaccination and to stratify
the patient. In total, three studies were carried out using serum and plasma samples from
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S. aureus nasal colonised healthy individuals (carriers and non-carriers) and bacteraemia
patients (control, complicated and uncomplicated sepsis). Bead-based assays were
performed and subsequent statistical analyses were done to identify immunogenic
antigens that might discriminate between the different clinical status and outcome.
Screening of healthy individuals (study-1) have shown significantly higher IgG responses
against 14 antigens in S. aureus nasal carriers compared to non-carriers. Furthermore, the
clonal complex 30 group of healthy carriers has shown significantly higher IgG responses
against toxic shock syndrome toxin-1 (Tsst1) in comparison to non-clonal complex 30
healthy carriers. Study-2 have shown extensively higher IgG responses against 67
antigens in control samples compared to sepsis patients. 50% of the antigens eliciting
different immune responses belonged to the extracellular components of S. aureus. The
IgG responses against MSCRAMM proteins such as FnbA, FnbB, Efb-1 have been
shown to be significantly higher in complicated sepsis. Study-3 have shown notably
higher IgG responses against 8 antigens (Plc, SspB, IsaA, SEM, GlpQ, HlgC,
SACOL0444, SACOL0985) at baseline in uncomplicated sepsis patients compared to
patients subsequently developing complicated sepsis.
In summary, the group of immunogenic antigens that have been identified in these studies
using immunoproteomics approach could be a starting point for the development of S.
aureus vaccines. Moreover, the suspension array technology approach facilitated the
identification of new S. aureus antigen candidates in addition to earlier reports. The
current results of this study support the hypothesis that it is possible to identify a
serological response to potential S. aureus antigens that correlate to progression of S.
aureus infections.
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Acknowledgements
This thesis was prepared at the department of functional genomics at the University of
Medicine, Greifswald, headed by Prof. Dr. Uwe Völker.
At first, I would like to express my deepest sense of gratitude to my supervisor Prof. Dr.
Uwe Völker for giving me this great opportunity to perform my doctoral study in the
most interesting field Host-Pathogen interactions using Immunoproteomics and for the
agreement to supervise my thesis as the first examiner.
I would like to thank immensely my advisor/guru Dr. Frank Schmidt for the supervision
since April 2012. You have been a tremendous mentor for me. I am grateful for his
concrete support, advice and critics during my work. He has also provided insightful
discussions about the research. He has given me the opportunities to learn many new
analytical techniques, softwares etc.
I am deeply grateful to Dr. Stephan Michalik for his cooperation during my Ph.D. in the
process of developing and establishing the serological assay using FLEXMAP 3D®,
developing the scripts for the data analysis.
I gratefully thank to Prof. Dr. Barbara M. Bröker, Dr. Sebastian Stentzel, Dr. Silva
Holtfreter, Dr. Julia Kolata and Dr. Hege Vangstein Aamot (Akershus
Universitetssykehus, Oslo) for their collaboration and their valuable scientific tips during
the Ph.D.
I would like to thank Dr. Vishnu Dhople, Dr. Manuela Gesell Salazar, and Ms. Annette
Murr for their assistance in the mass spectrometric measurements.
I sincerely thank to Dr. Maren Depke and Ms. Tanya Meyer for the proofreading of this
thesis.
I deeply acknowledge the support by our laboratory assistants, especially Mrs. Kirsten
Bartels, Mrs. Jette Anklam, Mrs. Ulrike Lissner, Mr. Marc Schaffer, Mrs. Anja Wiechert,
Mrs. Katrin Schoknecht, and Ms. Sophie Eisenlöffel.
138
I also thank to my friends (too many to list here but you know who you are!) for
providing the support and friendship that I needed.
A special thanks to my family. Words are not enough to express how grateful I am to my
wife Nithya, my mother, and my father for all of the sacrifices made on my behalf. Your
prayer for me was what sustained me thus far.
I would like to express my thanks to my beloved daughter Skandana and son Skawin for
being such a good children’s always cheering me up.
I would like to express my thanks to my sisters and my brothers for their encouraging
support throughout my life.
And now; Last but not least, I would like to thank God for letting me through all the
difficulties. I have experienced your guidance day by day. You are the one who let me
finish my degree. I will keep on trusting you for my future. Thank you God.
139
Appendix
The appendix of this thesis is saved on the enclosed CD. It contains the raw data
collected from the FLEXMAP 3D®
for all the 3 studies, stability study data, validation
data and supplementary documents. Furthermore the supplementary documents contains
the list of antigens used for the study-1 and 2 and it is labelled as supplementary 1 and the
supplementary 2 contains the list antigens used for the study-3. Furthermore, it contains
the list of samples with clinical status, meta data information, protein annotation, half
maximal calculations and other graphs we generated during the data analysis for all the 3
studies.
140
Eidesstattliche Erklärung
Hiermit erkläre ich, dass ich die vorliegende Dissertation selbständig verfasst und keine
anderen als die angegebenen Hilfsmittel benutzt habe.
Die Dissertation ist bisher keiner anderen Fakultät, keiner anderen wissenschaftlichen
Einrichtung vorgelegt worden.
Ich erkläre, dass ich bisher kein Promotionsverfahren erfolglos beendet habe und dass
eine Aberkennung eines bereits erworbenen Doktorgrades nicht vorliegt.
Datum Unterschrift
141
-Thirukkural (World ethics and moral written by a Tamil poet between 3rd and 1st centuries BCE)
Explanation: The stalks of water-flowers are proportionate to
the depth of water; so is men's greatness proportionate to their
minds.