Download Surface Characterization of Gram-Negative Bacteria

Surface Characterization of
Gram-Negative Bacteria and
their Vesicles
Using Fourier Transform Infrared Spectroscopy and
Dynamic Light Scattering
Sofia Carlsson
Degree Thesis in Chemistry 30 ECTS
Master’s level
Report passed: 07 2012
Supervisors: Madeleine Ramstedt
Abstract
Gram-negative bacteria are known to produce vesicles, why and how is still relatively
unknown. Gram-negative bacteria are widely studied but still there are large areas in
the field covering the bacterial chemistry that are unknown.
This study is a pilot study aimed to complement the studies on surface properties for
Gram-negative bacteria and their vesicles. The study is focused on the bacteria
Escherichia coli (E.coli). Differences and similarities between bacteria and vesicle
surface characteristics and chemical composition are presented using Attenuated
Total Reflectance Fourier Transformed Infrared Spectroscopy (ATR-FTIR) and
Dynamic Light Scattering (DLS).
There does not seem to be any direct correlation between bacteria and vesicles
regarding the surface characteristics. However, this study provides interesting data
regarding the production of vesicles and vesicle characteristics as well as information
regarding the surface characteristics of bacteria.
II
List of abbreviations
ε
f (κa)
η
ATR
D
DLS
DNA
E.coli
FTIR
Ge
HCl
IR
k
LPS
OM
OMP
OMV
R
SDS-PAGE
T
TEM
Z
ZnSe
XPS
U(E)
the dielectric constant
a function of the ratio between the radius of the particle and the
thickness of the electrical double layer.
The viscosity of the solution
Attenuated total reflectance
Translational diffusion coefficient
Dynamic light scattering
Deoxyribonucleic acid
Escherichia coli
Fourier transform infrared
Germanium
Hydrochloric acid
Infrared spectroscopy
Boltzmann constant
Lipopolysaccharides
Outer membrane
Outer membrane protein
Outer membrane vesicle
Hydrodynamic radius of a particle (or molecule)
Sodium dodecyl sulfate polyacrylamide gel electrophoresis
Absolute temperature
Transmission Electron Microscope
Zeta potential
Zinc selenide
X-ray photoelectron spectroscopy
Electrophoretic mobility
III
IV
Table of contents
Abstract .......................................................................................................................... I List of abbreviations .................................................................................................... III Table of contents ........................................................................................................... V Why study vesicles from Gram-negative bacteria? ....................................................... 1 Bacteria vs. vesicle......................................................................................................... 1 Gram-negative bacteria .............................................................................................. 1 Vesicles ...................................................................................................................... 2 Techniques used for analysis ......................................................................................... 4 Infrared (IR) spectroscopy ......................................................................................... 4 Fourier Transformed Infrared (FTIR) Spectroscopy ................................................. 5 Attenuated Total Reflectance (ATR) ......................................................................... 5 Microbial cells ........................................................................................................... 6 Dynamic light scattering, DLS .................................................................................. 6 Size measurement .................................................................................................. 6 Zeta potential measurements.................................................................................. 7 Method ........................................................................................................................... 9 Samples ...................................................................................................................... 9 Buffer preparation .................................................................................................... 10 Preparation of bacteria ............................................................................................. 10 Preparation of outer membrane vesicles .................................................................. 10 Sodium dodecyl sulfate polyacrylamideb gel electrophoresis (SDS-PAGE) ...... 10 DLS analysis ............................................................................................................ 10 ATR-FTIR spectroscopic analysis ........................................................................... 11 Results and discussion ................................................................................................. 11 Optimization of the method ..................................................................................... 11 DLS analyses ........................................................................................................... 14 ATR-FTIR spectroscopic analysis ........................................................................... 17 Other data ................................................................................................................. 23 SDS-PAGE .......................................................................................................... 24 X-ray photoelectron spectroscopy, XPS .............................................................. 24 Conclusions .................................................................................................................. 26 Acknowledgement ....................................................................................................... 26 Bibliography ................................................................................................................ 28 V
VI
Why study vesicles from Gram-negative bacteria?
So far little is known about the outer membrane vesicles (OMV) emerging from the
cell surface. Why are they produced, how do they interact and what are their
characteristics? There are many theories and studies but so far few answers (1), (2),
(3), (4), (5). This study tries to give some answers regarding the surface of vesicles
compared to the surface of bacteria. Are there some similarities? Are the
characteristics of vesicle surfaces consistent or random? And if similar to bacteria,
can we use them to further explore the bacterial surface and its interaction with its
surroundings?
So why do we want to explore the surface of bacteria? Due to its direct contact with
the external environment bacteria are in need of a cell surface that is adaptable to
rapid changes in, for example, environmental components, osmotic pressure and
nutrients. But it still needs to be strong and stable to survive different kinds of threats
(1). This need combined with flexibility have developed unique cell surface properties
where still much is unknown. A greater extent of knowledge could develop the usage
of bacteria and enable the usage of bacterial vesicles, for example, in the delivery of
different drugs to target cells (1). The focus of our research is to gain a greater
knowledge of the characteristics of the bacterial surface in the purpose to reach a
more comprehensive understanding of bacterial adhesion.
Bacteria vs. vesicle
Gram-negative bacteria
A Gram-negative bacterium differs from a Gram-positive bacterium mainly when it
comes to the outer membrane (OM) and the periplasm which are both missing in the
Gram-positive bacteria.
The OM consists of lipopolysaccharides (LPS), phospholipids and outer membrane
proteins (OMPs). The phospholipids form a bilayer structure where the LPS are
attached to the outer leaflet. This structure gives the OM a hydrophilic outer surface
and a hydrophobic core. The OMPs forms hydrophilic pores that can act as a link, or
channel, between the periplasm and the external environment (1).
The periplasm is a macromolecular gel consisting of peptidoglycan and includes
periplasmic proteins and other macromolecules. It has a high, fixed polyelectrolyte
concentration which makes it isosmotic with the cytoplasmic membrane (1).
The outline of Gram-Negative cell wall can be seen in Figure 1 (1).
Figure 1. Schematic image of Gram-negative bacteria.
1
Bacteria are very small organisms with a size range in microns; the size varies
between different species. If looking at the transmission electron microscope (TEM)
image, Figure 2, it can be seen that E.coli mutant ∆waaG has a size of approximately
1.0-1.5µm.
Furthermore, due to this small size, bacteria have a very high surface to volume ratio,
approximately 100 000 times (6). The high surface to volume ratio indicates that the
membranes of the bacteria is a large contributing factor to the dry mass of the
bacteria and understanding this part of the cell should therefore contribute greatly, to
the complete understanding of cellular function and -interaction.
From previous studies it seems that bacterial size and shape of the cells as well as the
buoyant density differ during the growth, they all seem to increase in the exponential
phase. However, the zeta potential seems to be constant (7). This indication is
reinforced if looking at E.coli bacteria in a TEM. Figure 2a shows an E.coli bacterium
that probably is in the beginning of the exponential phase, prior to cell division. This
can be compared to Figure 2b that shows a mutant of Pseudomonas aeruginosa that
is just about to divide.
a
b
Figure 2.TEM image of E.coli (a) and Pseudomonas aeruginosa (b). In the red
circle in image (a) there is a bacterium in the exponential phase, prior to cell
division. In the red circle in image (b) there is a bacterium in the exponential
phase during cell division (8).
Vesicles
Gram-negative bacteria naturally produce OMVs; small closed bubbles released from
the bacteria surface. They are suggested to be formed as a bulge arising from the
outer membrane as can be seen in Figure 3 (2), (3). The surface of OMVs are
suggested to consist of bacterial outer membrane including LPS and OMPs and it is
proposed that they are filled with periplasmic components such as proteins, enzymes
and toxins (3), (9). The full mechanism of vesicle production and its components are
still not fully understood. In electron microscope they appear as spherical structures
with a diameter of 50-250 nm depending on strain (2), (3). It is noteworthy that
vesicles are only produced by live bacteria (3).
2
Figure 3. Formation of membrane vesicles.
The purpose of vesicle production is still not fully understood although they seem to
be used as transport vehicles for Gram-negative bacteria components to eukaryotic
and prokaryotic cells in the surrounding environment of the bacteria (2). They are
suggested to function as both a channel of waste handling as well as a delivery
system. The range of components that the OMVs are suggested to transport is very
broad including virulence factors, antimicrobial agents, DNA, toxins, proteins,
antibiotics etc. (3). It seems the components transported can be either fully
functional substances with a purpose and/or a target cell or waste material such as
missfolded proteins (4). It also seems they can act as virulence factors, carrying
substances that damage the target cell in different ways (3). One of the advantages
suggested for vesicle production is the possibility to reach host cells located deep in
tissues that cannot be reached by the bacteria themselves (2).
Vesiculation seems to occur in various environments such as solid or liquid cultures,
in biofilms, planktonic cultures as well as natural environments (2), (3), (4). This also
includes infected tissues (5). Approximately 1 % of the OM materials in a culture of
E.coli are vesicles (5).
Interaction of Gram-negative OMVs with bacteria is suggested to occur by
attachment to the membrane via adherence or fusion (5). After adherence the
vesicles can be internalized into the host cell (5). For Gram-negative bacteria, this is
quite a straight forward process that does not need complete reorganization of the
outer membrane due to the fact that the OMVs are made of the outer membrane and
consist of similar components as the bacterial cell wall (3). When fused into the
membrane, the content of the vesicle can be delivered directly into the periplasm of
the bacteria (3). The interaction mechanism between Gram-negative OMVs and
Gram-positive bacteria is still unknown (3).
Due to the fact that the mechanism, composition and production of vesicles are so
unknown, there are a number of theories regarding vesiculation although they are not
fully comprehensive and restricted to a few species of bacteria.
Regarding surface characterization there has been studies on Pseudomonas
aeruginosa suggesting that the growth phase of bacteria only affects the zeta
3
potential of the vesicles produced, giving a more negative zeta potential for vesicles
when the bacteria are in the stationary phase. The size and the shape are suggested to
remain constant (7). Studies, presented in this report, on E.coli vesicles will
complement earlier findings in this field.
Other interesting studies suggest that there are also differences in the inner content
of the vesicles depending on when the vesicles are produced. For example, it is
suggested that vesicles produced in the exponential growth phase have a higher
amount of proteins (7). It was also suggested that the vesicles produced in the
exponential phase have a higher density and are more translucent (7). The amount
vesicle produced is suggested to be affected by the charge of the LPS of the bacteria
(3), (9) and the rate is suggested to be dependent on the bacterial growth phase with
a maximum rate during the late exponential phase (2), (4). All of these things are
tested on other bacterial stains than E.coli and they will not be directly studied in the
scope of this project.
Moreover, early studies made the assumption that the surface charge of bacteria were
homogeneously distributed over the surface although newer studies indicates that in
reality there is a higher concentration of surface sites at the poles of the bacteria (10).
If so, and if vesicles are produced as suggested than there will most likely be a
difference in vesicle structure due to where on the bacterial surface it is spun off
from. This topic is also outside the scope of this project.
Techniques used for analysis
Infrared (IR) spectroscopy
Each atom and each molecule has a motional freedom of three i.e. in x, y and z
direction. These motional freedoms can be divided into three different kinds of
movements; translational, rotational and vibrational as can be seen in Table 1. The
vibrational mode of motional freedom is often referred to as the normal mode of
vibration (11), (12).
Table 1. Motional freedoms of molecules. N stands for numbers of atoms (11),
(12).
Types of degrees
freedom
Translational
Rotational
Vibrational
Total
of Linear molecule
3
2
3N-5
3N
Non-linear molecule
3
3
3N-6
3N
Furthermore, the vibrational modes can then be divided into stretching (change in
bond length) and bending modes (change in bond angle). The stretching can be
divided into symmetric (in-phase) or asymmetric (out-of-phase) stretching.
Moreover, the bending modes can be divided according to type of bending;
scissoring, rocking, wagging and twisting. In addition, they can also be divided into
in-plane (scissoring and rocking) or out-of-plane (wagging and twisting) (11), (12).
The theory behind IR spectroscopy is based on the vibrational modes of motional
freedom of the atoms in a molecule and how these vibrations will affect neighboring
atoms through chemical bonds (12). However, not all vibrations give rise to an IR
spectrum, the vibration has to create a change in the dipole moment of the molecule.
The larger the change, the more intense will the absorption bands be (11).
A spectrum is obtained by letting infrared radiation pass through the sample and
then measuring which fraction of the incident radiation is absorbed at a specific
energy (11). Due to the vibrations different substances will absorb different amount of
4
radiation in different regions giving rise to individual spectra that give us information
regarding the bonds within a molecule (11).
The main advantage of IR spectroscopy is that almost any sample in almost any state
can be analyzed with different techniques (11). However, there is one special feature
that the analyzed samples have to possess namely, the molecule has to have an
electrical dipole moment that changes during vibration (11). An IR spectrum is
obtained when a sample absorbs radiation in the region of infrared electromagnetic
radiation (400-4000 cm-1 for mid-infrared). When the radiation is absorbed, a
quantum mechanical transition occurs between two vibrational energy levels (the
ground level and the first excited level for mid-infrared). The difference in energy is
directly related to the frequency of the radiation (12).
Fourier Transformed Infrared (FTIR) Spectroscopy
The basic idea behind the FTIR spectroscopy is that the inference between two beams
of radiation will yield an interferogram; a function of the change of path length
between the two beams. This information is then transformed by a mathematical
method called Fourier-transformation. The basic outline of the method can be seen in
Figure 4. The main advantage of FTIR spectroscopy is that the sample is scanned
with the whole spectral range at each time (12).
Source
Interferometer
Sample
Detector
Amplifier
Analog-to
dogital
converter
Computer
Figure 4. Basic outline for FTIR spectroscopy.
The crucial part in this setup is the interferometer, where the Michelson
interferometer is the most common one. It consists of two mirrors, one fixed and one
moveable, and a beam-splitter. The two mirrors are positioned 90 ° angle from each
other. The radiation hits the beam-splitter and then 50 % of the beam goes to each
mirror. The mirrors reflect the beams back to the beam splitter where they recombine
and interfere; this beam is called the transmitted beam and is the one detected by the
FTIR spectroscopy (12), (11).
The moving mirror will create an optical path difference between the two mirrors. If
both mirrors are at the same position they will reflect both beams to the detector.
During these conditions, the beams will be in phase when they hit the detector,
creating a perfect constructive interference and the radiation signal will be at
maximum intensity (12). If not at the same position, there will be an interference
pattern created by the difference in optical path length between the two mirrors (11).
During each scan the sample will absorb some radiation, lowering the intensity of the
incident radiation (12). The second mirror is moved between each scan giving rise to
a different interference pattern each time. It is these interference pattern that give
rise to the raw data, also called interferogram, that are transformed by the
mathematical algorithm Fourier transformation (12).
Attenuated Total Reflectance (ATR)
Different reflectance methods are used when the samples are too difficult to analyze
using normal transmittance methods. For ATR the sample is placed on a crystal and
then radiated with a beam of IR light. The method is based on total internal reflection
i.e. an optical phenomenon where, if the incoming angle of radiation is bigger than
the critical angle, all light is reflected. The critical angle is a function of the refractive
indices of the crystal surfaces (11). A schematic image of the ATR setup can be seen
in Figure 5.
5
The crystal used is of materials that have low solubility in water and have high
reflective index. This includes for example zinc selenide (ZnSe) and Germanium (Ge)
(11).
Figure 5. Example of an ATR setup.
Microbial cells
When analyzing microbial cells like bacteria using IR spectroscopy, a large number of
peaks can be expected. Proteins represent a major part of the bacterial mass and
consequently some peaks can be overshadowed and the major peaks in the spectra
will correspond to vibrations in proteins (11).
Due to the large penetration depth of the IR beam, in the range of a few µm, the
whole cell will be analyzed and consequently, FTIR spectroscopy cannot distinguish
between the cell wall and the inner cell (11), (10).
Dynamic light scattering, DLS
Size measurement
All particles in a liquid possess something called Brownian motion; a random motion
created when the surrounding medium push the particles around in the solution. The
speed of motion is directly proportional the particle size, the larger the particle the
slower the speed (13). The speed of motions is also proportional to the temperature
of the solution, as can be seen in Equation 1 (14).
The size measurements of a particle in a solution starts with illumination of the
solution with a laser and then the light will scatter due to the particles in the solution.
How they scatter is dependent on the size and this is measured by measuring how
fast the intensity of the scattered light fluctuates. Small particles will cause the
intensity to change more often than larger particles. The velocity of Brownian motion
for a particle is defined by a property called the translational diffusion coefficient (D)
and it is correlated from the intensity variations of the scattered light by a correlation
program (14). The D-value is then used to recalculate the hydrodynamic radius using
Stoke-Einstein equation, as can be seen in Equation 1, to give a value of the size.
Although, the size calculations are based on the particles hydrodynamic radius i.e. the
size reported is for a sphere that has the same D-value as the particle.
For a non-spherical particle the size measured will be either larger or smaller than
the real size depending on the particle shape (14). This also gives that small changes
in shape may alter the hydrodynamic size although not all changes are detected. For
6
example if the particle is rod-shaped then small changes in length will be detected but
small changes in thickness will most likely not (14).
Equation 1. The Stoke-Einstein equation; where D is the diffusion coefficient, k is
the Boltzmann constant, T is the absolute temperature, η is the viscosity of the
solution and R is the hydrodynamic radius of the particle (or macromolecule).
When measuring size, the temperature needs to be stable so that the motions
measured are Brownian motions and not flow motions in the sample created by
temperature differences, and that the correct T is used in calculations (14).
Furthermore, knowledge regarding the temperature also gives knowledge about the
viscosity of the solution which is required in the hydrodynamic size measurements.
The size measurement is also affected by the ionic strength of the solution i.e. the
conductivity. In a low ionic strength solution, the double electric layer will be more
extended than in high ionic strength (11).
Another factor that can affect the hydrodynamic size is the structure of the surface of
the particle; changes to the surface structure may alter the diffusion speed and
therefore also alter the hydrodynamic size of the particle (14).
Zeta potential measurements
A charged particle in solution will have a layer of ions of opposite charge bounded to
it, a so called Stern layer. Moreover, a second layer of ions are surrounding the
particle, a so called diffuse layer. The zeta potential is a physical property of a particle
in a suspension, and it is created in the slipping plane between the Stern layer and the
diffuse layer as can be seen in Figure 6 (15).
7
Figure 6. Schematic representation of zeta potential.
The zeta potential can indicate the degree of interaction between particles in a colloid
system, and consequently the stability of the same system. A near neutral zeta value
(-10 to 10 mV) or a zeta value of zero (isoelectric point) is an indication of an unstable
system i.e. the particles will flocculate. In contrast to a strongly negative (lower than 30 mV) or strongly positive (higher than 30 mV) zeta potential that indicates a stable
system i.e. the particles will repel each other and remain in solution (13), (15). It can
also be used to investigate the surface charge of a particle since the zeta potential is
dependent on the surface charge. However, the absolute zeta potential value will be
lower than the surface charge, due to the ions in the double electrical layer (13), (15)
(14).
The zeta potential is measured by measuring the electrophoretic mobility of the
particle. As the particle move the scattered light is phase shifted and this phase shift
is proportional to the zeta potential of the particle. The data is correlated to zeta
potential via the Henry equation as can be seen in Equation 2 (15).
8
Equation 2. Henrys equation (15).
Several different factors, such as the strength of the electric field, the dielectric
constant (ε) of the medium, the viscosity (ŋ) of the medium and the zeta potential (Z)
of the particle, affect the electrophoretic mobility (U(E)) (13), (15). The Henry
function f (κa) is a function of the ratio between the radius of the particle and the
thickness of the electrical double layer. For aqueous media with moderate electrolyte
concentration this value will be 1.5 and these conditions are referred to as the
Smoluchowski approximation (15).
The zeta potential is affected by several factors such as pH and conductivity of the
medium. For pH effects it can be seen that a particle in an alkaline solution generally
will have a more negative zeta potential and a particle in an acid solution will have a
more positive zeta potential (13). Furthermore, earlier in this report it was described
that the hydrodynamic size was affected by ionic strength. The same can be said
regarding the zeta potential although, in this case it is the interaction between the
particle surface and surrounding ions that can change the zeta potential (15).
Method
Samples
Table 2. Bacteria strains used in this study.
E.coli strain
BW25113
RN103
RN104
RN105
RN106
RN107
BW25113/pMF19
E.coli strain without flagella
BW25113
RN101
RN102
RN103
RN104
RN105
RN106
RN107
Strains for vesicle production
BW25113
RN103
RN104
RN105
RN106
RN107
Modified gene
WT
ΔwaaF
ΔwaaG
ΔwaaL
ΔwaaP
ΔgalE
WT-o-antigen
Modified gene
WT/flhD
ΔwaaC
ΔwaaE
ΔwaaF/flhD
ΔwaaG/fhlD
ΔwaaL/flhD
ΔwaaP/flhD
ΔgalE/flhD
Modified gene
WT/flhD
ΔwaaF/flhD
ΔwaaG/fhlD
ΔwaaL/flhD
ΔwaaP/flhD
ΔgalE/flhD
9
Core OS
Lipid
hldE
waaC
waaF
waaG
waaL
waaP
galE
BW25113
Figure 7. LPS-differentiated E.coli mutations (8).
Buffer preparation
Acetate buffer with an ionic strength of 20 mM and a pH of 5.5 was prepared by
mixing 1.6402 g of CH3COONa with 244 µl CH3COOH and then water was added to
the final volume of 1000 ml. Phosphate buffer with an ionic strength of 20 mM and a
pH of 7 was prepared by mixing 0.0.5531 g of NaH2PO4*2H20 with 1.8442 g of
NaHPO4*12H2O and then water was added to the final volume of 1000 ml. Finally,
the Tris buffer with an ionic strength of 20 mM and a pH of 8 was prepared by
mixing 5.2048 g of TRISMA base with 2.1 ml of HCl and then water was added to the
final volume of 1000 ml.
Preparation of bacteria
Bacteria were grown in 2 ml of ISO-SENSITEST liquid medium (Oxoid), collected by
centrifugation at 10 000 rpm for 3 min and washed in 2 ml of an appropriate buffer.
Samples were stored on ice and measured within 3 hours after preparation.
Preparation of outer membrane vesicles
Outer membrane vesicles (OMV) were prepared from E. coli BW25113 and mutants
of this strain deficient in different steps of LPS biosynthesis. Additionally, these
strains had a transposon insertion in flhD gene rendering them free of flagella. Such
strains were chosen as flagella are known to contaminate OMV preparation.
Bacteria were grown in 500 ml of ISO-SENSITEST liquid medium (Oxoid) containing
kanamycin 50 µg/ml and collected by centrifugation at 5 000 rpm for 30 min at 4°C.
Supernatant containing OMVs was collected and filtered through a sterile filter with
pore size of 0.2 µm (Sarstedt). The filtrate was centrifuged in ultra centrifuge at 29
000 rpm for 3 hours at 4°C. The pellet was re-suspended in 600 µl 20 mM Tris-HCL,
pH 8.0.
Sodium dodecyl sulfate polyacrylamideb gel electrophoresis (SDS-PAGE)
OMV samples were diluted in x2 loading buffer and separated for 1 h at 160V on 12%
ready-made SDS-PAGE (Invitrogen). 15µl of a sample was loaded to each lane of the
gel. After this the gel was stained with colloidal Coomassie stain overnight and
destained in water.
DLS analysis
Particle size and zeta potential were measured at 25 °C using a Malvern Nano ZS
zetasizer (16). Purified bacteria were diluted in Tris-, phosphate- or acetate buffer
prior to analysis so that the suspension had an optical density of 0.5 at a wavelength
of 600 nm. Purified OMV’s were diluted 10 times in Tris buffer For hydrodynamic
size measurement the dynamic light scattering method was used and the raw data
were converted by the Strokes-Einstein relationship. For zeta potential
measurements laser Doppler Micro-electrophoresis method was used and the raw
10
data were converted by the Smoluchowski approximation. For both size and zeta
measurements a clear disposable zeta cell was used during analysis.
ATR-FTIR spectroscopic analysis
The chemical composition was analyzed using a Bruker Vertex 80v spectrometer (17)
with a 3 reflection ZnSe crystal plate placed on a MIRacle base optics assembly (18).
Bacteria and OMV samples were centrifuged and the suspension removed. The pellet
was then placed on the ATR crystal. For OMVs the samples had to be frozen with
liquid nitrogen to enable transfer from the sample tube and placement on the crystal,
this was due to the small amount of purified OMVs and also because of the slimy
texture of the OMVs.
Buffer spectra were also obtained and later subtracted from the other spectra.
Results and discussion
Optimization of the method
The project started by developing an analysis method for bacteria as well as for
vesicles. First of all, the lowest possible ionic strength of the buffer solution had to be
established to ensure that the conductivity remained as low as possible but also to
guarantee that the bacteria stay intact during the analysis. During some initial sample
preparation there were indications of bacterial leakage for Pseudomonas aeruginosa
at the ionic strength of 10 mM. The same leakage was not detected for E. coli
indicating a more robust cell. Although to ensure that the most suitable ionic strength
were used, tests with different ionic strength of phosphate buffer were performed. As
can be seen in Figure 8, the zeta potential gets less negative the higher the ionic
strength. This is due to that there are more ions in the solution that can screen the
surface charge and therefore lowering the absolute zeta potential value. However, for
ΔwaaC at ionic strength of 10 mM and 20 mM, it can be seen that the zeta potential is
slightly higher at 20 mM indicating that there might be some instabilities in E.coli at
10 mM ionic strength. Analysis of viability did not show a large difference between 10
mM and 20 mM. However, for the rest of the measurements an ionic strength of 20
mM was used.
Zeta potential (mV)
0
‐10
10 mM
20 mM
50 mM
100 mM
‐20
‐30
WaaC
‐40
WT
‐50
‐60
‐70
Ionic strength
Figure 8. Variations in zeta potential, for ∆waaC and WT, due to alterations
regarding the ionic strength of the phosphate buffer.
Growth conditions for the bacteria were also optimized; we compared properties of
bacteria grown on blood agar plate or in liquid media. Both size and zeta potential
values showed that the media did not have substantial influence on the results
although standard deviation values were somewhat larger for the bacteria grown on
11
3000
‐55
Zeta potential (mV)
Hydrodynamic size (d.nm)
blood agar plate, as can be seen in Figure 9a and b, so the rest of the measurements
were done with bacteria grown in liquid ISO-SENSITEST media.
2000
1000
0
waaC‐LB
waaC‐blood
Bacterial strain
‐57
waaC‐LB
waaC‐blood
‐59
‐61
‐63
‐65
Bacteria strain
a
b
Figure 9. Effects from growing sample on different culture media (blood plate or
liquid media), on hydrodynamic size (a) and on zeta potential (b).
The vesicle production was a time-consuming process so to save time as well as
sample the vesicles were stored in a freezer after DLS analysis and then used for IR
after defrosting. To ensure that the vesicles stay stable during freezing, DLS analyses
were preformed before and after freezing and the result can be seen in Figure 10a and
b. There was some effect although it did not follow any trend and it was relatively
small. The source of the difference could be from either the freezing process or the
defrosting of the samples. The difference could also be due to uncertanties in the
measuring method; giving a higher uncertanty of the absolut zeta potential value
when the zeta potential was close to zero. Due to the small difference, the conclusion
was made that the vesicle samples could be stored in the freezer.
350
0
ΔwaaF
WT
‐2
250
200
Zeta potential (mV)
Hydrodynamic size (d.nm)
300
Fresh
sample
150
Defroste
d sample
100
50
‐4
Fresh
sample
‐6
Defroste
d sample
‐8
0
ΔwaaF
WT
Bacteria strain
a
‐10
Bacteria strain
b
Figure 10. Effects on hydrodynamic size (a) and zeta potential (b) from freezing
vesicle sample.
Three different buffers were used with three different pH; one acid, one alkaline and
one neutral solution. These buffers were used during analyses of bacteria to establish
the most suitable buffer for analyses of bacteria and vesicles. As can been seen in
Figure 11, bacteria in all buffers follow the same trend. Although bacteria in
phosphate buffer tended to have the most negative zeta potential and bacteria in
actetate buffer tended to have the least negative zeta potential. This result does not
follow the expected trend were the most alkaline solution would have the most
negative zeta potential, although, these differences seem to be mainly within the
standard deviation.
From these analyses only, it is difficult to make a conclusion on which buffer would
be the most suitible for vesicle analysis as they only differ in zeta value within the
same strain but follow the same trend between the strains. But we can make the
conclusion that neither pH nor chemical composition of the buffer influence the zeta
12
potential of bacteria to any greater extend. It does not change the correlation between
two strains in the same kind of buffer.
Figure 11 can also give an indication of how different alteration to the LPS chains will
effect the zeta potential. As can be seen there are two groups of bacteria, those with
long LPS chain (∆waaL, ∆waaP, ∆galE and WT) and those with short LPS chain
(∆waaC, ∆waaE, ∆waaF and ∆waaG). From the result there can be seen that there is
no indication of effects from flagella, mutants with and without flagella seem to have
the same zeta potential. There can also be seen that the mutants with long LPS chain
have a lower zeta potential and vice versa. And if comparing the mutants with long
LPS chain with WT there is only small differences in zeta potential indicating that
small changes to the LPS chain do not effect the zeta potential to any greater extend.
For the WT with additional o-antigen, the LPS chain is even longer since the oantigen is added to the LPS chain. Consequently, it will have a less negative zeta
potential than WT as can be seen in Figure 11. Exacly how the different components
in the LPS chain is affecting the zeta potential is not in the scope of this project, to
establish that, further reseach will be needed. But from the data it can be concluded
that larger changes in length of LPS influences the zeta potential.
0
Zeta potential (mV)
‐10
‐20
‐30
Acetate
‐40
Phosphate
Tris
‐50
‐60
‐70
Bacteria strain
Figure 11. Zeta potential for different bacteria strain, in three different buffers,
each with different pH.
From the OPLS-DA multivariate Scores plot of ATR-FTIR spectroscopic data of the
bacterial strains (Figure 12a, 13a and 14a) it can be seen that the higher the pH the
more clear is the separation of the samples, based on the type of LPS mutations.
Instead of Scores plot, the model overview an be used to gain more comprehensive
information about the separation. A model overview shows if there is any significant
difference between the different components and if so, how many components that
exhibit a significant differences between them. The more components that can be
differentiated from each other, the better is the separation of the difference
substances. From this result, there can be seen that the most suitable buffer for
analysing bacteria and vesicles would be Tris buffer, due to that seven different
components could be identified in the multivarite analysis, as can be seen in Figure
12b, 13b and 14b. Bacteria in acetate buffer and phosphate buffer are less easily
separable, most likely due to the bacterial surface is protonated or changed, in the
buffers of lower pH which makes the different strains more similar to each other
and/or more different within the biological replicates of a particlular strain. One
reasonable explanation would be that the phosphates and amines on protein residues
and the LPS chain get protonated and/or that the cells release something to the
surface. Since the tris buffer seemed to best separate the different samples this buffer
13
was chosen for the remaining work. From now on in this report all samples shown
were prepared and alaysed in Tris buffer.
25
SofiaC_Bacteria_2pointBaseline_new5.M24 (OPLS/O2PLS-DA)
R2Y(cum)
Q2(cum)
20
0,9
15
WP____AB_1
WF____AB_1
WC____AB_1
WT____AB_1
10
5
to[1]
0,8
0,7
WE____AB_1
WP____AB_0
gE____AB_1
0
0,6
WG____AB_1
WG____AB_0
0,5
WL____AB_0
gE____AB_0
-5
-10
0,4
WE____AB_0
WF____AB_0
WC____AB_0
0,3
WT____AB_0
WL____AB_1
0,2
-15
0,1
0,0
-25
-0,1
-50
-40
-30
-20
-10
0
10
20
30
40
50
t[1]
Comp[1]P
-20
a
b
Figure 12. Scattered plot (a) and model overview (b) of bacteria in acetate buffer.
Numbers of components = 1+1, Number of observations = 16, Number of classes
= 6, R2X = 0,827, R2Y = 0,155, Q2 = -0,135.
SofiaC_Bacteria_2pointBaseline_new5.M25 (OPLS/O2PLS-DA)
R2Y(cum)
Q2(cum)
0,9
20
0,8
WC____PB_0
WC____PB_1
WT____PB_0
0,7
10
0,6
t[2]
WT____PB_1
WE____PB_0
WF____PB_0
WL____PB_0
WE____PB_1
0
gE____PB_1
WL____PB_1
gE____PB_0
-10
WG____PB_0
0,5
0,4
WP____PB_0
0,3
WF____PB_1
WG____PB_1
0,2
0,1
WP____PB_1
-20
0,0
-30
-20
-10
0
10
20
30
c
40
t[1]
Comp[2]P
-40
Comp[1]P
-0,1
d
Figure 13.Scattered plot (a) and model overview (b) of bacteria in phosphate
buffer. Numbers of components = 2+0, Number of observations = 16, Number of
classes = 8, R2X = 0,759, R2Y = 0,218, Q2 = -0,0716.
SofiaC_Bacteria_2pointBaseline_new5.M23 (OPLS/O2PLS-DA)
R2Y(cum)
Q2(cum)
25
0,9
20
0,8
15
t[2]
5
0,7
WL____TB_0
gE____TB_0
10
WT____TB_1
WL____TB_1
WT____TB_0
gE____TB_1
0,6
0,5
WG____TB_0
WG____TB_1
WE____TB_1
WF____TB_1
0
0,4
WF____TB_0
WC____TB_1
WE____TB_0WC____TB_0
-5
0,3
0,2
-10
WP____TB_0
0,1
-15
0,0
WP____TB_1
-20
-15
-10
-5
0
t[1]
5
10
15
20
25
30
35
40
e
Comp No.
Comp[7]P
-20
Comp[6]P
-25
Comp[5]P
-30
Comp[4]P
-35
Comp[3]P
-40
Comp[2]P
-25
Comp[1]P
-0,1
f
Figure 14. Scattered score plot (s) and model overview (b) of bacteria in Tris
buffer. Numbers of components = 7 + 2, Number of observations = 16, Number
of classes = 8, R2X = 0,993, R2Y = 0,899, Q2 = -0,276.
DLS analyses
One hypothesis was that those E. coli mutants and their vesicles that have the
shortest LPS chain would have a smaller hydrodynamic size. All sizes measured in
this report are the hydrodynamic diameter of the particle and measured in nm. If
Figure 15a and b is compared to Figure 7, it can be seen that the hydrodynamic size
does not follow the expected trend for either vesicles or bacteria. For bacteria the
14
expected trend is followed with just two exception; WT and ∆galE, that were expected
to be more similar to ∆waaL and ∆waaP. If looking at the size measurements for
bacteria there can be seen that the hydrodynamic size is relatively large in the range
of 4000 - 8000 nm although if comparing that to the size observed in the TEM
images in Figure 2 (1000-1500 nm) it can be seen that the hydrodynamic size
measured is much larger than the size of one bacterium. Indicating that the bacteria,
in the suspention used in DLS measurement, are aggregating. There were also
variation between data points for the bacterial samples, an additional indication of
aggregation.
For vesicles there is no similarity in trend compared to bacteria and they do not
follow the expected trend at any point. For example, vesicles from ∆waaF/flhD and
∆waaG/flhD was expected to be smaller than the other mutants. These results
indicate that the size of the vesicles do not correlate to the size of the bacterial
aggregates Another observation regarding the size of vesicles is that the
measurements show that they are larger than expected. As mentioned previously,
earlier studies indicate that the vesicle size would be 50-250 nm, the result from this
study indicate that vesicles from E.coli are more likely to have a hydrodynamic size in
the range of 200-400 nm (unless they also aggregate as the bacterial cells do).
Due to that the bacteria seem to have a higher tendency to cluster, fewer reliable DLS
data points could be collected and therefore is there a higher uncertainty, regarding
the hydrodynamic size values, for bacteria than for vesicle.
Hydrodynamic size (d.nm)
Hydrodynamic size (d.nm)
10000
8000
6000
4000
2000
0
Bacteria strain
a
700
600
500
400
300
200
100
0
Bacterial strain
b
Figure 15. Trend in hydrodynamic size for bacteria (a) vs. vesicle (b). Samples
are in Tris buffer.
Our hypothesis regarding the zeta potential of bacteria compared to vesicles was that
vesicles from one specific mutant would have the same zeta potential as the bacteria
and/or that the zeta potential would follow the same trend as for bacteria. If
comparing the bacteria and their vesicles regarding zeta potential, it can be seen that
this hypothesis were proved incorrect and that there is no similarity in values or
trend, as can be seen in Figure 16. Indicating that the zeta potential of the vesicle is
not correlated to the zeta potential of the bacteria, unless aggregation affects the zeta
potential.
15
0
Zeta potential (mV)
‐10
Δwaaf/fhlD ΔwaaG/fhlD ΔwaaL/flhD ΔwaaP/flhD WT/flhD
ΔgalE/flhD
‐20
‐30
‐40
Bacteria
‐50
Vesicle
‐60
‐70
Bacteria strain
Figure 16. Trend in zeta potential for bacteria vs. vesicle. Samples are in Tris
buffer.
500
400
Zeta potential (mV)
Hydrodynamic size (d.nm)
Moreover, from the Figures 15 and 16, it can be seen that vesicle hydrodynamic size
and zeta potential seem to follow some kind of weak trend, namely that, a bigger
vesicle will have a slightly more negative zeta potential (although it is within the
standard deviation). However, this kind of trend can not be found regarding the
hydrodynamic size and the zeta potential of bacterial aggregates, see Figure 15 and
16. Due to that both hydrodynamic size and zeta potential are affected by pH and
ionic strength it could be expected that there would be some correlation between size
and zeta potential of bacteria. For example, it could be expected that bacteria with
lower zeta potential will have a higher tendency to aggregate, i.e. an opposite trend
from the trend for vesicles.
Another aspect that was needed to be investigated was how the zeta potential and the
hydrodynamic size were affected by vesicles ageing. The results indicates that the
vesicles gets smaller with time and that the zeta potential perhaps gets slightly more
negative as can be seen in Figure 17 a and b. The change in hydrodynamic size can be
due to different factors such as the vesicles shrink or that there is a change in surface
structure and/or chemical composition. One explanation to the alternation in size
could be that the vesicles rearrange into smaller vesicle. The change in zeta potential
could also be due to rearrangement of the LPS chains. It could also be a result from
concentration of the surface charge as a result of the shrinking of the vesicle.
However, since the decrease in zeta potential is within the standard deviation it is
very uncertain that it is decreasing, more measurements would be needed to see if
this is a trend or not.
300
200
Day 1
100
Day 2
0
ΔwaaG
ΔgalE
Bacteria strain
a
0
‐5
ΔwaaG
ΔgalE
‐10
Day 1
‐15
Day 2
‐20
‐25
Bacteria strain
b
Figure 17. Effects on hydrodynamic size and zeta potential, of vesicle, after
letting the sample grow old. Samples are in Tris buffer.
16
400
0
Zeta potential (mV)
Hydrodynamic size (d.nm)
There were also changes in zeta potential and hydrodynamic size of vesicles due to
the growth state of bacteria. As can be seen in Figure 18a and b, bacteria in the
exponential phase (7h) produce bigger and less negative vesicles. Compared to in the
stationary phase (15h) where vesicles seem to be smaller but more negative.
As mentioned, earlier studies on vesicles from Pseudomonas aeruginosa indicate
that there is no change in size but in zeta potential with growth phase (3). For E.coli,
both zeta and hydrodynamic size were found to be affected by the bacterial growth
phase. Interesting is also that vesicles produced after 7 hours had a size larger than
the earlier documented range of size of approximatly 50-250 nm and that vesicles
produced after 15 hours were in that size range. Previously there does not seem to
have been any studies of the size after specific time periods in the growth curve of
E.coli.
300
200
7 h
100
15 h
0
ΔwaaF
WT
‐5
WT
‐10
7 h
‐15
15 h
‐20
‐25
Bacterial strain
ΔwaaF
Bacteria strain
a
b
Figure 18. Variations on vesicle zeta potential and hydrodynamic size due to
bacterial growth phase. Samples are in Tris buffer.
8000
Exp.
6000
4000
Late
exp.
2000
Stat.
0
WT
waaC
Zeta potential (mV)
Hydrodynamic size (n.dm)
As can be seen in Figure 19 a and b there is no change in zeta potential of the bacteria
during the growth curve although there are changes in size. Changes in size would be
expected during a living organisms growth curve. They grow bigger in the exponential
phase, which is the phase where they reproduce through cell division. The observed
changes in hydrodynamic size can also be due to that bacteria have different tendency
to aggregate in different state of its growth curve. And the non existing change in zeta
potential is probably due to that the bacteria can regulate its surface charge to be
constant, maybe by vesiculation. This regulation can not be performed by vesicles due
to that they are not living organisms.
0
‐10
‐20
‐30
‐40
‐50
‐60
‐70
WT
waaC
Exp.
Late
exp.
Stat.
Bacterial strain
a
b
Figure 19. Variations on bacterial zeta potential and hydrodynamic size due to
bacterial growth phase. Samples are in Tris buffer.
Bacteria strain
ATR-FTIR spectroscopic analysis
IR spectra from both vesicles and bacteria can be seen in Figure 20. For all the IR
spectra most peaks are overshadowed by the peaks from water, mainly at around
3300 cm-1, 2100 cm-1 and 1635 cm-1, indicating that the buffer spectra were
17
incompletely subtracted. Furthermore, there is also a region around 2900 cm-1 that
corresponds to hydrocarbons mainly from lipids. Moreover, for E.coli bacteria, it can
be seen a region between 1550 cm-1 and 1080 cm-1 that corresponds to vibrations
from the proteins in the bacteria. Other components in this region will be
overshadowed by the protein peaks, due to general abundance of proteins in bacteria.
When comparing with the IR spectra of vesicles, the peaks corresponding to proteins
are less intense or completely absent and there is another region in the area of 1100800 cm-1, most likely corresponding to carbohydrates, that is more intense. This
region of peaks is most likely present in bacteria spectra as well but due to the large
amount of peptides the carbohydrate peaks will be relatively small. Another possible
contribution to these peaks might be DNA or RNA although, due to the high amount
of sugar in the vesicles, the DNA and RNA vibrations are most likely less pronounced.
If looking even closer at the region between 1100 cm-1 and 1200 cm-1, in Figure 21, it
can be seen that there are four main peaks; three of them differ from the spectra of
bacteria. It is the peak at 1027, 1105 cm-1 and 1152 cm-1, the peak at 1082 cm-1 is
represented in both vesicle spectra and bacteria spectra and most likely correspond to
phosphate and sugar vibrations.
0.8
3287
1634
1233
1546
Absorbance Units
0.4
0.6
1152
1456
1105
0.2
2111
1397
1082
0.0
1027
3500
3000
2500
2000
1500
1000
Wavenumber cm-1
Figure 20. ATR-FTIR spectra for bacteria (red) and vesicles (black). Numbers
indicates wavenumber (cm-1) of the peaks. Samples are in Tris buffer.
18
Figure 21. FTIR spectra of different vesicles compared to a ∆waaL bacterium
spectra (light blue). The different vesicles spectra are as followed; ∆galE (pink),
∆waaF (black), ∆waaG (red), ∆waaL (blue), ∆waaP (green) och WT (grey).
Numbers indicates wavelength (cm-1) of the peaks. Samples are in Tris buffer.
Comparisons of spectrum from IR analyses of bacteria and vesicles have been made
using OPLS-DA multivariate analysis. The results are presented by Scores plots,
Loadings plots and the original spectra. The three different presentations are all
correlated. In the Loadings plot the areas at the positive side of the spectra will be
more intensive in the samples on the positive side of the Scores plot and vice versa.
Furthermore, there can also be seen which peaks in the original spectra that the
peaks in the Loadings plot originates from. The P(corr) value in the Loadings plot is a
value that indicates how much the samples differs at a specific wavenumber in the IR
spectra. If the P(corr) value is strongly positive or negative then there is a strong
difference between the samples. Conversely, if peak values in the Loadings plot are
close to zero, there is a weak difference between the samples.
Noteworthy, the data is total area normalized in OPUS; meaning that the intensity of
the peaks is relative to each other and that changes in one area will alter the rest. This
will have a consequence in the Loadings plot where big differences in small peaks will
be overshadowed by small differences in large peak areas.
Prior to the multivariate analysis, all spectra were baseline corrected and the buffer
spectrum was subtracted from the other spectra.
A comparison between bacteria and vesicle can be seen in Figures 22-25. The bacteria
mutants are those without flagella due to that these are the ones that the vesicles
originate from and it will also ensure that there is no influence from flagella on the
results. In the Scores plot, Figure 22, a clear distinction between bacteria and vesicles
can be seen.
Comparing the Scores plot in Figure 22 to the Loadings plot in Figure 21, there can be
seen that the peaks in the positive area of the Loadings plot corresponds to the
samples marked black in the Scores plot, i.e. to the vesicle samples. Comparing the
Loadings plot to the original spectra in Figure 24 and 25, there can be seen that the
negative area, in the Loadings plot, correspond to the area marked black in the IR
spectra. Comparing the Scores plot to the IR spectra, there can be seen that the area
marked black in the IR spectra, is the peaks that differentiate vesicles from bacteria.
The multivariate analysis result confirms the previous indications that the main
component in the vesicle that differs from bacteria is the area of peaks in the region
19
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p(corr)[1]
t[2]
around 1100-800 cm-1 (most likely from sugar) and the lack of peaks in the area of
1550 to 1080 cm-1 (most likely from proteins). From the differences in the positions
in the Loadings plot for the peaks around 1637 cm-1 and 1549 cm-1 we can conclude
that the water peak was not sufficiently subtracted. If it had been a peak at around
1637 cm-1 it would have represented a protein band and therefore followed the trend
of the other protein bands.
OPUS_Baseline_corrected_2pointbaseline_Areanorm2.M1 (PCA-X), Bacteria vs vesicle
t[Comp. 1]/t[Comp. 2]
Colored according to classes in M1
10
1,0
0,5
-1,0
WF_fD_TB_b
gE_fD_TB_b
0
WF_fD_TB_b
gE_fD_TB_b
-10
-20
-35
-30
-25
-20
R2X[1] = 0,386934
-15
-10
0,4
-0,5
-5
0
5
R2X[2] = 0,287888
1653
1531
R2X[1] = 0,386934
10
15
20
-0,3
1313
-0,8
25
1610
1
2
30
WT_fD_TB_b
20
WT_fD_TB_b
WG_ve_TB_b
WF_ve_TB_b
gE_ve_TB_b
WT_ve_TB_b
WP_ve_TB_b
WP_fD_TB_b
WG_fD_TB_b
WL_fD_TB_b
WL_fD_TB_b
WG_fD_TB_b
WL_ve_TB_b
WP_fD_TB_b
-30
t[1]
30
35
Figure 22. PCA Scattering plot, bacteria (red) vs. vesicle (black). Samples are in
Tris buffer. Numbers of components = 5, Number of observations = 18, Number
of classes = 2, R2X = 0,912, Q2 = 0,763.
Ellipse: Hotelling T2 (0,95)
1687
OPUS_Baseline_corrected_2pointbaseline_Areanorm3.M1 (PCA-X), Bacteria vs vesicle
p(corr)[Comp. 1]
0,9
1153
1028
1005
0,8
1187
-0,7
982
0,7
0,6
1431
1362
1335
0,3
1477
0,2
1105
0,1
-0,1
0,0
-0,2
-0,4
1173
-0,6
1454
1124
1088
1069
-0,9
1394
1238
Var ID (Primary)
Figure 23. Loadings plot, bacteria (red) vs. vesicle (black). Numbers indicates
wavelength (cm-1) of the peaks. Samples are in Tris buffer.
SIMCAP+1201 2012053114:05:22(UTC+1)
20
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OPUS_Baseline_corrected_2pointbaseline_Areanorm3.DS1 OPUS_Baseline_corrected_2pointbaseline_Areanorm
Observation
0,55
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1394
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1105
0,00
Var ID (Primary)
Figure 24. IR spectra for bacteria (red) and vesicle (black) correlated to the
loadings plot. The numbers represent the wavelength (cm-1) of the peaks.
Samples are in Tris buffer.
SIMCA-P+12.0.1-2012-05-3113:54:48(UTC+1)
1637
OPUS_Baseline_corrected_2pointbaseline_Areanorm3.DS1 OPUS_Baseline_corrected_2pointbaseline_Areanorm
Observation
0,54
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1229
1153
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0,02
-0,02
0,00
Var ID (Primary)
Figure 25. IR spectra for bacteria (red) and vesicle (black). The number
represent wavelength (cm-1) of the peak. Samples are in Tris buffer.
Moreover, a comparison between the different vesicles can be seen in Figure 26-28.
Due to lack of replicates when analyzing vesicles by IR spectroscopy, the analysis of
the data will have a higher degree of uncertainty.
In the Scores plot, Figure 26, there can be seen a clear distinction between ∆waaG,
∆waaP and ∆waaL. Moreover, WT, ∆waaF and ∆galE seem to be quite similar and the
multivariate analysis is not able to distinguish between them. However, there is a
clear difference between the group of WT, ∆waaF and ∆galE compared to the other
three vesicle sample.
21
If the Scores plot in Figure 26 is correlated to the Loadings plot in Figure 27, it can be
seen that the peaks in the negative area of the loadings plot corresponds to the vesicle
samples in the negative area of the Scores plot, i.e. to WT, ∆waaF, ∆waaG and ∆galE.
And in the same way do the peaks in the positive area of the Loadings plot
correspond to the vesicle sample in the positive area of the Scores plot, i.e. to ∆waaL
and ∆waaP. And if then looking at the Loadings plot and correlate it to the IR spectra
in Figure 28, there can be seen that the negative area, in the Loadings plot,
correspond to the area marked red in the IR spectra and vice versa for the area
marked black.
If we correlate the Scores plot to the IR spectra, it can be seen that the area marked
black in the IR spectra, is the peaks where ∆waaL and ∆waaP differs from ∆waaF,
∆galE, ∆waaG and WT. This result indicates that vesicles from different mutants
differ in chemical composition. And that the difference noticeable is most likely due
to different sugar components, which also is logical since vesicles derivates from the
outer membrane of bacteria with differences in sugar content, as can be seen in table
3.
OPUS_Baseline_corrected_2pointbaseline_Areanorm1.M2 (PCA -X), Vesicle comparision
t[Comp. 1]/t[Comp. 2]
Colored according to classes in M2
1
2
3
4
5
6
50
40
30
WP_ve_TB_b
20
10
t[2]
WG_ve_TB_b
0
WL_ve_TB_b
WT_ve_TB_b
gE_ve_TB_b
WF_ve_TB_b
-10
-20
-30
-40
-50
-60
-50
-40
-30
-20
-10
0
10
20
30
40
50
60
t[1]
R2X[1] = 0,427427
R2X[2] = 0,370143
Ellipse: Hotelling T2 (0,95)
Figure 26. The PCA Scores plot, vesicle comparison. Samples are in Tris
buffer.The different vesicles spectra are as followed; ∆galE (pink), ∆waaF
(black), ∆waaG (red), ∆waaL (blue), ∆waaP (green) och WT (orange). Numbers
of components = 2, Number of observations = 6, Number of classes = 6, R2X =
0,798, Q2 = 0,343.
22
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p(corr)[1]
OPUS_Baseline_corrected_2pointbaseline_Areanorm3.M2 (PCA-X), Vesicle comparision
p(corr)[Comp. 1]
0,9
0,8
1792
1657
-0,9
0,55
1477
0,7
0,6
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R2X[1] = 0 427427
Var ID (Primary)
Figure 27. Loadings plot, vesicle comparison. Black represents the vesicle on the
positive side of the Scores plot and vice versa. Numbers indicates wavelength
(cm-1) of the peaks in Tris buffer.
OPUS_Baseline_corrected_2pointbaseline_Areanorm3.DS1 OPUS_Baseline_corrected_2pointbaseline_Areanorm
Observation
WF_ve_TB_br1_2404_BC_0
WG_ve_TB_br1_1505_BC_0
WL_ve_TB_br1_0705_BC_0
WP_ve_TB_br1_1105_BC_0
WT_ve_TB_br1_1005_BC_0
gE_ve_TB_br1_1105_BC_0
1639
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0,30
0,25
0,20
0,15
0,10
1026
0,05
1103
1078
0,00
Var ID (Primary)
SIMCAP+1201 2012053114:28:37(UTC+1)
Figure 28. IR spectra correlated to the Loadings plot. Black represents the
vesicle on the positive side of the Scores plot and vice versa. Numbers represent
wavelength (cm-1) of the peaks. Samples are in Tris buffer.
Other data
Some other data were provided for comparison from other scientist in the research
group.
23
SDS-PAGE
A SDS-PAGE gel of vesicles from different bacteria mutants, see Figure 29, showed
the most protein content in ΔwaaG (mainly at 45 kD, 30 kD and 20 kD), followed by
ΔwaaF, ΔwaaP and galE (at 45 kD and 30 kD). There seem to be some proteins in
ΔwaaL (at 45 kD) as well, although for WT the bands are too weak to tell if anything
is there. The lack of bands in the gel for WT indicates that those vesicles have a lesser
amount of proteins. What kind of proteins the different mutants release with their
vesicles is still unknown although, there is no indication that they are solution
proteins from cell leakage. If the proteins would have been from cell leakage there
would have been a high amount of bands all over the gel. Moreover, it seems like
most mutants have the same proteins i.e. the bands in the gel are at the same
position, an indication of similar molecular weight.
If this protein content is compared with the size of vesicles as can be seen in Figures
10 and 15b, there could be some indication of a correlation due to that the vesicles
with the lowest amounts of proteins also are the once with the smallest
hydrodynamic size. However, the data points are too few to make any clear
conclusions or to make any correlations to the DLS or the IR spectroscopic data.
12% SDS‐PAGE of E.coli
k
10
5
84
50
36
28
LadderWTΔwaaLΔwaaPΔgalEΔwaaGΔwaaF
Figure 29. SDS-Page of E.coli vesicles.
X-ray photoelectron spectroscopy, XPS
If looking at the XPS data for the different bacteria mutants there can be seen that
most bacteria have a high content of peptides and less of sugar and lipids. With one
exception for ΔwaaE, were the lipid content is slightly higher than the peptide
content. Why is unknown, however a possible explanation could be that the length
and composition of the LPS chain is affecting the protein content.
Furthermore, if comparing the vesicle sample with the bacterium sample of the same
mutant, ∆waaL, there can be seen that the vesicle have a higher contents of lipids
than bacterium and a low contents of both peptides and sugar. These data support
the data extracted from the IR spectroscopic analyses regarding the low peptide
contents in vesicles and the visible variation in sugar contents of vesicles.
24
Table 3. Multianalysis of C 1s XPS spectra, table shows the % contents of total
carbon in the sample,
Sample
WT
ΔwaaC
ΔwaaE
ΔwaaF
ΔwaaG
ΔwaaP
ΔgalE
ΔwaaL
ΔwaaL/fhlD
ves ΔwaaL
Peptide
53
58
39
69
67
74
69
75
62
18
Lipid
27
32
49
19
18
10
11
7
18
63
Sugar
20
10
12
13
15
16
20
18
20
19
25
Conclusions
For the DLS data there could be seen some very interesting trends regarding the
factors affecting vesicles and the production of them. First of all, the results indicate
that the composition of the LPS chain of bacteria is an important factor affecting the
zeta potential. Furthermore, this study also indicates that there is no direct
correlation between bacterial zeta potential and the vesicle zeta potential. However,
there is indication that the growth phase of the bacteria is a factor that affects both
the zeta potential and the hydrodynamic size of vesicles. Making them smaller and
more negative the later in the growth they are collected. Vesicles are also affected by
ageing, getting smaller and slightly more negative by time. However, since there is an
uncertainty regarding the aggregation of the sample during DLS analyses all
conclusions have a high degree of uncertainty and should be treated thereafter.
Further analyses are needed to verify the results in this study.
From the IR spectroscopic results a clear difference between vesicles and bacteria
could be seen indicating a difference in chemical composition. However, the IR
spectroscopic analyses have a higher degree of uncertainty than the DLS data due to a
smaller amount of data points. Moreover, even though the intentions were to
subtract the buffer, the results indicate an incomplete subtraction. So to gain more
information regarding the components a complete subtraction is needed in the
future. Analyzing the spectra in combination with XPS data, the differences is
suggested to be from different amount of peptides and lipids, where a lower peptide
contents in vesicle reveals the sugar peaks in the spectra. The main component in
vesicle seems to be lipids although those peaks are still mostly overshadowed by
peaks corresponding to water. Sugar peaks seem to differ indicating that the sugar
components varies among the vesicles. What kind of differences there are and how
big the differences are, is still unknown and further research is needed.
To sum up, there does not seem to be any direct correlation between the zeta
potential of vesicles and bacteria indicating that vesicles cannot be used as a model
for the bacterial surface. However, there are interesting trends emerging from these
results, which need to be further investigated. There are also a lot of questions arising
from this pilot study. For example, what exactly does happen to vesicles after they are
released? What are the factors that control the contents of the vesicles? If the surface
characteristics are independent from the bacteria they originate from, what other
factors affect the surface?
These questions and more will hopefully be answered in future research.
Acknowledgement
A huge thank to my supervisor Madeleine Ramstedt for the help and support during
the project. Always positive and encouraging, making it really nice working with you.
Bringing out the best of each and everyone. You also manage something no one else
has, to make me interested in research. Maybe I come back some day to find out why
these “lovely” vesicles are acting like they do.
And Olena Rzhepishevska (the red haired whirlwind/yrväder), what would I have
done without you? Probably nothing, no samples – no work… András Gorzsás, thanks
for making me understand multivariate analysis and for helping me when the IR
spectrometer misbehaved.
Peter Kallioniemi, you know I couldn’t have done this without you. Thanks for
helping me understand the world of the living organisms and the world of English
grammar.
26
27
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