genomewide characterization of host-pathogen interactions by ...

infection rate
cfu / 10 mg tissue
cfu / 10 mg tissue
Maren Depke
Results
KIDNEY GENE EXPRESSION PATTERN IN AN
IN VIVO INFECTION MODEL
Infection rate in kidney samples
The two Staphylococcus aureus strains RN1HG and RN1HG ΔsigB were used to infect mice
with an almost equal infection dose for both strains. It was known from other genetic
backgrounds that virulence and bacterial load are often similar for sigB deletion mutants and
their parental strain. Nevertheless, individual mice might still display differences. Therefore, the
infection rate of each sample was tested on molecular basis to identify potentially existing outlier
samples of divergent bacterial load.
Infection rates of individual mice kidney samples were comparable in range, mean, and
median in the biological replicates (BR) samples as well used as in for samples 10 arrays_Exp of infection Feb 09with the two different
strains (Fig. R.2.1). The infection rate of samples and infected exp. Apr 2009 with S. aureus RN1HG ranged from
4.3E+05 cfu/10 mg tissue to 2.2E+06 cfu/10 mg tissue (mean: 1.1E+06; median: 9.2E+05) and of
those infected with RN1HG ΔsigB from 4.7E+05 cfu/10 mg tissue to 2.3E+06 cfu/10 mg tissue
Infection rate cfu / 10 mg tissue
(mean: 1.0E+06; median: 8.6E+05) in both biological [experiments replicates. february and april 2009]
3.0E+06 3.010 6
Fig. R.2.1:
Infection rate in kidney samples of
mice infected with S. aureus RN1HG
and RN1HG ΔsigB.
The infection rate of each sample was
determined in a qPCR approach in
comparison to data from a mixture of
non-infected kidney tissue and in vitro
cultivated staphylococcal cells. The
line indicates the mean of infection
rate for each biological replicate.
BR – biological replicate.
2.0E+06 2.010 6
1.0E+06 1.010 6
infection with
RN1HG
first biological
replicate BR1
wt (2 kidneys)_Feb09
NMRI infection infected with with infection RN1HGwith
RN1HG ΔsigB RN1HG
first biological second biological
replicate BR1 replicate BR2
delta sigB (2 kidneys)_Feb09
wt (2 kidneys)_Apr09
infection with
RN1HG ΔsigB
second biological
replicate BR2
delta sigB (2 kidneys)_Feb09
Reproducibility of replicates and clustering of Exp. treatment Feb. 09; group members Exp. Apr. 09;
used for array analysis (4/7; 5/8) used for array analysis (5/8; 5/8)
Principal Component Analysis (PCA) was applied for a first general impression of the array
data set. This method calculates the direction of strongest variation from the multidimensional
array data set, and reduces it to a new value of the parameter called Principle Component (PC).
The remaining variation in the data set is subsequently addressed in the same way until all or a
pre-defined fraction of variation is collapsed into new values. This procedure results in a set of
PCs, each of which accounts for a fraction of the total variance in the data set. Usually, the first 2
or 3 PCs are displayed in a 2- or 3- dimensional plot, respectively. In such a plot, the distance of
the points that represent the individual data sets correlates to the difference between them.
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