genomewide characterization of host-pathogen interactions by ...

Maren Depke
Results
GENE EXPRESSION PATTERN OF BONE-MARROW DERIVED
MACROPHAGES AFTER INTERFERON-GAMMA TREATMENT
Experimental application of serum-free differentiation and cultivation of mouse BMM
Bone-marrow derived macrophages (BMM) allow experimental analysis of macrophage
reactions and functions without the impact of immunological conditioning that affects already
differentiated macrophages for example from spleen or peritoneum. The standardization of
experiments was markedly improved in 2009 when Eske et al. published a serum-free culture
system for BMM that allows conduction of studies independently from the influence of
undefined immune-active substances and possible variation introduced to experiments by using
fetal calf serum (FCS) as supplement. BMM cultivated in this new serum-free medium maintain
the already known characteristics of macrophages and have proven to lead to highly reproducible
results (Eske et al. 2009).
The experimental setup in this study included the two mouse strains BALB/c and C57BL/6.
After in vitro differentiation, BMM of each strain were divided in two treatment groups: IFN-γ
treated BMM and non-treated medium control BMM.
The approach of transcriptome analysis using the Affymetrix GeneChip Mouse Gene 1.0 ST
allowed monitoring of 35557 probe sets, of which 6613 belong to the group of controls. The
remaining 28944 probe sets represent 20074 EntrezGene records in the annotations of Rosetta
Resolver software (annotation of 06/2009). The LC-MS/MS proteome analysis provided
information on 946 reliably identified proteins (Dinh Hoang Dang Khoa).
High reproducibility of gene expression in experiments using serum-free differentiation and
cultivation of mouse BMM
For a first general impression of the array data set, the method of Principal Component
Analysis (PCA) was applied. 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, of which each 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 coordinate system, respectively.
In such a plot, the distance of the points that represent the individual data sets correlates to the
difference between them.
In this study, the PCA plot was derived from log-transformed intensity data of 12 arrays,
analyzing 3 biological replicates of 2 strains and 2 treatment groups (Fig. R.3.1). The PCA clearly
depicted the high reproducibility of biological replicates obtained from the serum-free model of
BMM differentiation, cultivation and IFN-γ treatment: The 3 biological replicates of each group
were arranged together, while the BMM of different strains and treatments were separated.
Differences on the axis for PC 1 were by a factor of approximately 250 smaller than differences
on the axes for PCs 2 and 3. That implied that PCs 2 and 3 covered the main part of variance in
the data set. This led to the conclusion that the data set was mainly influenced by 2 factors. In an
experimental design of the 2 factors strain and treatment together with the observed grouping of
biological replicates obviously the experimental factors were responsible for the observed
variation.
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