genomewide characterization of host-pathogen interactions by ...
genomewide characterization of host-pathogen interactions by ... genomewide characterization of host-pathogen interactions by ...
Maren Depke Results Kidney Gene Expression Pattern in an in vivo Infection Model A co-factor biosynthesis cholesterol, triacylglycerol, phospholipid biosynthesis aerobic respiration amino acid degradation amino acid biosynthesis other degradation pathways purine/ pyrimidine biosynthesis sugar degradation gluconeogenesis steroid and glycogen hormone biosynthesis biosynthesis glycolysis prostaglandin biosynthesis TCA purine degradation fatty acid oxidation, triacylglycerol, phospholipid degradation B co-factor biosynthesis cholesterol, triacylglycerol, phospholipid biosynthesis aerobic respiration amino acid degradation amino acid biosynthesis other degradation pathways purine/ pyrimidine biosynthesis sugar degradation gluconeogenesis steroid and glycogen hormone biosynthesis biosynthesis glycolysis prostaglandin biosynthesis TCA purine degradation fatty acid oxidation, triacylglycerol, phospholipid degradation Fig. R.2.13: The influence of infection on gene expression in murine metabolic pathways (modified from omics-viewer(s) of BIOCYC, SRI International, CA, USA, http://biocyc.org/expression.html). Nodes represent metabolites and lines indicate reactions. The metabolic reactions are colored according to the enzymes’ gene expression regulation in the comparison of infection vs. sham infection. Red marks an increase and yellow a decrease in infected tissue. The display is limited to genes whose absolute fold change exceeds 2 in at least one of the two comparisons “infection with RN1HG vs. sham infection” and “infection with RN1HG ΔsigB vs. sham infection” (A) or to those whose absolute fold change exceeds 1.5 in at least one of the two comparisons mentioned before (B). 90
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. 91
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Maren Depke<br />
Results<br />
Kidney Gene Expression Pattern in an in vivo Infection Model<br />
A<br />
co-factor biosynthesis<br />
cholesterol,<br />
triacylglycerol,<br />
phospholipid<br />
biosynthesis<br />
aerobic<br />
respiration<br />
amino acid degradation<br />
amino acid<br />
biosynthesis<br />
other<br />
degradation<br />
pathways<br />
purine/<br />
pyrimidine<br />
biosynthesis<br />
sugar<br />
degradation<br />
gluconeogenesis steroid<br />
and glycogen hormone<br />
biosynthesis biosynthesis<br />
glycolysis<br />
prostaglandin<br />
biosynthesis<br />
TCA<br />
purine<br />
degradation<br />
fatty acid oxidation,<br />
triacylglycerol,<br />
phospholipid<br />
degradation<br />
B<br />
co-factor biosynthesis<br />
cholesterol,<br />
triacylglycerol,<br />
phospholipid<br />
biosynthesis<br />
aerobic<br />
respiration<br />
amino acid degradation<br />
amino acid<br />
biosynthesis<br />
other<br />
degradation<br />
pathways<br />
purine/<br />
pyrimidine<br />
biosynthesis<br />
sugar<br />
degradation<br />
gluconeogenesis steroid<br />
and glycogen hormone<br />
biosynthesis biosynthesis<br />
glycolysis<br />
prostaglandin<br />
biosynthesis<br />
TCA<br />
purine<br />
degradation<br />
fatty acid oxidation,<br />
triacylglycerol,<br />
phospholipid<br />
degradation<br />
Fig. R.2.13: The influence <strong>of</strong> infection on gene expression in murine metabolic pathways (modified from omics-viewer(s) <strong>of</strong> BIOCYC,<br />
SRI International, CA, USA, http://biocyc.org/expression.html).<br />
Nodes represent metabolites and lines indicate reactions. The metabolic reactions are colored according to the enzymes’ gene<br />
expression regulation in the comparison <strong>of</strong> infection vs. sham infection. Red marks an increase and yellow a decrease in infected<br />
tissue. The display is limited to genes whose absolute fold change exceeds 2 in at least one <strong>of</strong> the two comparisons “infection with<br />
RN1HG vs. sham infection” and “infection with RN1HG ΔsigB vs. sham infection” (A) or to those whose absolute fold change exceeds<br />
1.5 in at least one <strong>of</strong> the two comparisons mentioned before (B).<br />
90
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