genomewide characterization of host-pathogen interactions by ...

Maren Depke
Results
Kidney Gene Expression Pattern in an in vivo Infection Model
Metabolic effects of infection in kidney tissue
In an approach to recognize changes in gene expression of metabolic enzymes after infection
lists of differentially regulated genes were subjected to a BIOCYC “omics-Viewer” analysis (BIOCYC,
SRI International, CA, USA, http://biocyc.org/expression.html). This tool allows the display of
gene expression data on highly abstracted metabolic pathway schemes and therefore an intuitive
comprehension of processes in the selected experimental setup. Two different lists were
included in the analysis: a) genes significantly different between infection and sham infection
with a minimal absolute fold change of 2 in at least one of the two comparisons “infection with
RN1HG vs. sham infection” and “infection with RN1HG ΔsigB vs. sham infection” and b) genes
significantly different between infection and sham infection with a minimal absolute fold change
of 1.5 in at least one of the two comparisons described before.
When focusing on regulation that exceeds a factor of 2 in at least one comparison, regulation
was discernible for certain groups of metabolic pathways (Fig. R.2.13 A). After infection, a
repression of genes relevant for cholesterol biosynthesis was visible, and also pathways of amino
acid degradation contained repressed genes. Induction of gene expression was detected for
steroid hormone biosynthesis and for purine degradation. This is surprising, as induction was also
visible for steps of the purine/pyrimidine biosynthesis. Similarly, an increase in gene expression
was measured for certain amino acid biosynthesis steps.
Including differentially expressed genes with lower absolute fold change values (1.5 in at least
one of two comparisons) supplemented the data set with many repressed and only few induced
genes, which is visualized by the over-representation of yellow colored reaction steps
(Fig. R.2.13 B). In this view, repression in further steps in the group of lipid biosynthesis and
amino acid degradation was visible. Additional repression occurred in aerobic respiration, TCA
cycle, glycolysis, but also in gluconeogenesis and glycogen biosynthesis. The pattern of increased
genes dominating both purine degradation as well as biosynthesis was maintained after inclusion
of regulation with lower absolute fold change values.
As the gene expression analysis was performed for tissue samples, i. e. of a mixture of
different kidney tissue cell types and in case of infection additionally of a mixture of different
immune cell types invading the inflamed tissue, the resulting pattern is difficult to match for
particular cell types. While one cell type might induce purine degradation e. g. for using it as
catabolic substrate, another cell type might induce purine biosynthesis e. g. as basis for DNA
replication during proliferation. Such situation could lead to contradictory results concerning
metabolic pathways if the number of cells influencing the pattern in a certain direction is high
enough.
The general gene expression pattern showed a metabolic disturbance in several pathways
that cannot be distinguished into anabolic or catabolic shift because it contained reduction of
several catabolic pathways like glycolysis, amino acid degradation, aerobic respiration, and TCA
cycle, but also reduction in anabolic reactions like gluconeogenesis or amino acid biosynthesis.
The reason of this unclear or mixed reaction might be found in the high extent of infection and
illness. Possibly the infection has caused such a strong damage to the kidney after 5 days that
also organ function, nutrition supply and possibly oxygen availability are impaired.
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