genomewide characterization of host-pathogen interactions by ...

Maren Depke
Discussion and Conclusions
2.5 h and the 6.5 h time points, but the protein abundance increased only at the 6.5 h time point.
This indicates that the first mRNA expression changes were manifested on proteome level
probably after the first analyzed time point (2.5 h) and before or at latest directly at the second
analyzed time point (6.5 h).
A time shift between detectable messenger and protein level changes can explain the
observation that approximately 5 % of protein abundance changes found their correspondence at
transcript level at the 6.5 h time point, and vice versa, the even smaller fraction of 0.8 % of
differential expression was realized on proteome level at the same time point. It has to be
mentioned that a small number of genes/proteins showed reverse effects in transcriptome and
proteome analysis, or changes in protein abundance preceeded mRNA changes. This can be
explained by post-transcriptional, translational and protein turnover mechanisms, by time shift
aspects or by the possibility of counter-regulatory processes. During proteome analysis, different
methods have to be applied to study proteins from different compartments or with different
physical characteristics. In this study, a gel-free proteomics approach was applied, which is
known to yield less information on membrane proteins because these are not accessed during
extract preparation and enzymatic digestion. Thus, a defined part of the microarray results can
practically not match proteome results for lack of accessibility. The results from the comparison
of the proteome and the transcriptome analysis approach underline the importance of
performing both analysis levels in parallel. Information of both approaches complement each
other and supply valuable data to establish a comprehensive view of the experimental system.
Although transcriptome profiling revealed the very small number of 40 differentially
expressed genes in S9 cells 2.5 h after start of infection with S. aureus RN1HG, these gene
expression changes indicated the beginning response of the host cells to infection. Furthermore,
these changes were in very good agreement with the differential gene expression at the second
analysis time point four hours later.
At the 2.5 h time point, a strong induction of a proinflammatory response was observed. This
included cytokines like IL-6 and IFN-β, genes related to inflammation mediators like PTGS2, and
genes which are linked to these aspects because they are part of signal transduction and
transcription processes. In addition to the observation of proinflammatory mechanisms, gene
expression changes which aim to counter-regulate and balance the inflammation were observed
already 2.5 h after start of infection. Nevertheless, the infection influenced the host cells very
strongly, and hints for morphological changes and a limitation of cell cycle progression became
visible.
The study included a second sampling point 6.5 h after start of infection. The cellular state at
this time point was characterized by a stronger loss of viability, but these non-viable cells were
not included in the sample preparation after FACS-sorting. Thus, a viable, GFP-positive infected
cell fraction was utilized for transcriptome profiling. Resulting differentially expressed genes
distinguished a rather strong cellular response as the number of regulated genes increased by a
factor of approximately 30 in comparison to the first time point.
In a general view, the S9 cells started a regulatory gene expression program 6.5 h after start of
infection, which was strongly associated with diverse inflammation and cell death related
functions. These included signal transduction linked genes for the interferon signaling cascade
together with IFN-β itself, which has already been induced at the 2.5 h time point, pattern
recognition receptors, antigen presentation and immunoproteasome, but also death receptor
signaling. The amplification of cellular response was indicated by the emergence of functional
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