genomewide characterization of host-pathogen interactions by ...

fold change (infected vs. control)
fold change (infected vs. control)
fold change (infected vs. control)
Maren Depke
Results
Host Cell Gene Expression Pattern in an in vitro Infection Model
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Fig. R.4.7:
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Comparison of fold change value for differentially expressed genes both after
2.5 h and after 6.5 h.
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Absolute fold change values for almost all of these genes increased at the later
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time point (black lines). PTGS2 had a smaller fold change after 6.5 h than after
2.5 h (gray dashed line) and only one gene, ADAMTS1, showed an reversed -4
direction of regulation in both time points (gray dashed/dotted line).
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2.5 h 2.5 h 2.5 h 6.5 h 6.5 h 6.5 h
In IPA, a global functional analysis can be applied to the complete data set. Results offer an
overview on the data-associated biological functions with a p-value to judge on overrepresentation
of the corresponding function in the data set. The results can be grouped in three
topics: 1) Diseases and Disorders 2) Molecular and Cellular Functions, and 3) Physiological System
Development and Function. Here, especially the first and second topic were relevant for data
analysis because the data were generated in vitro. To get a first impression, only the first five
categories with smallest p-value were selected from the topics 1) and 2) for the 6.5 h time point.
In the topic “Diseases and Disorders” a very dominant association of the data set with diverse
inflammation and infection related categories was visible (Table R.4.4). Category “Cancer”
obtained the smallest p-value, but as such category collects diverse functions that might also be
immune-system associated, the result will not be interpreted as cancer like reaction of S9 cells to
infection. Indeed, in a comparison of all genes in the list associated with “Cancer” and all genes
linked to the other four immune-related categories in this analysis, approximately one third
appeared in both “Cancer” as well as in the immune-related categories.
The top categories of “Molecular and Cellular Function” mainly dealt with cellular growth,
proliferation, death or related aspects (Table R.4.4). First signs of the influence of infection on
this issue have already been recognized in the 2.5 h samples. In the later analysis time point of
6.5 h this influence was even more manifested and resulted in very low p-values.
Table R.4.4: Global functional analysis (IPA, www.ingenuity.com) of the infection-dependent differentially regulated genes in S9 cells
6.5 h after start of infection. First five categories of the topics “Diseases and Disorders” and “Molecular and Cellular Functions” are
cited with the range of p-values of the associated sub-categories.
rank
Diseases and Disorders
Molecular and Cellular Functions
category p-value range category p-value range
1 Cancer 3.44E-09 – 1.85E-03 Cellular Growth and Proliferation 1.04E-12 – 2.11E-03
2 Infection Mechanism 3.76E-09 – 1.50E-03 Cell Death 1.27E-10 – 2.21E-03
3 Antimicrobial Response 5.46E-07 – 7.96E-04 Cellular Development 4.94E-09 – 2.06E-03
4 Inflammatory Response 5.46E-07 – 1.84E-03 Cellular Function and Maintenance 8.19E-08 – 1.95E-03
5 Immunological Disease 1.12E-06 – 1.76E-03 Cell Cycle 1.22E-07 – 2.21E-03
The Canonical Pathways tool in IPA allows analysis of the data set in the context of predefined
pathways and their associated genes, which are displayed in order of cellular location and
function and which can be overlaid with gene expression data.
The first, most significant result for the infection-dependent gene expression changes in S9
cells 6.5 h after start of infection was the pathway “Interferon Signaling” (p = 5.81E−09;
ratio = 13/30; Fig. R.4.8). Induced gene expression was documented for IFN-β, the signal
transduction molecules JAK2, STAT1, and STAT2, the regulatory suppressor of cytokine signaling
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