Interferon gamma (IFN-γ) regulation of macrophages plays an essential role in innate immunity and
pathogenicity of viral infections by directing large and small genome-wide changes in the transcriptional
program of macrophages. Smaller changes at the transcriptional level are difficult to detect but can have
profound biological effects, motivating the hypothesis of this thesis that responses of macrophages to
immune activation by IFN-γ include small quantitative changes that are masked by noise but represent
meaningful transcriptional systems in pathways against infection. To test this hypothesis, statistical
meta-analysis of microarray studies is investigated as a tool to obtain the necessary increase in analysis
sensitivity. Three meta-analysis models (Effect size model, Rank Product model, Fisher’s sum of logs) and three
further modified versions were applied to a heterogeneous set of four microarray studies on the effect of
IFN-γ on murine macrophages. Performance assessments include recovery of known biology and are
followed by development of novel biological hypotheses through secondary analysis of meta-analysis
outcomes in context of independent biological data sources. A separate network analysis of a microarray
time course study investigate s if gene sets with coordinated time-dependent relationships overlap can
also identify subtle IFN-γ related transcriptional changes in macrophages that match those identified
through meta-analysis.
It was found that all meta-analysis models can identify biologically meaningful transcription at
enhanced sensitivity levels, with slightly improved performance advantages for a non-parametric model
(Rank Product meta-analysis). Meta-analysis yielded consistently regulated genes, hidden in individual
microarray studies, related to sterol biosynthesis (Stard3, Pgrmc1, Galnt6, Rab11a, Golga4, Lrp10),
implicated in cross-talk between type II and type I interferon or IL-10 signalling (Tbk1, Ikbke, Clic4,
Ptpre, Batf), and circadian rhythm (Csnk1e). Further network analysis confirms that meta-analysis
findings are highly concentrated in a distinct immune response cluster of co-expressed genes, and also
identifies global expression modularisation in IFN-γ treated macrophages, pointing to Trafd1 as a
central anti-correlated node topologically linked to interactions with down-regulated sterol biosynthesis
pathway members.
Outcomes from this thesis suggest that small transcriptional changes in IFN-γ activated macrophages
can be detected by enhancing sensitivity through combination of multiple microarray studies. Together
with use of bioinformatical resources, independent data sets and network analysis, further validation
assigns a potential role for low or variable transcription genes in linking type II interferon signalling to
type I and TLR signalling, as well as the sterol metabolic network