Statistical modelling of masked gene regulatory pathway changes across microarray studies of interferon gamma activated macrophages

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