Doherty Institute - Theses
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ItemIntegrative genomics to understand immune function and regulation( 2017)Characterising the mechanistic principles underlying immune function and regulation will help us understand how the immune system provides effective host defence. However, the highly complex and multi-level nature of the immune system requires a systems-level analysis to gain multi-dimensional insight and unravel its complexity. High-throughput profiling technologies allow quantitative measurement of various immunological parameters that capture system-wide information. This has led to the generation of large-scale multi-omics datasets from human populations, experimental set-ups, and a compendium of immune cell types. Developments in bioinformatics offer integrative approaches to explore the functional and regulatory relationships within and between various organisational levels of the immune system as well as across other biological systems. For this thesis, multi-omic analysis was used to characterise immune processes in terms of genetics, transcriptional networks and interactions with metabolism. First, I mapped the genetics and interactions of immune gene networks with circulating metabolites in in a population-based study. I integrated blood transcriptomic, metabolomic, and genomic profiles from two population-based cohorts, including a subset with 7-year follow-up sampling. I identified topologically robust gene networks enriched for immune functions including cytotoxicity, viral response, B cell, platelet, neutrophil, and mast cell/basophil activity. These immune gene modules showed complex patterns of association with 158 circulating metabolites, including lipoprotein subclasses, lipids, fatty acids, amino acids, and CRP. Genome-wide scans for module expression quantitative trait loci (mQTLs) revealed five modules with mQTLs with both cis and trans effects. Then, I explored the underlying shared genetic architecture between correlated cytokines, the regulatory agents of the immune system. Multivariate genome-wide association scan was performed to identify genetic variants regulating circulating cytokines in ~9,000 individuals from three independent population studies. Eight loci were identified as regulating this network, including two previously undetected loci. Expression quantitative loci (eQTL) analysis revealed that these loci harbour eQTLs. Further linking these loci with genetic variants associated with disease risk provided insight into the possible inflammatory pathways underlying these common diseases. Thirdly, I explored a particular component of the immune system, immunological memory; with emphasis on tissue resident memory T-cells (TRM cells). I employed a network-based approach to identify a transcriptional sub-network related to the residency of murine TRM cells isolated from various tissues. Comparative analysis further revealed that expression profiles of tissue resident immune cells from different lineages share transcriptional similarity. Finally, the role of TGF-β, an extrinsic tissue-derived factor in influencing the transcriptional signature of TRM cells was investigated. I compared the global transcription of T-cells induced in vitro by TGF-β with the residency-related transcription signature previously established in TRM cells. This demonstrated that the transcriptional signature of TRM cells is largely driven by TGF-β. Findings from this thesis demonstrate the power of integrative bioinformatics analyses to gain novel insights into the immune system, which can assist in predicting its response to perturbations. It also may help explain how inter-individual variability in immune function contributes to differential disease susceptibility and treatment outcomes. This thesis offers a general framework to systematically integrate and analyse multi-omics data to answer important biological questions.