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  • Publication
    Multi-omic data integration and comparative systems biology of bovine and human tuberculosis
    (University College Dublin. School of Agriculture and Food Science, 2021) ;
    Bovine tuberculosis (bTB), caused by infection with Mycobacterium bovis, is a major endemic disease affecting global cattle production and costs an estimated €3 billion to global agriculture annually. Its human counterpart, human tuberculosis (hTB) caused by Mycobacterium tuberculosis, which shares 99.5% genome sequence identity with M. bovis, is the leading cause of death due to a single infectious agent worldwide, killing on average one person every 20 seconds. The full extent of the host-pathogen interaction between the host alveolar macrophage and the mycobacteria during the initial stages of infection, and which genetic variants influence successful clearance is not fully understood. In this thesis, I investigate the cross-species host-pathogen interaction during mycobacterial infection using an array of integrative genomics computational workflows, as well as developing software to integrate the results with genome-wide association study (GWAS) data sets. In Chapter 2, I analysed chromatin immunoprecipitation sequencing (ChIP-seq), RNA-seq and miRNA-seq data to examine the effect of M. bovis infection on the bovine alveolar macrophage (bAM) epigenome and transcriptome. I show that H3K4me3 is more prevalent, at a genome-wide level, in chromatin from M. bovis -infected bam compared to control non-infected bAM. Identification of these genes facilitated integration of high-density GWAS data, which revealed genomic regions associated with resistance to M. bovis infection in cattle. In Chapter 3, I used differential expression and network approaches to analyse the host response to infection with M. bovis to identify core infection response pathways and gene modules. The host expression data consisted of bovine RNA-seq data from bAM infected with M. bovis at 24- and 48-hours post-infection. These RNA-seq data were analysed using three analysis pipelines: First, a standard differential expression analysis (DE). Second, two networks were constructed at each time point using gene correlation patterns to determine changes in expression across conditions. Third, a gene interaction network (GIN) of the mammalian host response to mycobacterial infection was generated using online databases. The results from these analyses were integrated with GWAS data to determine which of the three approaches identified genes significantly enriched for SNPs associated with resistance to M. bovis infection. Results indicated distinct overlap in SNP discovery, demonstrating that network-based integration of biologically relevant transcriptomics data can leverage substantial additional information from GWAS data. Finally, in Chapter 4, I compared the host transcriptional responses to both mycobacterial pathogens by analysing existing RNA-seq data extracted from four infected cell groups. These RNA-seq data were re-analysed using four computational analysis pipelines: standard differential expression of genes (DEG), differential expression interaction networks (DEN), combined pathway analysis (CPA), and Ingenuity Pathway Analysis (IPA). To identify common/distinct genomic variants significantly associated with bTB/hTB resistance, the results of the four analytical pipelines were integrated with two GWAS data sets: 1) a bTB resistance GWAS study consisting of high-density genotypes for 7,346 bulls and epidemiological data from 781,270 cattle, and 2) a hTB case-control GWAS study consisting of 2,219 infected individuals and 450,045 non-infected controls. Using these analysis methods, I prioritised 12 cattle genes containing/proximity to 224 SNPs significantly associated with bTB disease resistance and identified 20 human genes containing/proximity to 106 SNPs significantly associated with hTB disease resilience. Analysis of these 32 human and bovine gene loci revealed that SNPs with disease resistance are located within genes that are core to granuloma formation, the NF-¿B signalling pathway and cytokine receptor interactions.