Pathology - Theses
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ItemApplication of bayesian networks to a longitudinal asthma study( 2016)Asthma is a highly prevalent and often serious condition causing significant illness and sometimes death. It typically consumes between 1-3% of the medical budget in most countries and imposes a disease burden on society comparable to schizophrenia or cirrhosis of the liver. Its causes are as yet unknown but a significant number of risk factors, covering such diverse factors as viral infections during infancy, blood antibody titres, mode of birth and number of siblings have been identified. In recent years there has been increasing recognition of the role played by the microbiome in human health, with a growing understanding that our relationship with the microbes that colonise the different parts of the human body is symbiotic. Disruptions in the microbiome have been implicated in diseases such as obesity, autism and auto-immune diseases, as well as asthma. At the same time there has been an increasing awareness in asthma research that its multi-faceted and multi-factorial nature requires more sophistication than statistical association and regression. In this spirit we employ Bayesian networks, whose properties render them suitable for representing time-direct or even causal relationships, to gain insight into the nature of asthma. We begin with an example of the simplest Bayesian networks, a linear classifier, with which we predict outcomes in the fifth year-of-life according to the statistical distribution of variables from the first two years-of-life. (The qualification linear refers to the neglect of correlation and interaction among the predictive variables.)While classifiers have long been used for prognosis and diagnosis, we use them to identify useful asthma subtypes, called endotypes. Different endotypes often require different treatments and management programs, and driven by different biological factors. These different factors provide different predictors, and a predictor which separates one endotype from the healthy may not do so for a different endotype. We use this to mathematically construct an indicator of when a given predictor is exclusively predictive of a given endotype. Our so-called “exclusivity index” is quantitatively precise, unlike a significance threshold. The Cohort Asthma Study, whose longitudinal data we analyse, includes the relative abundances of genera present in the nasopharyngeal microbiome. In an apparent diversion, we use qq-plots to indicate relationships between the infant microbiome and fifth-year wheeze- and atopy- status. Interestingly, the relative abundance of Streptococcus under certain circumstances was found to be highly predictive of one of the endotypes we identified in the preceding chapter. Finally, we address the problem of mapping out the complicated interactions among multiple variables. Our model is an adaption of a package originally designed for inferring gene-interaction networks, called ARTIVA. This was a non-trivial matter requiring us to augment the discrete data values in order to make them compatible with the underlying mathematics of ARTIVA’s algorithm. With questions from the asthma literature and the posterior probabilities output by ARTIVA, we were guided to networks of the interactions between atopy, wheeze and infection, and could see the difference in the development of immunity-related variables between those who went on to exhibit wheeze in the fifth year-of-life and those who did not. Our model yielded networks indicating that sensitivity to viral infection is an effect and not a cause of atop and wheeze.
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ItemInvestigating the mechanistic link between neuroinflammation and biometal homeostasis in neurodegenerative diseases( 2016)Neuroinflammation and biometal dyshomeostasis are two pathogenic features underlying a number of neurodegenerative diseases, however the mechanistic link between these two pathways has yet to be delineated. This study examined the hypothesis that impaired biometal homeostasis is associated with neuroinflammatory changes. To test this hypothesis I aimed to investigate the effects of key biometals on inflammatory processes in cultured microglia, and in turn, investigate how inflammatory activation of microglia affects homeostasis of biometals. These relationships were further examined in vivo to determine the effects of the type 1 interferon (IFN) pathway on biometal homeostasis in the CNS. In my in vitro study, primary murine microglial cultures were treated for 24h with maximal sub-toxic doses of biometals, delivered as ferric ammonium chloride (FAC), ZnCl2 and CuCl2 and the biometal chelators, diamsar or N,N,N_,N_-Tetrakis(2-pyridylmethyl)ethylenediamine (TPEN) with and without concurrent interferon-_ (IFN_) and tumour necrosis factor-_ (TNF_) stimulation. Non-stimulated and IFN_/TNF_ stimulated microglia served as negative and positive controls for inflammatory activated microglia, respectively. I measured the levels of a number of key inflammatory cytokines to assess microglial inflammatory response to biometal and biometal chelator treatments. I found that FAC and CuCl2 treatment, significantly induced Fe and Cu uptake respectively, in both non-stimulated and stimulated microglia and that all biometal treatments, significantly reduced the expression of MCP-1 in stimulated and non-stimulated microglia, indicative of an anti-inflammatory role. In contrast, FAC treatment also induced TNF_ mRNA expression in these cultures, suggesting Fe may play a dual role in neuroinflammation. In addition, to investigate how inflammatory activation of microglia affects biometal homeostasis, the gene expression of the metal-binding protein, metallothionein-1 (MT-1) and the biometal transporter, ZRT/IRT-like transporter protein (Zip7) were also measured. I also found that IFN_/TNF_ stimulation inhibited Fe-induced MT-1 and Zip7 expression in microglia. These findings demonstrate that sub-toxic levels of key biometals have multiple modulatory actions on cultured microglia, with both inhibitory and stimulatory effects on cytokines. These changes may be associated with induction or inhibition of major metal response proteins, such as MT-1 and transporters. To examine the effects of the type 1 IFN pathway on biometal homeostasis in the CNS, I performed a spatio-temporal analysis of Fe, Zn, Cu and Mn levels in the CNS of interferon _ receptor-1 (Ifnar1) knock-out (-/-) mice and wild type (WT) mice at 6 and 10 months of age using ICP-MS analysis. A subset of 6-month-old Ifnar1-/- mice was also stimulated with lipopolysaccharide (LPS) treatment for 6h to determine the effects of Ifnar1-/- on biometals homeostasis under inflammatory conditions. I found reduced Cu and Mn levels in the cerebellum of aged (10-month-old) Ifnar1-/- mice, however, expression of key Cu and Mn transporter and regulatory proteins remained unchanged. I also found no significant alterations to biometals between WT and Ifnar1-/- mice at 6-month of age, however, when mice were challenged with LPS, I found a significant decrease in Fe levels in the cerebellum and cerebrum of WT mice and a significant decrease in Zn levels in the cerebrum of Ifnar1-/- mice compared to naïve mice of their respective genotypes. A significant increase and an upward trend in transferrin receptor1 (TfR1) levels in the cerebrum of LPS-challenged and naïve Ifnar1-/- mice, respectively was also observed. These data demonstrate that the type 1 IFN pathway is involved in the regulation of CNS biometal homoeostasis. The studies provide further evidence to support a major role for biometals in neuroinflammatory pathways, with important implications for neurodegenerative disease in which brain biometal homeostasis is altered.