Macrophages are present in virtually all tissues and account for approximately 10% of all body mass. Although classically credited as the scavenger cells of innate immune system, ridding a host of pathogenic material and cellular debris though their phagocytic function, macrophages also play a crucial role in embryogenesis, homeostasis, and inflammation. De-regulation of macrophage function is therefore implicated in the progression of many disease states including cancer, arthritis, and atherosclerosis to name just a few. The diverse range of activities of this cell can be attributed to its exceptional phenotypic plasticity i.e. it is capable of adapting its physiology depending on its environment; for instance in response to different types of pathogens, or specific cocktail of cytokines detected. This plasticity is exemplified by the macrophages capacity to adjust rapidly its transcriptional profile in response to a given stimulus. This includes interferons which are a group of cytokines capable of activating the macrophage by interacting with their cognate receptors on the cell. The different classes of interferons activate downstream signalling cascades, eventually leading to the expression (as well as repression) of hundreds of genes. To begin to fully understand the properties of a dynamic cell such as the macrophage arguably requires a holistic appreciation of its constituents and their interactions. Systems biology investigations aim to escape from a gene-centric view of biological systems. As such this necessitates the development of better ways to order, display, mine and analyse biological information, from our knowledge of protein interactions and the systems they form, to the output of high throughput technologies. The primary objectives of this research were to further characterise the signalling mechanisms driving macrophages activation, especially in response to type-I and type- II interferons, as well as lipopolysaccharide (LPS), using a ‘systems-level’ approach to data analysis and modelling. In order to achieve this end I have explored and developed methods for the executing a ‘systems-level’ analysis. Specifically the questions addressed included: (a) How does one begin to formalise and model the existing knowledge of signalling pathways in the macrophage? (b) What are the similarities and differences between the macrophage response to different types of interferon (namely interferon-β (IFN-β) and interferon-γ (IFN-γ))? (c) How is the macrophage transcriptome affected by siRNA targeting of key regulators of the interferon pathway? (d) To what extent does a model of macrophage signalling aid interpretation of the data generated from functional genomics screens? There is general agreement amongst biologists about the need for high-quality pathway diagrams and a method to formalize the way biological pathways are depicted. In an effort to better understand the molecular networks that underpin macrophage activation an in-silico model or ‘map’ of relevant pathways was constructed by extracting information from published literature describing the interactions of individual constituents of this cell and the processes they modulate (Chapter-2). During its construction process many challenges of converting pathway knowledge into computationally-tractable yet ‘understandable’ diagrams, were to be addressed. The final model comprised 2,170 components connected by 2,553 edges, and is to date the most comprehensive formalised model of macrophage signalling. Nevertheless this still represents just a modest body of knowledge on the cell. Related to the pathway modelling efforts was the need for standardising the graphical depiction of biology in order to achieve these ends. The methods for implementing this and agreeing a ‘standard’ has been the subject of some debate. Described herein (in Chapter-3) is the development of one graphical notation system for biology the modified Edinburgh Pathway Notation (mEPN). By constructing the model of macrophage signalling it has been possible to test and extensively refine the original notation into an intuitive, yet flexible scheme capable of describing a range of biological concepts. The hope is that the mEPN development work will contribute to the on-going community effort to develop and agree a standard for depicting pathways and the published version will provide a coherent guide to those planning to construct pathway diagrams of their biological systems of interest. With a desire to better understand the transcriptional response of primary mouse macrophages to interferon stimulation, genome wide expression profiling was performed and an explorative-network based method applied for analysing the data generated (Chapter-4). Although transcriptomics data pertaining to interferon stimulation of macrophages is not entirely novel, the network based analysis of it provided an alternative approach to visualise, mine and interpret the output. The analysis revealed overlap in the transcriptional targets of the two classes of interferon, as well as processes preferentially induced by either cytokine; for example MHC-Class II antigen processing and presentation by IFN-γ, and an anti-proliferative signature by IFN-β. To further investigate the contribution of individual proteins towards generating the type-I (IFN-β) response, short interfering RNA (siRNA) were employed to repress the expression of selected target genes. However in macrophages and other cells equipped with pathogen detection systems the act of siRNA trasfection can itself induce a type-I interferon response. It was therefore necessary to contend with this autocrine production of IFN-β and optimise an in vitro assay for studying the contribution of siRNA induced gene-knock downs to the interferon response (described in Chapter-5). The final assay design incorporated LPS stimulation of the macrophages, as a means of inducing IFN-β autonomously of the transfection induced type-I response. However genome-wide expression analysis indicated the targeted gene knock-downs did not perturb the LPS response in macrophages on this occasion. The optimisation process underscored the complexities of performing siRNA gene knockdown studies in primary macrophages. Furthermore a more thorough understanding of the transcriptional response of macrophages to stimulation by interferon or by LPS was required. Therefore the final investigations of this thesis (Chapter-6) explore the transcriptional changes over a 24 hour time-course of macrophage activation by IFN-β, IFN-γ, or LPS and the contribution of the macrophage pathway model in interpreting the response to the three stimuli. Taken together the work described in this thesis highlight the advances to be made from a systems-based approach to visualisation, modelling and analysis of macrophage signalling.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:563664 |
| Date | January 2011 |
| Creators | Raza, Sobia |
| Contributors | Freeman, Thomas. ; Ghazal, Peter |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/5898 |
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