A hallmark of the joint disease osteoarthritis (OA) is the degradation of the articular cartilage in the affected joint, debilitating pain and decreased mobility. At present there are no disease modifying drugs for treatment of osteoarthritis. This represents a significant, unmet medical need as there is a large and increasing prevalence of OA. Using a systems biology approach, we aimed to better understand the pathogenic mechanisms of OA and ultimately aid development of therapeutics. This thesis focuses on the analysis of gene expression data from human OA cartilage obtained at total knee replacement (TKR). This transcriptomics approach gives a genome-wide overview of changes, but can be challenging to interpret. Network-based algorithms provide a framework for the fusion of knowledge so allowing effective interpretation. The PhenomeExpress algorithm was developed as part of this thesis to aid the interpretation of gene expression data. PhenomeExpress uses known disease gene associations to identify relevant dysregulated pathways in the data. PhenomeExpress was further developed into an 'app' for Cytoscape, the widely used network analysis and visualisation platform. To investigate the processes that occur during the degradation of cartilage we examined the gene expression of damaged and intact OA cartilage using RNA-Seq and identified key altered pathways with PhenomeExpress. A regulatory network driven by four transcription factors accounts for a significant proportion of the observed differential expression of damage-associated genes in the PhenomeExpress identified pathways. We further explored the role of the cytokines IL-1 and TNF that have been reported to β drive the progression of OA. Comparison of the expression response of in vitro cytokine-treated explants with the in vivo damage response revealed major differences, providing little evidence for any significant role of IL-1 and TNF as drivers of OA β damage in vivo. Finally, we examined the heterogeneity of OA through analysis of cartilage expression profiles at TKR. Through a network-based clustering method, we found two subgroups of patients on the basis of their gene expression profiles. These subgroups were found to have distinct OA expression perturbations and we identified TGF and S100A8/9 β signalling as potentially explaining the observed differential expression. We developeda RT-qPCR based classifier that allowed classification of new samples into these subgroups so allowing future assessment of the clinical significance of these subgroups. The work presented in this thesis includes a novel, widely-accessible tool for the analysis of disease gene expression data, which we used to give new insights into the pathogenesis of osteoarthritis. We have produced a rich dataset for future research and our analysis of this data has increased our understanding of cartilage damage processes and the heterogeneity of OA.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:740314 |
| Date | January 2017 |
| Creators | Soul, Jamie |
| Contributors | Boot-Handford, Raymond ; Schwartz, Jean-Marc |
| Publisher | University of Manchester |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.research.manchester.ac.uk/portal/en/theses/a-systems-biology-approach-to-knee-osteoarthritis(0b229b46-7be4-4fdb-9a14-062c3dcfcf05).html |
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