Curcumin is the active ingredient present in the roots and rhizomes of the tropical plant known as Curcuma Longa (Turmeric). Scientific research has identified curcumin to possess various pharmacological properties including, anti-inflammatory and anticancer activities. This novel research focused on investigating the advantageous properties of curcumin in combination with current therapeutic options for the treatment of conditions of an inflammatory nature such as rheumatoid arthritis (RA) and cancer, specifically, non-small cell lung cancer (NSCLC) and glioblastoma multiforme (GBM). Previous microarray results from our laboratory was able to identify a set of 74 genes which were differentially expressed in untreated human fibroblast-like synoviocytes isolated from RA patients (HFLS-RA), methotrexate (MTX) and curcumin treated cells, individually and in combination. From this list, 13 genes were selected for further qRT-PCR analysis due to the established role they play in the process of inflammation. The change in expression of these 13 candidate genes were monitored in order to investigate the efficacy of curcumin as a therapeutic agent whilst identifying potential therapeutic biomarkers for the treatment of these inflammatory conditions. HFLS-RA cells were treated with curcumin and MTX, individually and in combination. The novel finding from this study highlighted that individual treatment with curcumin was the most effective since it lowered the expression of 11 out of the 13 candidate genes. The levels of CD248, HSPA6, MMP1, MMP13 and TNFSF10 were sharply downregulated across all treatment conditions, allowing the identification of potential therapeutic targets for intervention in RA. As normal synoviocytes (HFLS) were also studied alongside HFLS-RA cells, it was possible to also identify potential diagnostic biomarkers for RA. The expression of CD248, HSPA6, MMP1, MMP13 and TNFSF10 was significantly higher (p < 0.001) in HFLS-RA cells compared to normal synoviocytes (HFLS). This highlighted the use of these genes as potential diagnostic biomarkers as they were suggested to play a role in the pathogenesis of RA. A549 lung carcinoma cells were treated with curcumin and cisplatin, individually and in combination. Curcumin treatment alone was the most effective and downregulated the expression of 11 inflammatory response genes studied, ANGPTL7, CD248, CH25H, COL14A1, CXCL12, HSPA6, IFITM1, IL-7, MMP1, MMP13 and TNFSF10. Cisplatin was only able to decrease the expression of 7 genes, however, in combination with curcumin, a total of 9 genes were downregulated. Curcumin was able to work in synergy with cisplatin in order to potentiate the effects of this cytotoxic agent and help to overcome cisplatin-resistance mechanisms. U87-MG glioblastoma cells were treated with curcumin and TMZ, individually and in combination. Curcumin lowered the expression of 10 candidate genes compared to TMZ which downregulated 8. Combination treatment was the most effective as it decreased the expression of ANGPTL7, CD248, CH25H, COL14A1, CXCL12, CYTL1, IFITM1, IL-6, IL-7, MMP1 and TNFSF10. The use of curcumin in combination with TMZ sensitises glioma cells to the activity of TMZ, thereby increasing the therapeutic potential of the drug. Curcumin was able to specifically target mediators involved in the inflammatory pathway, and so the use of this natural compound for RA, NSCLC and GBM can improve the efficacy of current therapeutic options, limit undesirable side effects and help to overcome resistance mechanisms deployed by the cells.

This thesis describes the development of a novel approach for prediction of the three-dimensional structure of transmembrane regions of membrane proteins directly from amino acid sequence and basic transmembrane region topology. The development rationale employed involved a knowledge-based approach. Based on determined membrane protein structures, 20x20 association matrices were generated to summarise the distance associations between amino acid side chains on different alpha helical transmembrane regions of membrane proteins. Using these association matrices, combined with a knowledge-based scale for propensity for residue orientation in transmembrane segments (kPROT) (Pilpel et al., 1999), the software predicts the optimal orientations and associations of transmembrane regions and generates a 3D structural model of a gi ven membrane protein, based on the amino acid sequence composition of its transmembrane regions. During the development, several structural and biostatistical analyses of determined membrane protein structures were undertaken with the aim of ensuring a consistent and reliable association matrix upon which to base the predictions. Evaluation of the model structures obtained for the protein sequences of a dataset of 17 membrane proteins of detennined structure based on cross-validated leave-one-out testing revealed generally high accuracy of prediction, with over 80% of associations between transmembrane regions being correctly predicted. These results provide a promising basis for future development and refinement of the algorithm, and to this end, work is underway using evolutionary computing approaches. As it stands, the approach gives scope for significant immediate benefit to researchers as a valuable starting point in the prediction of structure for membrane proteins of hitherto unknown structure.