Introduction: Hypoxia is found in solid cancerous tumours. The presence of hypoxia within tumours inhibits anti-cancer treatment strategies such as chemotherapy from being completely effective and it is suspected that multiple mechanisms contribute to the resistance. Methods: In this project a systems biology approach was applied to determine how the toxicity of doxorubicin is affected by hypoxia at the metabolome level. A multitude of analytical techniques were applied to analyse the intracellular metabolism of a monolayer of cancer cells (MDA-MB-231). Metabolic profiling was used to determine metabolite markers related to hypoxia-induced chemoresistance. For this gas chromatography mass spectrometry (GC-MS) and ultra high performance liquid chromatography mass spectrometry (UHPLC-MS) were used. Furthermore, network-based correlation analysis was developed as a novel tool to bridge the gap between metabolomics dataset and systems biology modelling. This methodology was applied to elucidate novel metabolic pathways as potential therapeutic targets to overcome hypoxia-induced chemoresistance. This algorithm determines significant correlation differences between different physiological states, and through applying graph-theory on large genome scale models; it is possible to construct a metabolic network of the pathways connecting the pair-wise correlation. Finally, imaging mass spectrometry using time-of-flight secondary ion mass spectrometry (ToF-SIMS) was developed as a tool for in situ metabolite analysis to investigate the metabolic response to chemotherapy in multi-tumour spheroids (MTSs). Results: Metabolic fingerprinting analysis characterised a snapshot of cells exposed to various environmental perturbations. Metabolite markers associated with hypoxia-induced chemoresistance were related to metabolic pathways including gluconeogenesis, DNA synthesis and fatty acid synthesis. Furthermore, network-based correlation analysis revealed specific metabolites in the fatty acid synthesis pathways were contributing to drug resistance, which included malonyl-CoA, 3-oxoeicosanoyl-CoA, stearoyl-CoA and octadecanoic acid. To facilitate the detection of metabolites in ToF SIMS datasets, a series of metabolites standard spectra were acquired. Hypoxic metabolite markers detected in ToF-SIMS data of cell lysates included glycine, lactic acid and succinic acid, which were also shown to be metabolite markers in GC-MS metabolic data. Furthermore, MTS sections were imaged using ToF-SIMS to profile the chemical response to chemotherapy treatment within the oxygen gradient. Loadings from image PCA were explored to determine the metabolic response in the highly oxygenated outer region and hypoxic inner region of the MTS. Conclusion: A multitude of analytical techniques were able to contribute to elucidating the metabolic mechanisms associated with hypoxia-induced chemoresistance. Metabolic profiling combined with a systems biology approach was further able to identify potential underlying metabolic regulation of resistance. Finally ToF-SIMS was developed as a tool for metabolite analysis in complex biological systems in situ.
|Date of Award
|1 Aug 2013
- The University of Manchester
|Nicholas Lockyer (Supervisor), Royston Goodacre (Supervisor), Kaye Williams (Supervisor), Adam McMahon (Supervisor) & Hans Westerhoff (Supervisor)
- Systems biology