Systems biology of chemotherapy in hypoxia environments
Systems biology of chemotherapy in hypoxia environments
复制标题
DOI：
--
复制
发表时间：
2012-11
期刊：
影响因子：
2.7
通讯作者：
Helen L. Kotze;H. Westerhoff;N. Lockyer;R. Goodacre;Emily G. Armitage;K. Williams
中科院分区：
文献类型：
--
作者：
Helen L. Kotze;H. Westerhoff;N. Lockyer;R. Goodacre;Emily G. Armitage;K. Williams
简介：缺氧被发现在实体癌肿瘤。肿瘤内缺氧的存在抑制了抗癌治疗策略，如化疗完全有效，并且怀疑多种机制有助于耐药性。研究方法：在这个项目中，系统生物学方法被应用于确定阿霉素的毒性如何在代谢组水平上受到缺氧的影响。应用多种分析技术来分析单层癌细胞（MDA-MB-231）的细胞内代谢。代谢谱用于确定与缺氧诱导的化疗耐药性相关的代谢物标志物。为此，使用了气相色谱质谱法（GC-MS）和超高效液相色谱质谱法（UHPLC-MS）。此外，基于网络的相关性分析被开发为一种新的工具，以弥合代谢组学数据集和系统生物学建模之间的差距。这种方法学被应用于阐明新的代谢途径作为潜在的治疗靶点，以克服缺氧诱导的化疗耐药性。该算法确定不同生理状态之间的显着相关性差异，并通过将图论应用于大型基因组规模模型;可以构建连接成对相关性的途径的代谢网络。最后，使用飞行时间二次离子质谱（ToF-SIMS）的成像质谱被开发为原位代谢物分析的工具，以研究多肿瘤球体（MTS）对化疗的代谢反应。结果：代谢指纹分析表征了暴露于各种环境扰动的细胞的快照。与低氧诱导的化疗耐药相关的代谢标志物与代谢途径有关，包括代谢产物生成、DNA合成和脂肪酸合成。此外，基于网络的相关性分析显示，脂肪酸合成途径中的特定代谢产物有助于耐药性，这些代谢产物包括丙二酰辅酶A、3-氧代二十烷酰辅酶A、硬脂酰辅酶A和十八烷酸。为了便于检测ToF西姆斯数据集中的代谢物，获得了一系列代谢物标准光谱。在细胞裂解物的ToF-SIMS数据中检测到的代谢物标记物包括甘氨酸、乳酸和琥珀酸，其也显示为GC-MS代谢数据中的代谢物标记物。此外，使用ToF-SIMS对MTS切片进行成像，以描绘氧梯度内对化疗治疗的化学反应。探讨了图像PCA的负荷，以确定MTS的高氧合外部区域和缺氧内部区域的代谢反应。结论：多种分析技术能够有助于阐明与缺氧诱导的化学抗性相关的代谢机制。结合系统生物学方法的代谢谱分析能够进一步确定抗性的潜在潜在代谢调节。最后，ToF-SIMS被开发为复杂生物系统中原位代谢物分析的工具。
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.