Eyassu Filmon, Angione Claudio
Department of Computer Science and Information Systems, Teesside University, Middlesbrough, UK.
R Soc Open Sci. 2017 Oct 25;4(10):170360. doi: 10.1098/rsos.170360. eCollection 2017 Oct.
Metabolism is the only biological system that can be fully modelled at genome scale. As a result, metabolic models have been increasingly used to study the molecular mechanisms of various diseases. Hypoxia, a low-oxygen tension, is a well-known characteristic of many cancer cells. Pyruvate dehydrogenase (PDH) controls the flux of metabolites between glycolysis and the tricarboxylic acid cycle and is a key enzyme in metabolic reprogramming in cancer metabolism. Here, we develop and manually curate a constraint-based metabolic model to investigate the mechanism of pyruvate dehydrogenase under hypoxia. Our results characterize the activity of pyruvate dehydrogenase and its decline during hypoxia. This results in lactate accumulation, consistent with recent hypoxia studies and a well-known feature in cancer metabolism. We apply machine-learning techniques on the flux datasets to identify reactions that drive these variations. We also identify distinct features on the structure of the variables and individual metabolic components in the switch from normoxia to hypoxia. Our results provide a framework for future studies by integrating multi-omics data to predict condition-specific metabolic phenotypes under hypoxia.
新陈代谢是唯一能够在基因组规模上进行全面建模的生物系统。因此，代谢模型越来越多地被用于研究各种疾病的分子机制。缺氧，即低氧张力，是许多癌细胞的一个众所周知的特征。丙酮酸脱氢酶（PDH）控制着糖酵解和三羧酸循环之间的代谢物通量，是癌症代谢中代谢重编程的关键酶。在这里，我们开发并人工整理了一个基于约束的代谢模型，以研究缺氧条件下丙酮酸脱氢酶的机制。我们的结果表征了丙酮酸脱氢酶的活性及其在缺氧过程中的下降。这导致乳酸积累，与最近的缺氧研究一致，也是癌症代谢中一个众所周知的特征。我们将机器学习技术应用于通量数据集，以识别驱动这些变化的反应。我们还识别了从常氧到缺氧转变过程中变量和单个代谢成分结构上的不同特征。我们的结果通过整合多组学数据，为预测缺氧条件下特定条件的代谢表型提供了一个未来研究的框架。
