Garcia Mar Arias, Alvarez Miguel Sanchez, Sailem Heba, Bousgouni Vicky, Sero Julia, Bakal Chris
Chester Beatty Laboratories, Division of Cancer Biology, Institute of Cancer Research, 237 Fulham Road, London, UK.
Mol Biosyst. 2012 Oct;8(10):2605-13. doi: 10.1039/c2mb25092f.
Reactive Oxygen Species (ROS) are a natural by-product of cellular growth and proliferation, and are required for fundamental processes such as protein-folding and signal transduction. However, ROS accumulation, and the onset of oxidative stress, can negatively impact cellular and genomic integrity. Signalling networks have evolved to respond to oxidative stress by engaging diverse enzymatic and non-enzymatic antioxidant mechanisms to restore redox homeostasis. The architecture of oxidative stress response networks during periods of normal growth, and how increased ROS levels dynamically reconfigure these networks are largely unknown. In order to gain insight into the structure of signalling networks that promote redox homeostasis we first performed genome-scale RNAi screens to identify novel suppressors of superoxide accumulation. We then infer relationships between redox regulators by hierarchical clustering of phenotypic signatures describing how gene inhibition affects superoxide levels, cellular viability, and morphology across different genetic backgrounds. Genes that cluster together are likely to act in the same signalling pathway/complex and thus make "functional interactions". Moreover we also calculate differential phenotypic signatures describing the difference in cellular phenotypes following RNAi between untreated cells and cells submitted to oxidative stress. Using both phenotypic signatures and differential signatures we construct a network model of functional interactions that occur between components of the redox homeostasis network, and how such interactions become rewired in the presence of oxidative stress. This network model predicts a functional interaction between the transcription factor Jun and the IRE1 kinase, which we validate in an orthogonal assay. We thus demonstrate the ability of systems-biology approaches to identify novel signalling events.
活性氧(ROS)是细胞生长和增殖的自然副产物,是蛋白质折叠和信号转导等基本过程所必需的。然而,ROS的积累以及氧化应激的发生会对细胞和基因组完整性产生负面影响。信号网络已经进化出通过参与多种酶促和非酶促抗氧化机制来应对氧化应激,以恢复氧化还原稳态。在正常生长期间氧化应激反应网络的结构,以及ROS水平升高如何动态地重新配置这些网络,在很大程度上尚不清楚。为了深入了解促进氧化还原稳态的信号网络结构,我们首先进行了全基因组RNA干扰筛选,以鉴定超氧化物积累的新型抑制因子。然后,我们通过对描述基因抑制如何影响不同遗传背景下超氧化物水平、细胞活力和形态的表型特征进行层次聚类,推断氧化还原调节因子之间的关系。聚集在一起的基因可能在相同的信号通路/复合物中起作用,从而形成“功能相互作用”。此外,我们还计算了差异表型特征,描述了未处理细胞与遭受氧化应激的细胞在RNA干扰后细胞表型的差异。利用表型特征和差异特征,我们构建了氧化还原稳态网络组件之间发生的功能相互作用的网络模型,以及在氧化应激存在下这种相互作用如何重新连接。这个网络模型预测了转录因子Jun和IRE1激酶之间的功能相互作用,我们在正交试验中对其进行了验证。因此,我们证明了系统生物学方法识别新型信号事件 的能力。