Hypoxia-inducible factor (HIF) network: insights from mathematical models.
Hypoxia-inducible factor (HIF) network: insights from mathematical models.
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DOI：
10.1186/1478-811x-11-42
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发表时间：
2013-06-10
期刊：
影响因子：
--
通讯作者：
Cheong A
中科院分区：
文献类型：
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作者：
Cavadas MA;Nguyen LK;Cheong A
氧是细胞功能的关键分子。当氧气需求超过供应时，以缺氧诱导因子（HIF）为中心的氧感应通路被打开，并通过上调参与血管生成、红细胞生成和糖酵解的基因来促进对缺氧的适应。HIF的调控是通过复杂的调控机制严格调控的。值得注意的是，其蛋白稳定性受氧传感脯氨酸羟化酶结构域（PHD）酶控制，其转录活性受天冬酰胺羟化酶FIH（抑制因子HIF-1）控制。为了探究缺氧诱导的HIF信号的复杂性，对该通路的数学建模已经进行了大约十年。在本文中，我们回顾了用于描述和解释HIF通路特定行为的现有数学模型，以及它们如何为我们对网络的理解提供新的见解。建模的主题包括对氧梯度降低的开关式响应、微环境因素的作用、FIH的调节以及HIF响应的时间动态。我们还将讨论这些模型的技术方面、范围和局限性。最近，HIF通路通过与NFκB和mTOR等通路的串扰，涉及其他疾病背景，如缺氧炎症和癌症。我们将研究未来的数学建模和相互关联网络的模拟如何有助于理解复杂病理生理情况下的HIF行为。最终，这将允许在不同的疾病环境中识别新的药理学靶点。
Oxygen is a crucial molecule for cellular function. When oxygen demand exceeds supply, the oxygen sensing pathway centred on the hypoxia inducible factor (HIF) is switched on and promotes adaptation to hypoxia by up-regulating genes involved in angiogenesis, erythropoiesis and glycolysis. The regulation of HIF is tightly modulated through intricate regulatory mechanisms. Notably, its protein stability is controlled by the oxygen sensing prolyl hydroxylase domain (PHD) enzymes and its transcriptional activity is controlled by the asparaginyl hydroxylase FIH (factor inhibiting HIF-1). To probe the complexity of hypoxia-induced HIF signalling, efforts in mathematical modelling of the pathway have been underway for around a decade. In this paper, we review the existing mathematical models developed to describe and explain specific behaviours of the HIF pathway and how they have contributed new insights into our understanding of the network. Topics for modelling included the switch-like response to decreased oxygen gradient, the role of micro environmental factors, the regulation by FIH and the temporal dynamics of the HIF response. We will also discuss the technical aspects, extent and limitations of these models. Recently, HIF pathway has been implicated in other disease contexts such as hypoxic inflammation and cancer through crosstalking with pathways like NFκB and mTOR. We will examine how future mathematical modelling and simulation of interlinked networks can aid in understanding HIF behaviour in complex pathophysiological situations. Ultimately this would allow the identification of new pharmacological targets in different disease settings.