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首页> 外文期刊>PLoS Computational Biology >A Model for the Epigenetic Switch Linking Inflammation to Cell Transformation: Deterministic and Stochastic Approaches
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A Model for the Epigenetic Switch Linking Inflammation to Cell Transformation: Deterministic and Stochastic Approaches

机译：表观遗传转换模型连接炎症与细胞转化：确定性和随机方法。

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Recently, a molecular pathway linking inflammation to cell transformation has been discovered. This molecular pathway rests on a positive inflammatory feedback loop between NF-κB, Lin28, Let-7 microRNA and IL6, which leads to an epigenetic switch allowing cell transformation. A transient activation of an inflammatory signal, mediated by the oncoprotein Src, activates NF-κB, which elicits the expression of Lin28. Lin28 decreases the expression of Let-7 microRNA, which results in higher level of IL6 than achieved directly by NF-κB. In turn, IL6 can promote NF-κB activation. Finally, IL6 also elicits the synthesis of STAT3, which is a crucial activator for cell transformation. Here, we propose a computational model to account for the dynamical behavior of this positive inflammatory feedback loop. By means of a deterministic model, we show that an irreversible bistable switch between a transformed and a non-transformed state of the cell is at the core of the dynamical behavior of the positive feedback loop linking inflammation to cell transformation. The model indicates that inhibitors (tumor suppressors) or activators (oncogenes) of this positive feedback loop regulate the occurrence of the epigenetic switch by modulating the threshold of inflammatory signal (Src) needed to promote cell transformation. Both stochastic simulations and deterministic simulations of a heterogeneous cell population suggest that random fluctuations (due to molecular noise or cell-to-cell variability) are able to trigger cell transformation. Moreover, the model predicts that oncogenes/tumor suppressors respectively decrease/increase the robustness of the non-transformed state of the cell towards random fluctuations. Finally, the model accounts for the potential effect of competing endogenous RNAs, ceRNAs, on the dynamics of the epigenetic switch. Depending on their microRNA targets, the model predicts that ceRNAs could act as oncogenes or tumor suppressors by regulating the occurrence of cell transformation.
机译：最近，已经发现了将炎症与细胞转化联系起来的分子途径。该分子途径建立在NF-κB，Lin28，Let-7 microRNA和IL6之间的阳性炎症反馈环上，这导致了表观遗传学转换，允许细胞转化。由癌蛋白Src介导的炎症信号的瞬时激活会激活NF-κB，从而引起Lin28的表达。 Lin28降低Let-7 microRNA的表达，这导致IL6的水平高于直接通过NF-κB获得的水平。反过来，IL6可以促进NF-κB激活。最后，IL6还引发了STAT3的合成，这是细胞转化的关键激活因子。在这里，我们提出了一个计算模型来说明这种积极的炎症反馈回路的动力学行为。通过确定性模型，我们显示了细胞转化状态和非转化状态之间不可逆的双稳态开关是连接炎症与细胞转化的正反馈回路动力学行为的核心。该模型表明，该正反馈回路的抑制剂（肿瘤抑制剂）或激活剂（癌基因）通过调节促进细胞转化所需的炎症信号（Src）阈值来调节表观遗传开关的发生。异质细胞群体的随机模拟和确定性模拟都表明，随机波动（由于分子噪声或细胞间差异）可以触发细胞转化。而且，该模型预测致癌基因/肿瘤抑制物分别降低/增加了细胞非转化状态对随机波动的鲁棒性。最后，该模型说明了竞争性内源RNA，ceRNA对表观遗传转换动力学的潜在影响。根据它们的microRNA靶标，该模型预测ceRNA可以通过调节细胞转化的发生而充当癌基因或抑癌基因。

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