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真核生物梯度感知中的自发极化:基于前后通路相互抑制的数学模型。

Spontaneous polarization in eukaryotic gradient sensing: a mathematical model based on mutual inhibition of frontness and backness pathways.

作者信息

Narang Atul

机构信息

Department of Chemical Engineering, University of Florida, Gainesville, FL 32611-6005, USA.

出版信息

J Theor Biol. 2006 Jun 21;240(4):538-53. doi: 10.1016/j.jtbi.2005.10.022. Epub 2005 Dec 15.

Abstract

A key problem of eukaryotic cell motility is the signaling mechanism of chemoattractant gradient sensing. Recent experiments have revealed the molecular correlate of gradient sensing: Frontness molecules, such as PI3P and Rac, localize at the front end of the cell, and backness molecules, such as Rho and myosin II, accumulate at the back of the cell. Importantly, this frontness-backness polarization occurs spontaneously even if the cells are exposed to uniform chemoattractant profiles. The spontaneous polarization suggests that the gradient sensing machinery undergoes a Turing bifurcation. This has led to several classical activator-inhibitor and activator-substrate models which identify the frontness molecules with the activator. Conspicuously absent from these models is any accounting of the backness molecules. This stands in sharp contrast to experiments which show that the backness pathways inhibit the frontness pathways. Here, we formulate a model based on the mutually inhibitory interaction between the frontness and backness pathways. The model builds upon the mutual inhibition model proposed by Bourne and coworkers [Xu et al., 2003. Divergent signals and cytoskeletal assemblies regulate self-organizing polarity in neutrophils. Cell 114, 201-214.]. We show that mutual inhibition alone, without the help of any positive feedback (autocatalysis), can trigger spontaneous polarization of the frontness and backness pathways. The spatial distribution of the frontness and backness molecules in response to inhibition and activation of the frontness and backness pathways are consistent with those observed in experiments. Furthermore, depending on the parameter values, the model yields spatial distributions corresponding to chemoattraction (frontness pathways in-phase with the external gradient) and chemorepulsion (frontness pathways out-of-phase with the external gradient). Analysis of the model suggests a mechanism for the chemorepulsion-to-chemoattraction transition observed in neurons.

摘要

真核细胞运动的一个关键问题是趋化因子梯度感知的信号传导机制。最近的实验揭示了梯度感知的分子关联:前沿分子,如磷脂酰肌醇-3-磷酸(PI3P)和Rac,定位于细胞前端,而后沿分子,如Rho和肌球蛋白II,则在细胞后端积累。重要的是,即使细胞暴露于均匀的趋化因子分布中,这种前后极化也会自发发生。自发极化表明梯度感知机制经历了图灵分岔。这导致了几个经典的激活剂-抑制剂和激活剂-底物模型,这些模型将前沿分子视为激活剂。这些模型中明显缺少对后沿分子的任何描述。这与实验结果形成鲜明对比,实验表明后沿信号通路会抑制前沿信号通路。在这里,我们基于前沿和后沿信号通路之间的相互抑制相互作用构建了一个模型。该模型基于伯恩及其同事提出的相互抑制模型[徐等人,2003年。不同信号和细胞骨架组装调节中性粒细胞的自组织极性。《细胞》114卷,201 - 214页。]。我们表明,仅相互抑制,无需任何正反馈(自催化)的帮助,就能触发前沿和后沿信号通路的自发极化。前沿和后沿分子在响应前沿和后沿信号通路的抑制和激活时的空间分布与实验中观察到的一致。此外,根据参数值,该模型产生的空间分布对应于化学吸引(前沿信号通路与外部梯度同相)和化学排斥(前沿信号通路与外部梯度异相)。对该模型的分析提出了一种在神经元中观察到的化学排斥到化学吸引转变的机制。

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