Computational model of the insect pheromone transduction cascade.
Computational model of the insect pheromone transduction cascade.
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DOI：
10.1371/journal.pcbi.1000321
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发表时间：
2009-03
影响因子：
4.3
通讯作者：
Rospars JP
中科院分区：
文献类型：
--
作者：
Gu Y;Lucas P;Rospars JP
提出了雄性飞蛾嗅觉受体神经元中受体电位产生的生物物理模型。它考虑了所有前效应器过程——信息素分子从空气到感器淋巴的易位、它们的失活和与受体的相互作用，以及G蛋白和效应酶的激活——并重点关注主要的后效应器过程。这些过程涉及第二信使（IP3和DAG）的产生和降解、一系列离子通道（IP3门控Ca2+通道、DAG门控阳离子通道、Ca2+门控Cl−通道、Ca2+-和电压门控K+通道）的打开和关闭以及Ca2+挤出机制。整个网络由调节剂（蛋白激酶 C 和 Ca2+-钙调蛋白）调节，对效应器和通道施加反馈抑制。模拟了这些相连的化学物质和电流随时间的演变，以及响应不同强度的单脉冲刺激而产生的膜电位。通过与不同信息素剂量下实验测量的受体电位的幅度和时间特征（上升和下降时间）进行比较来拟合未知参数值。所获得的模型捕捉了剂量反应曲线的主要特征：6个十进制的宽动态范围，与实验数据相同的幅度，短的上升时间和长的下降时间。它还再现了第二信使动力学。这表明两种主要类型的去极化离子通道在低和高信息素浓度下发挥不同的作用。 DAG门控阳离子通道在低浓度下起主要去极化作用，Ca2+门控Cl−通道在中高浓度下起主要去极化作用。提出了一些可测试的预测，并讨论了未来的发展。所有感觉神经元都通过类似的分子和离子机制将其自然刺激转换为流过其感觉膜的电流，无论是分子、光子还是机械力。嗅觉受体神经元（ORN）的刺激是挥发性分子，也不例外，其中最著名的一种是：雄性飞蛾的嗅觉受体神经元极其敏感，可以检测同种雌性飞蛾释放的性信息素。我们提供了这种 ORN 类型中起作用的细胞内分子机制的详细计算模型。我们定性和定量地描述了初始事件（信息素分子与 ORN 表面专门受体的相互作用）如何通过一系列相关的生化和电事件放大为全细胞反应（受体电位）。我们详细介绍了向上激活反应的各自作用，涉及可渗透阳离子、氯和钾的离子通道级联，它们通过反馈失活机制的控制，以及钙的中心调节作用。该计算模型有助于对该信号通路的综合理解，提供可测试的假设，并提出新的实验方法。
A biophysical model of receptor potential generation in the male moth olfactory receptor neuron is presented. It takes into account all pre-effector processes—the translocation of pheromone molecules from air to sensillum lymph, their deactivation and interaction with the receptors, and the G-protein and effector enzyme activation—and focuses on the main post-effector processes. These processes involve the production and degradation of second messengers (IP3 and DAG), the opening and closing of a series of ionic channels (IP3-gated Ca2+ channel, DAG-gated cationic channel, Ca2+-gated Cl− channel, and Ca2+- and voltage-gated K+ channel), and Ca2+ extrusion mechanisms. The whole network is regulated by modulators (protein kinase C and Ca2+-calmodulin) that exert feedback inhibition on the effector and channels. The evolution in time of these linked chemical species and currents and the resulting membrane potentials in response to single pulse stimulation of various intensities were simulated. The unknown parameter values were fitted by comparison to the amplitude and temporal characteristics (rising and falling times) of the experimentally measured receptor potential at various pheromone doses. The model obtained captures the main features of the dose–response curves: the wide dynamic range of six decades with the same amplitudes as the experimental data, the short rising time, and the long falling time. It also reproduces the second messenger kinetics. It suggests that the two main types of depolarizing ionic channels play different roles at low and high pheromone concentrations; the DAG-gated cationic channel plays the major role for depolarization at low concentrations, and the Ca2+-gated Cl− channel plays the major role for depolarization at middle and high concentrations. Several testable predictions are proposed, and future developments are discussed. All sensory neurons transduce their natural stimulus, whether a molecule, a photon, or a mechanical force, in an electrical current flowing through their sensory membrane via similar molecular and ionic mechanisms. Olfactory receptor neurons (ORNs), whose stimuli are volatile molecules, are no exception, including one of the best known: the exquisitely sensitive ORNs of male moths that detect the sexual pheromone released by conspecific females. We provide a detailed computational model of the intracellular molecular mechanisms at work in this ORN type. We describe qualitatively and quantitatively how the initial event, the interaction of pheromone molecules with specialized receptors at the ORN surface, is amplified through a sequence of linked biochemical and electrical events into a whole cell response, the receptor potential. We detail the respective roles of the upward activating reactions involving a cascade of ionic channels permeable to cations, chloride and potassium, their control by feedback inactivating mechanisms, and the central regulatory role of calcium. This computational model contributes to an integrated understanding of this signalling pathway, provides testable hypotheses, and suggests new experimental approaches.
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影响因子：
9.8
作者：
Benton, Richard;Sachse, Silke;Michnick, Stephen W;Vosshall, Leslie B
通讯作者：
Vosshall, Leslie B
影响因子：
4.5
作者：
BOTON, R;SINGER, D;DASCAL, N
通讯作者：
DASCAL, N
影响因子：
3.8
作者：
BOEKHOFF, I;SEIFERT, E;BREER, H
通讯作者：
BREER, H
影响因子：
2.8
作者：
Dolzer, J;Fischer, K;Stengl, M
通讯作者：
Stengl, M
DOI：
10.1073/pnas.0504099102
发表时间：
2005-07-26
影响因子：
11.1
作者：
Dougherty, DP;Wright, GA;Yew, AC
通讯作者：
Yew, AC