Both ligand- and cell-specific parameters control ligand agonism in a kinetic model of g protein-coupled receptor signaling.
Both ligand- and cell-specific parameters control ligand agonism in a kinetic model of g protein-coupled receptor signaling.
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DOI：
10.1371/journal.pcbi.0030006
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发表时间：
2007-01-12
影响因子：
4.3
通讯作者：
Linderman JJ
中科院分区：
文献类型：
--
作者：
Kinzer-Ursem TL;Linderman JJ
G蛋白偶联受体(GPCRs)以多种动态(如配体结合、非活性、G蛋白偶联)存在，影响G蛋白的激活和最终的反应产生。在包含这些不同状态的GPCR信号的定量模型中，参数值通常没有特征性或在大范围内变化，使得识别重要参数和信号结果变得困难。在这里，我们使用参数变化和敏感性分析，确定了配体和细胞特定的参数，这些参数是GPCR信号的动态模型中细胞反应行为的重要决定因素。不出所料，反应的性质(即正/中性/反向激动性)显著地受配体偏向受体进入活性构象的能力影响。我们还发现，一些细胞特有的参数，包括活跃和非活跃受体种类的比率，G蛋白激活的速率常数，以及受体和G蛋白的表达水平也显著影响激动性。表达受体或G蛋白的数量高于或低于内源性水平的几倍可能会导致系统行为与内源性系统中测量的不一致。最后，通过敏感性分析发现，细胞特异性参数的微小变化是反应行为的重要决定因素，可以将配体诱导的反应从阳性改变为阴性，这一现象被称为蛋白质激动症。我们的发现为β-2-肾上腺素能和α-2A-肾上腺素能受体系统中的蛋白质激动剂提供了一个解释。G蛋白偶联受体(GPCRs)是一种跨膜蛋白，参与血管扩张、免疫反应和记忆等多种生理功能。内源性配体(如激素、神经递质)和外源性配体(如药物)都与这些受体结合，启动细胞内事件，最终导致细胞反应。我们描述了G蛋白激活的动态模型，并结合有效的参数变异和敏感性分析技术来识别影响G蛋白激活的关键细胞和配体特定的参数。我们的结果表明，尽管配体特定的参数确实强烈地影响细胞的反应(导致G蛋白激活的增加或减少)，但细胞参数也可能决定G蛋白激活的大小和方向。我们应用我们的发现来描述蛋白质激动症，一种现象，在这种现象中，相同的配体可能会引起积极和消极的反应，可能会导致细胞特异性参数的变化。这些发现可能被用来理解不同类型的细胞和组织对药物治疗的不同反应的分子基础。此外，这些方法可以普遍应用于细胞信号模型，并将有助于指导实验资源进一步表征这些网络中的关键参数。
G protein–coupled receptors (GPCRs) exist in multiple dynamic states (e.g., ligand-bound, inactive, G protein–coupled) that influence G protein activation and ultimately response generation. In quantitative models of GPCR signaling that incorporate these varied states, parameter values are often uncharacterized or varied over large ranges, making identification of important parameters and signaling outcomes difficult to intuit. Here we identify the ligand- and cell-specific parameters that are important determinants of cell-response behavior in a dynamic model of GPCR signaling using parameter variation and sensitivity analysis. The character of response (i.e., positive/neutral/inverse agonism) is, not surprisingly, significantly influenced by a ligand's ability to bias the receptor into an active conformation. We also find that several cell-specific parameters, including the ratio of active to inactive receptor species, the rate constant for G protein activation, and expression levels of receptors and G proteins also dramatically influence agonism. Expressing either receptor or G protein in numbers several fold above or below endogenous levels may result in system behavior inconsistent with that measured in endogenous systems. Finally, small variations in cell-specific parameters identified by sensitivity analysis as significant determinants of response behavior are found to change ligand-induced responses from positive to negative, a phenomenon termed protean agonism. Our findings offer an explanation for protean agonism reported in β2--adrenergic and α2A-adrenergic receptor systems. G protein–coupled receptors (GPCRs) are transmembrane proteins involved in physiological functions ranging from vasodilation and immune response to memory. The binding of both endogenous ligands (e.g., hormones, neurotransmitters) and exogenous ligands (e.g., pharmaceuticals) to these receptors initiates intracellular events that ultimately lead to cell responses. We describe a dynamic model for G protein activation, an immediate outcome of GPCR signaling, and use it together with efficient parameter variation and sensitivity analysis techniques to identify the key cell- and ligand-specific parameters that influence G protein activation. Our results show that although ligand-specific parameters do strongly influence cell response (either causing increases or decreases in G protein activation), cellular parameters may also dictate the magnitude and direction of G protein activation. We apply our findings to describe how protean agonism, a phenomenon in which the same ligand may induce both positive and negative responses, may result from changes in cell-specific parameters. These findings may be used to understand the molecular basis of different responses of cell types and tissues to pharmacological treatment. In addition, these methods may be applied generally to models of cellular signaling and will help guide experimental resources toward further characterization of the key parameters in these networks.
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影响因子：
56.9
作者：
Blower, SM;Gershengorn, HB;Grant, RM
通讯作者：
Grant, RM
影响因子：
3.4
作者：
Bagci, EZ;Vodovotz, Y;Bahar, I
通讯作者：
Bahar, I
影响因子：
6.4
作者：
Grant, KR;Harnett, W;Harnett, MM
通讯作者：
Harnett, MM
DOI：
10.1073/pnas.0500362102
发表时间：
2005-03-22
影响因子：
11.1
作者：
Chang, ST;Linderman, JJ;Kirschner, DE
通讯作者：
Kirschner, DE
影响因子：
4.8
作者：
Clark, MJ;Harrison, C;Traynor, JR
通讯作者：
Traynor, JR