Computational investigation of neuropathic changes in primary afferent excitabili

描述（由申请人提供）：神经性疼痛由神经系统损伤或功能障碍引起。这是一个无法估量的痛苦的来源，尽管基础研究取得了进展，但仍然难以治疗。初级传入神经的异位尖峰通过驱动中枢致敏和向CNS提供异常感觉输入而直接导致神经性疼痛。因此，研究人员花了相当大的努力试图了解异位尖峰，事实上，现在知道了很多关于哪些离子通道在不同的初级传入中表达，以及这些通道在神经病条件下如何改变。然而，离子通道表达或性质的变化并不总是对细胞兴奋性具有直接的影响;例如，Nav1.7通道中的单个突变已显示对兴奋性具有相反的影响，这取决于细胞中存在的其他通道（Rush等人，2006; PNAS 103：8245-50）。这说明细胞的兴奋性是一个紧急的属性，取决于膜电流之间的复杂的相互作用。 因此，我们认为，成功开发新的镇痛药需要一种方法，专门解决和占膜电流相互作用的复杂方式。复杂的（即非线性的）相互作用意味着膜电流彼此竞争、合作或干扰。解读这些相互作用需要计算工具，这对疼痛研究来说是陌生的.我们建议从动力系统理论中导入工具，更重要的是，建立概念框架，将这些工具与实验方法相结合。我们将证明我们的综合方法的效用，通过使用它来解释如何模式的定性，损伤诱导的神经元兴奋性的变化（这是明确的神经性疼痛）从定量改变膜电流之间的异常非线性相互作用。 我们的方法是一个多学科的，协同结合计算机建模，数学分析和实验。自上而下的建模将被用来复制细胞兴奋性的变化在最小的计算机模型，使动力系统分析可以用来解释兴奋性的变化的基础上改变非线性相互作用。在通过自上而下建模和分析获得的理论知识的指导下，自下而上建模将用于确定特定膜电流中哪些损伤诱导的变化足以解释细胞过度兴奋模式。此外，为了建立自上而下和自下而上模型预测的变化之间的因果关系，我们将在来自新生和神经损伤动物的真实的神经元中进行动态钳夹实验，以确定哪些分子（通道）变化是必要的，足以解释大直径背根神经节（DRG）神经元的过度兴奋。 总之，我们专注于膜电流之间的非线性相互作用是新颖的。我们提出的解决方案，调查这些相互作用，使用计算工具（迄今为止一直缺少疼痛研究）作为一个综合的，多学科的方法的一部分，同样具有创新性和潜在的变革性。 公共卫生相关性：神经性疼痛是一种难以估量的痛苦来源，仍然是众所周知的难以治疗。鉴于其患病率约为十五分之一，并且对护理人员和医疗保健系统造成负担，总成本是巨大的。开发对神经性疼痛具有更大疗效的新镇痛药的困难突出了对新的和创新的基础研究的需求，例如计算建模和动力学分析，这将有助于解释现有数据，并最终导致临床翻译。

DESCRIPTION (provided by applicant): Neuropathic pain results from damage to or dysfunction of the nervous system. It is a source of incalculable suffering and remains notoriously difficult to treat despite advances in basic research. Ectopic spiking in primary afferents contributes directly to neuropathic pain by driving central sensitization and by providing abnormal sensory input to the CNS. Accordingly, researchers have spent considerable effort trying to understand ectopic spiking, and indeed, much is now known about which ion channels are expressed in different primary afferents and how those channels are altered under neuropathic conditions. However, changes in ion channel expression or properties do not always have straightforward effects on cellular excitability; for example, a single mutation in Nav1.7 channels has been shown to have opposite effects on excitability depending on the other channels present in the cell (Rush et al. 2006; PNAS 103: 8245-50). This illustrates that cellular excitability is an emergent property that depends on the complex interaction between membrane currents. We argue, therefore, that successful development of new analgesics requires an approach that specifically addresses and accounts for the complex ways in which membrane currents interact. Complex (i.e. nonlinear) inter- actions imply that membrane currents compete, cooperate, or interfere with one another. Deciphering those inter- actions requires computational tools that are foreign to pain research. We propose to import tools from dynamical systems theory and, more importantly, to establish the conceptual framework by which to integrate those tools with experimental approaches. We will demonstrate the utility of our integrated approach by using it to explain how patterns of qualitative, injury-induced changes in neuronal excitability (that are clearly linked with neuropathic pain) arise from aberrant nonlinear interactions between quantitatively altered membrane currents. Our approach is a multidisciplinary one that synergistically combines computer modeling, mathematical analysis, and experiments. Top-down modeling will be used to replicate cellular excitability changes in minimal computer models so that dynamical systems analysis can be used to explain excitability changes on the basis of altered nonlinear interactions. Guided by the theoretical knowledge gained through top-down modeling and analysis, bottom-up modeling will be used to identify which injury-induced changes in specific membrane currents are sufficient to explain cellular hyperexcitability patterns. Furthermore, to establish causal links between the changes predicted by top-down and bottom-up modeling, we will conduct dynamic clamp experi- ments in real neurons from naove and nerve-injured animals to determine which molecular (channel) changes are necessary and sufficient to explain hyperexcitability in large diameter dorsal root ganglion (DRG) neurons. In summary, our focus on nonlinear interactions between membrane currents is novel. Our proposed solution for investigating those interactions using computational tools (which have heretofore been missing from pain research) as part of an integrative, multidisciplinary approach is equally innovative and potentially transformative. PUBLIC HEALTH RELEVANCE: Neuropathic pain is a source of incalculable suffering that remains notoriously difficult to treat. Given its prevalence of approximately one in 15 people and its burden on caregivers and the healthcare system, the total costs are enormous. The difficulty developing new analgesics with greater efficacy against neuropathic pain highlights the need for new and innovative lines of basic research, such as computational modeling and dynamical analysis, that will facilitate interpretation of existing data and, ultimately, lead to clinical translation.