Recruitment principles and injury-induced plasticity in thoracic paravertebral sympathetic postganglionic neurons

项目摘要 本项目探索了一个几乎没有研究和理解的脊椎动物自主神经领域， 神经科学：胸椎旁交感节后神经元的募集特性 （tSPN）。胸段椎旁交感神经链节的突出作用是作为最终的神经控制 调节血管紧张素的成分。考虑到它们在向身体发出自主信号中的战略节点位置，任何 tSPN中的可塑性可能具有很高的意义。不幸的是，tSPN在很大程度上无法在体内 因此，从颈和腰链神经节的研究中推断出了手术原理。仅3例体外试验 研究揭示了tSPN的电生理学特性：没有一个能准确测量细胞整合 由于电极穿刺损伤导致的性质或潜在的募集原则。我们进行了第一次 成年小鼠尾侧胸链神经节离体制备的生理学研究 节段性节前和吻尾神经节间连接完整。我们得到了第一个完整的- tSPN的细胞膜片钳记录和观察到的根本不同的整合和放电特性， 比以前观察到的。这种可靠的数据集是现实的计算模拟的关键先决条件。 我们建议将实验测试与建模相结合，以了解tSPN的招募原则， 其综合属性。[SA1]我们将检验tSPN具有异质性突触、细胞、 和网络属性，并积极参与投入产出招聘战略。 高位胸段脊髓损伤（SCI）通过以下途径破坏了调节tSPN兴奋性的脑干通路： 脊髓节前袢这种破坏可导致突然危及生命的tSPN介导的高血压， 自主神经反射障碍（autonomic dysreflexia）椎旁交感神经链神经节功能障碍是否有助于 血管扩张反应中的扩增是未知的。为了填补这一重大的知识空白，实验 研究将揭示用于tSPN募集的细胞和突触组织规则的可塑性。[SA2] 我们将检验tSPN增加其内在兴奋性并从线性转换为非线性的假设 SCI后的增益放大器。计算模拟将构建一个数据库， 具有潜在临床相关性的招募原则，可能会对该领域产生变革。的相对 组织的简单性使得通过建模发现原则比在 复杂的系统tSPN功能和紧急功能障碍的神经基础的真实模拟可以 催化基于预测性药物发现的高通量模拟， 临床前测试 意义：我们的目标是揭示控制最终神经指挥通路的运作原理 调节血管张力。由于交感神经功能亢进与各种自主神经紊乱有关， 适用于现实建模研究的方法将在临床前和转化研究中具有广泛的预测用途。

Project Summary The present project explores a barely studied and poorly-understood area of vertebrate autonomic neuroscience: the recruitment properties of thoracic paravertebral sympathetic postganglionic neurons (tSPNs). The prominent role of thoracic paravertebral sympathetic chain ganglia is as the final neural control element regulating vasomotor tone. Given their strategic nodal site in autonomic signaling to body, any plasticity in tSPNs is likely to be of high significance. Unfortunately, tSPNs are largely inaccessible for in vivo study, so operational principles are inferred from studies in cervical and lumbar chain ganglia. Only 3 in vitro studies have revealed tSPN electrophysiological properties: none accurately measure cellular integrative properties or underlying recruitment principles due to electrode impalement injury. We undertook the first physiological studies on caudal thoracic chain ganglia in the adult mouse by developing an ex vivo preparation with intact segmental preganglionic and rostrocaudal interganglionic connections. We obtained the first whole- cell patch clamp recordings of tSPNs and observed fundamentally different integrative and firing properties are than previously observed. This reliable data set is a critical prerequisite to realistic computational simulation. We propose to interleave experimental testing with modeling to understand tSPN recruitment principles and their integrative properties. [SA1] We will test the hypothesis that tSPNs have heterogeneous synaptic, cellular, and network properties, and are active participants in input-output recruitment strategies. Higher thoracic spinal cord injuries (SCI) disrupt the brainstem pathways that regulate tSPN excitability via spinal preganglionic loops. Such disruption can lead to sudden life-threatening tSPN mediated hypertensive crises (autonomic dysreflexia). Whether paravertebral sympathetic chain ganglia dysfunction contributes to amplification in a vasomotor response is unknown. To fill this significant gap in knowledge, experimental studies will disclose plasticity in the cellular and synaptic organizational rules serving tSPN recruitment. [SA2] We will test the hypothesis that tSPNs increased their intrinsic excitability and convert from linear to non-linear gain amplifiers after SCI. Computational simulation will construct a database amenable to realistic modeling of recruitment principles of potential clinical relevance that could be transformative to the field. The relative simplicity of the organization makes discovery of principles through modeling more assured than in more complex systems. Realistic simulation of the neural bases of tSPN function and emergent dysfunction could catalyze predictive drug discovery-based high throughput simulations that normalize function for rapid preclinical testing. Significance: we aim to uncover the operational principles governing the final neural command pathways regulating vascular tone. As sympathetic hyperactivity is implicated in various autonomic disorders, a database amenable to realistic modeling studies will be of broad predictive use in preclinical and translational studies.