CRCNS:Mechanisms of Axonal Gradient Detection

CRCNS：轴突梯度检测机制

• 批准号: 6797202
• 负责人: Jeffrey S. Urbach
• 金额: $ 24.15万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-30 至 2006-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6797202
• 关键词: axon; gene deletion mutation; laboratory rat; nerve growth factors; nervous system regeneration; neural conduction; neural information processing; neurogenesis; neuronal guidance; newborn animals; receptor binding; spinal ganglion

DESCRIPTION (provided by applicant): Correct brain function requires correct brain wiring. An important step in the establishment of appropriate connectivity is the guidance of axons over long distances in the developing brain to find their correct targets. A crucial type of guidance cue axons use is concentration gradients of attractive or repellent factors. Over the past decade many new molecules have been discovered that guide axons in this way. However, as yet we still have very little understanding of the precise mechanisms by which axons detect and respond to gradients. A better understanding of these mechanisms would enable us to understand better (1) how the nervous system is normally constructed, (2) why axons sometimes mistarget during development, (3) the effect of gene deletions and mutations on wiring, and (4) how to encourage axonal regeneration to appropriate targets after injury. The goal of this project is to develop a mechanistic understanding of axonal behavior in gradients by building computational models of gradient detection and directed movement for axons. Two types of models will be investigated. The first type is based on the idea that gradient detection is limited by inevitable stochastic noise in the receptor binding process. These models assume a small, spherical sensing device, and make predictions about the minimum detectable gradient steepness that such sensing devices can detect. The second type of model addresses the unique role that filopodia play in axonal gradient sensing and movement. The model is based on the idea that filopodia act as somewhat independent sensing devices, and it is their combined dynamics that determines the threshold for gradient detection and the trajectories that axons follow. A crucial component of the project is that the computational modeling will be directly tested and constrained using a new, quantitative experimental assay the investigators have recently developed. The assay allows stable molecular gradients of precisely controlled shape to be established in a collagen gel. The system used will be the guidance of dorsal root ganglion axons by gradients of Nerve Growth Factor (NG). Exponentially-shaped gradients of varying steepness of NG will be used to determine the minimum gradient steepness axons can detect as a function of absolute concentration, and the trajectories of axons in these gradients will be quantitatively analyzed.

描述（由申请人提供）：正确的大脑功能需要正确的大脑布线。建立适当连接的一个重要步骤是在发育中的大脑中长距离引导轴突找到正确的目标。轴突使用的一种关键类型的引导线索是吸引或排斥因子的浓度梯度。在过去的十年里，许多新的分子被发现以这种方式引导轴突。然而，到目前为止，我们仍然对轴突检测和响应梯度的精确机制知之甚少。更好地理解这些机制将使我们能够更好地理解（1）神经系统是如何正常构建的，（2）为什么轴突在发育过程中有时会出错，（3）基因缺失和突变对布线的影响，以及（4）如何在损伤后鼓励轴突再生到适当的目标。这个项目的目标是通过建立梯度检测和轴突定向运动的计算模型来发展对梯度中轴突行为的机械理解。将研究两种类型的模型。第一种是基于梯度检测受限于受体结合过程中不可避免的随机噪声的想法。这些模型假设一个小的，球形的传感设备，并作出预测的最小可检测的梯度陡度，这样的传感设备可以检测。第二类模型提出了丝状伪足在轴突梯度感知和运动中的独特作用。该模型是基于这样的想法，即丝状伪足作为某种独立的传感设备，它是它们的组合动力学，确定梯度检测的阈值和轴突遵循的轨迹。该项目的一个关键组成部分是，计算建模将直接使用研究人员最近开发的一种新的定量实验分析进行测试和约束。该测定允许在胶原凝胶中建立精确控制形状的稳定分子梯度。所用系统将通过神经生长因子（NG）梯度引导背根神经节轴突。NG的不同陡度的指数形状梯度将用于确定轴突可以检测到的作为绝对浓度的函数的最小梯度陡度，并且将定量分析这些梯度中轴突的轨迹。