How do neurons maintain mitochondrial homeostasis in vivo?

神经元如何维持体内线粒体稳态？

基本信息

批准号：
10706587
负责人：
Erin L Barnhart
金额：
$ 41.13万
依托单位：
COLUMBIA UNIV NEW YORK MORNINGSIDE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-19 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10706587
关键词：
Active Biological Transport Affect Alzheimer&apos s Disease Architecture Axon Binding Biological Models Cell Culture System Cell Culture Techniques Cells Complement Complex Data Set Defect Dendrites Diffusion Distal Drosophila genus Ensure Genetic Goals Health Homeostasis Individual Label Link Maintenance Measurement Measures Mitochondria Mitochondrial Proteins Modeling Molecular Morphology Motion Movement Neurodegenerative Disorders Neurons Organism Outcome PINK1 gene Parkin Parkinson Disease Pattern Physiological Play Population Role Spatial Distribution Stress System Testing Time Vision Visual System cell motility cell type experimental study fly imaging approach in vivo in vivo imaging in vivo imaging system innovation insight mathematical model meter millisecond mitochondrial autophagy nanometer neuronal cell body oxidative damage predictive modeling prevent self organization spatiotemporal tool visual stimulus

项目摘要

Project Summary Mitochondria are critical for neuronal function and must be reliably distributed throughout the entire neuron. To maintain healthy, properly distributed mitochondria, neurons must coordinate mitochondrial dynamics, including motility, fission and fusion, and degradation, over space and time. The broad goal of this proposal is to define mechanisms for spatiotemporal control of mitochondrial dynamics in neurons in vivo. To that end, we will employ an innovative in vivo imaging approach to measure mitochondrial dynamics in well-defined motion vision neurons in Drosophila. By combining our in vivo measurements with mathematical modeling, we will gain mechanistic insight into how neurons maintain mitochondrial homeostasis at the systems level. We propose three specific aims. In Aim 1 we will determine how neurons maintain steady-state mitochondrial distribution patterns despite high levels of mitochondrial motility within complex neuronal morphologies. Specifically, we will test the hypothesis that neuronal architectures are optimized for the robust self- organization of specific mitochondrial localization patterns. We will use experimental measurements of in vivo mitochondrial motility and neuronal branching patterns to develop a quantitative model linking large-scale mitochondrial distributions to branch scaling rules. We will test this model by predicting mitochondrial localization patterns from experimental measurements of neuronal architecture across morphologically and functionally diverse Drosophila visual system neurons. We will then test our model predictions by comparing to ground truth measurements of mitochondrial distributions in EM datasets. In Aim 2 we will investigate how proper spatiotemporal control of mitochondrial fission and fusion contributes to the maintenance of healthy mitochondria in distal axons and dendrites. We will test the hypothesis that neurons optimize fission and fusion rates to both maximize complementation across mitochondria and ensure efficient delivery of newly- synthesized mitochondrial proteins to distal axons and dendrites. Finally, in Aim 3 we will probe the relationship between neuronal activity and mitophagy rates in neurons in vivo. Altogether, this proposal promises to provide a critical mechanistic framework for understanding how neurons regulate mitochondrial movement, fission and fusion, and degradation to maintain healthy, properly distributed mitochondrial populations in vivo, providing new insight into the molecular and cellular basis for neurodegenerative diseases.

项目概要线粒体对于神经元功能至关重要，必须可靠地分布在整个神经元中。到维持健康、适当分布的线粒体，神经元必须协调线粒体动力学，包括随空间和时间的运动、裂变和聚变以及降解。该提案的总体目标是定义体内神经元线粒体动力学时空控制的机制。为此，我们将采用创新的体内成像方法来测量明确运动中的线粒体动力学果蝇的视觉神经元。通过将我们的体内测量与数学模型相结合，我们将深入了解神经元如何在系统水平上维持线粒体稳态。我们提出三个具体目标。在目标 1 中，我们将确定神经元如何维持稳态线粒体尽管在复杂的神经元形态中线粒体运动水平很高，但分布模式仍然存在。具体来说，我们将测试神经元架构针对稳健的自学习进行优化的假设。特定线粒体定位模式的组织。我们将使用体内实验测量线粒体运动和神经元分支模式，以开发连接大规模的定量模型线粒体分布到分支缩放规则。我们将通过预测线粒体来测试这个模型来自形态学和神经元结构实验测量的定位模式功能多样的果蝇视觉系统神经元。然后，我们将通过比较来测试我们的模型预测 EM 数据集中线粒体分布的地面实况测量。在目标 2 中，我们将研究如何线粒体裂变和融合的适当时空控制有助于维持健康远端轴突和树突中的线粒体。我们将测试神经元优化裂变和融合的假设率，以最大限度地提高线粒体的互补性，并确保新的有效传递将线粒体蛋白合成到远端轴突和树突。最后，在目标 3 中我们将探讨这种关系体内神经元的神经元活性和线粒体自噬率之间的关系。总而言之，该提案承诺提供理解神经元如何调节线粒体运动、裂变和线粒体运动的关键机制框架融合和降解以维持体内健康、适当分布的线粒体群体，提供对神经退行性疾病的分子和细胞基础的新见解。