How do neurons coordinate alternative energy sources to meet the demands of computation?

项目摘要 神经元如何协调替代能源， 神经计算的需求 主要研究者：克兰丁 大脑在能量上是昂贵的，这是一种神经活动固有的代谢成本， 大脑如何计算的定义特征。由于这种能源密集型操作，主要方法 用于测量人类神经活动的变化，例如功能性磁共振成像（fMRI）， 实际上是通过测量血流的变化来推断神经活动，血流是局部能量消耗的代表。 此外，许多改变能量生产效率和平衡的疾病的特征是： 严重的脑功能缺陷然而，神经活动如何在神经元水平上塑造能量产生， 单个细胞，电路和整个大脑只是不完全理解，特别是在 积极的感觉和行为。 该领域的长期工作，基于单细胞和人类神经成像的体外模型， 揭示了不同的能量产生途径如何对神经活动的变化做出反应， 神经活动的增加会导致核心细胞能量货币ATP的消耗。我们最近的工作使用 行为果蝇的完整大脑建立在这些结果的基础上，并揭示了一个新的元素之间的耦合 新陈代谢和能量产生，即细胞利用当前的神经活动水平来预测未来的神经活动。 能源需求。因此，该项目旨在回答神经代谢的反应性和预测性因素如何影响神经代谢的影响。 能量耦合作用 拟议的工作集中在三个关键问题上。首先，不同的神经元类型， 完整大脑中的活动模式，显示它们对代谢负荷的反应和预测的差异？ 第二，神经元如何通过两种替代能源平衡能量产生，即糖酵解和代谢。 氧化磷酸化，既反应代谢成本和预测未来的支出？最后， 这些代谢负荷在行为动物检测感官刺激的电路中协调？我们 假设由于神经元活动水平在不同细胞类型之间存在显著差异，并且由于糖酵解 氧化磷酸化能以不同的速率和效率产生ATP， 隔室，神经元和电路在替代能源之间动态切换， 计算需求和预测未来的代谢需求。为了验证这一假设，我们建议使用两个 神经活动和代谢通量的荧光传感器的光子成像，结合遗传和 特定细胞类型的光遗传学扰动，使用成年果蝇脑作为模型。 由于许多参与能量代谢的基因在人类之间是进化保守的， 和苍蝇，加深我们对神经活动如何与体内能量代谢耦合的理解， 增加我们对代谢疾病的神经影响的理解，可能开辟新的治疗方法， 未来调查的途径。

Project Summary How do neurons coordinate alternative energy sources to meet the demands of neural computation? PI:Clandinin The brain is energetically expensive, a metabolic cost that is intrinsic to neural activity and hence a defining feature of how the brain computes. As a result of this energy intensive operation, the main methods for measuring changes in neural activity in humans, such as functional magnetic resonance imaging (fMRI), actually infer neural activity by measuring changes in blood flow, a proxy for local energy consumption. Moreover, many diseases that alter the efficiency and balance of energy production are characterized by profound deficits in brain function. However, how neural activity shapes energy production at the level of individual cells, circuits and across the brain are only incompletely understood, particularly in the context of active sensation and behavior. Longstanding work in the field, based in vitro models of single cells and human neuroimaging, have revealed how different pathways for energy production react to changes in neural activity, responding when increases in neural activity cause depletion of ATP, a core cellular energy currency. Our recent work using the intact brain of the behaving fruit fly build on these results, and revealed a new element to the coupling between metabolism and energy production, namely that cells use current levels of neural activity to predict future energy needs. Thus, this project seeks to answer how the reactive and predictive elements of neural-metabolic energy coupling interact. The proposed work focuses on three key questions. First, do different neuron types, with distinct patterns of activity in the intact brain, display differences in how they react to, and predict, metabolic load? Second, how do neurons balance energy production via two alternative energy sources, namely glycolysis and oxidative phosphorylation, to both react to metabolic cost and predict future expenditures? Finally, how are these metabolic loads coordinated across circuits in behaving animals detecting sensory stimuli? We hypothesize that because neuronal activity levels differ substantially across cell types, and because glycolysis and oxidative phosphorylation can produce ATP with different latencies and efficiencies, subcellular compartments, neurons and circuits dynamically switch between alternative energy sources to both react to computational demand and predict future metabolic need. To test this hypothesis, we propose to use two photon imaging of fluorescent sensors of neural activity and metabolic flux, combined with genetic and optogenetic perturbations of specific cell types, using the adult fruit fly brain as a model. As many of the genes involved in energy metabolism are evolutionarily conserved between humans and flies, deepening our understanding of how neural activity couples to energy metabolism in vivo will increasing our understanding of the neural impacts of metabolic diseases, possibly opening new therapeutic avenues for future investigation.