Integration of Experience-Induced Gene Expression and Circuit Functions

多位 PI：Xinyu Zhu、Meyer Jackson，威斯康星大学麦迪逊分校。 标题：经验诱导的基因表达和电路功能的整合 了解细胞、基因网络、神经回路和行为之间的复杂关系需要 可以探测不同类型神经元的分子组成、评估其特性并测试的技术 他们在更高级别职能中的作用。特定神经元群体中表达的基因决定了它们的 电特性以及这些特性及其突触连接共同塑造了电特性 神经回路的活动。一组由表达定义的神经元很好地说明了这一点 Ca2 结合蛋白小清蛋白 (PV)。 PV 中间神经元 (PVI) 是稀疏分布、快速放电的细胞， 向主要神经元提供反馈和前馈抑制。定义最明确的网络功能之一 PVI 的作用在于神经元网络及其相关振荡的协调。 PVI 夹带皮质 网络驱动伽马振荡（30-100 Hz）并控制其频率和强度。 PVI介导的 众所周知，伽马振荡在感觉处理、注意力、工作记忆和 认知。然而，控制 PVI 功能及其对伽马振荡影响的基因网络仍然存在 不清楚。 PVI 很容易受到环境条件和经验的影响。 PV 免疫反应性增加 经过对新环境的探索、环境丰富（EE）的培养以及自愿跑步 （虚拟现实）。这些变化发生在与认知相关的大脑区域，包括海马体、前额皮质、 和杏仁核。行为适应期间 PVI 变化的分子机制仍然存在 未知。尽管研究表明行为适应会影响伽马振荡，但 PVI 在 行为适应和伽马振荡之间的联系尚未建立。该应用程序需要一个 多学科方法来解决 PVI 如何促进行为适应这一基本问题。 我们的首要假设是，改变 PVI 细胞特性的基因表达变化将 改变网络振荡，使 PVI 成为行为适应的关键枢纽。我们将确定 行为适应是否调动 PVI 中的基因网络，并评估这些基因的贡献 PVI 生理学和伽马振荡网络。该项目结合了联合 PI 赵的独特专业知识 （神经发育的遗传调控）和杰克逊（神经生理学和神经回路）和罗伊共同 （系统生物学和机器学习）和罗森伯格（计算和系统神经科学）。通过整合 实验数据与基因网络分析和多细胞网络计算建模，这项工作 将揭示分子/细胞特性的变化如何影响神经回路的新兴特性。

Multi-PI: Xinyu Zhao, Meyer Jackson, University of Wisconsin-Madison. Title: Integration of Experience-Induced Gene Expression and Circuit Functions Understanding the complex relationships between cells, gene networks, neural circuits, and behavior requires techniques that can probe the molecular makeup of distinct types of neurons, evaluate their properties, and test their roles in higher level functions. Genes expressed within specific populations of neurons determine their electrical properties and these properties together with their synaptic connectivity collectively shape the electrical activity of neural circuits. This is especially well illustrated by a population of neurons defined by expression of the Ca2+ binding protein parvalbumin (PV). PV interneurons (PVIs) are sparsely distributed, fast-spiking cells that provide feedback and feedforward inhibition to principal neurons. One of the most well-defined network functions of PVIs is in the coordination of neuronal networks and their associated oscillations. PVIs entrain cortical networks to drive gamma oscillations (30-100 Hz) and control their frequency and strength. PVI-mediated gamma oscillations are known to have important roles in sensory processing, attention, working memory, and cognition. However, the gene networks that control PVI functions and their impact on gamma oscillations remain unclear. PVIs are readily modified by environmental conditions and experience. PV immunoreactivity increases after exploration of a novel environment, rearing under environmental enrichment (EE), and voluntary running (VR). These changes occur in brain regions associated with cognition, including hippocampus, prefrontal cortex, and amygdala. The molecular mechanisms underlying PVI changes during behavioral adaptation remain unknown. Although studies suggest that behavioral adaptions affect gamma oscillations, a role for PVIs in the link between behavioral adaption and gamma oscillations has not been established. This application takes a multidisciplinary approach to address the fundamental question of how PVIs contribute to behavioral adaptations. Our overarching hypothesis is that changes in gene expression that modify the cellular properties of PVIs will alter network oscillations, enabling PVIs to serve as a critical hub in behavioral adaptations. We will determine whether behavioral adaptation mobilizes networks of genes in PVIs, and assess the contributions of these networks to PVI physiology and gamma oscillations. This project combines the unique expertise of co-PIs Zhao (genetic regulation of neurodevelopment) and Jackson (neurophysiology and neural circuits) and co-Is Roy (system biology and machine learning) and Rosenberg (computational and system neuroscience). By integrating experimental data with gene network analysis and computational modeling of multicellular networks, this work will reveal how changes in molecular/cellular properties impact the emergent properties of neural circuits.