Integration of Experience-Induced Gene Expression and Circuit Functions

多派：赵新宇，迈耶·杰克逊，威斯康星大学麦迪逊分校。 标题：经验诱导的基因表达和回路功能的整合 理解细胞、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、评估其特性并进行测试的技术 他们在更高层次的职能中的作用。在特定神经元群体中表达的基因决定了它们的 电学属性和这些属性以及它们的突触连接共同塑造了电学 神经回路的活动。这一点从一组神经元上得到了很好的说明，这些神经元是由 钙结合蛋白小白蛋白(PV)。PV中间神经元(PVI)是稀疏分布的快速尖峰细胞， 向主神经元提供反馈和前馈抑制。定义最明确的网络功能之一 下丘脑室隔核的主要功能是协调神经元网络及其相关的振荡。PVIs累及大脑皮层 用于驱动伽马振荡(30-100赫兹)并控制其频率和强度的网络。变坡点介导的 众所周知，伽马振荡在感觉处理、注意力、工作记忆和 认知力。然而，控制PVI功能及其对伽马振荡的影响的基因网络仍然存在 不清楚。变坡点很容易受到环境条件和经验的影响。PV免疫反应性增加 在探索了新的环境后，在环境富集化(EE)下饲养，并自愿跑步 (VR)。这些变化发生在与认知相关的大脑区域，包括海马体、前额叶皮质、 还有杏仁核。在行为适应过程中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。此应用程序需要 多学科方法，以解决PVI如何促进行为适应这一根本问题。 我们最重要的假设是，改变PVI细胞特性的基因表达的变化将 改变网络振荡，使PVI在行为适应中充当关键枢纽。我们将决定 行为适应是否调动了下丘脑的基因网络，并评估了这些因素的贡献 与PVI生理和伽马振荡有关的网络。这个项目结合了合作PIS赵的独特专业知识 (神经发育的遗传调节)和杰克逊(神经生理学和神经回路)和合著者罗伊 (系统生物学和机器学习)和罗森博格(计算和系统神经科学)。通过集成 实验数据用基因网络分析和多细胞网络的计算建模，这项工作 将揭示分子/细胞属性的变化如何影响神经电路的新属性。

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.