Mapping opioid-dependence state transitions across structural, functional, and transcriptomic topologies

项目总结 阿片成瘾是一种慢性进行性疾病，助长了目前美国阿片类药物过量死亡的流行。 多年来，人们投入了大量的研究工作来了解生物作用。 阿片受体和开发治疗疼痛和对抗阿片类药物的新一代合成阿片类药物 上瘾。然而，鉴于当代和新的神经科学技术的进步，我们有 超越阿片受体(MORS)思考的工具，以开发改进的OUD疗法。这项提议旨在 探讨脑内内源性MOR表达神经回路的构筑和功能 确定这些回路如何维持细胞依赖性，并驱动整个大脑的适应不良可塑性 OUD周期的不同阶段。在四个相辅相成的目标中，我们将首先绘制出转变的结构和 用病毒遗传学和组织清除方法鉴定阿片能网络的功能连通性 单突触对戒断激活的MOR表达细胞和轴突输出投射的影响 阿片类药物的接触和戒除。然后，我们将把这些输入/输出映射与像元类型信息集成在一起 用超多路3D原位杂交技术改变依赖网络中的基因表达 生成数百个依赖相关基因的解剖定位，针对细胞类型和追溯- 已标记为连接。最后，为了揭示核心区内表达MOR的细胞是如何在 阿片类药物的实时暴露，我们将使用微型头盔显微镜来成像种群活动- 在细胞分辨率--跨越数周的阿片类药物暴露和戒断。我们的模型将提供正式的 随着阿片类药物重复暴露和基因表达的演变，其活性、连接性和基因表达的概述 撤回，我们的数据集将在生成时公开提供。在这些实验中架起桥梁 并为我们的分析提供一个通用框架，我们将采用网络控制理论来 确定驱动阿片依赖状态之间转换的大脑节点，以确定潜在的候选者 这不成比例地推动了每个州的发展。

PROJECT SUMMARY Opioid addiction is a chronic, progressive disorder that fuels the current US epidemic of opioid overdose deaths. Over the years, a tremendous amount of research effort has been devoted to understanding the biological roles of opioid receptors and developing newer generations of synthetic opioids to treat pain and combat opioid addiction. However, given the advancement of contemporary and novel neuroscience technologies, we have the tools to think beyond mu-opioid receptors (MORs) to develop improved OUD therapeutics. This proposal aims to investigate the architecture and function of endogenous MOR-expressing neural circuits in the brain and to determine how these circuits maintain cellular dependence and drive brain-wide maladaptive plasticity across different stages of the OUD cycle. In four complementary aims, we will first map the shifting structural and functional connectivity of opioidergic networks using viral-genetic and tissue clearing methods to identify monosynaptic inputs to withdrawal-active MOR-expressing cells and axonal output projections, as a function of opioid exposure and abstinence. We will then integrate these input/output maps with cell-type information and gene expression changes within dependence networks using hyper-multiplexed 3D in situ hybridizations to generate the anatomic localization of hundreds of dependence-related genes, targeted to cell types and retro- labeled connections. Finally, to reveal how MOR-expressing cells within core regions are modulated during opioid exposure in real-time, we will use miniature head-mounted microscopes to image the population activity— at cellular resolution—across weeks of opioid exposure and withdrawal. Our models will provide formal summaries of activity, connectivity, and gene expression as they evolve with repetitive opioid exposure and withdrawal, and our datasets will be made publicly available as they are generated. To bridge these experimental measurements and provide a common framework for our analyses, we will adopt Network Control Theory to identify brain nodes that drive the transition between opioid dependence states to identify potential candidates that disproportionately drive each state.