Circuitry dynamics underlying opioid-dependence: Integrating structural, functional, and transcriptomic mechanisms

阿片类药物依赖性的电路动力学：整合结构、功能和转录组机制

• 批准号: 10838996
• 负责人: KEVIN T BEIER
• 金额: $ 5.97万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10838996
• 关键词: Abstinence; Acceleration; Address; Adopted; Anatomy; Architecture; Brain; Brain region; Cell Nucleus; Cells; Chronic; Data Set; Dependence; Development; Future; Gene Expression; Genes; Head; In Situ Hybridization; Label; Link; Maintenance; Maps; Measurement; Methods; Microscope; Neuroanatomy; Neurons; Opiate Addiction; Opioid Peptide; Opioid Receptor; Process; Relapse; Research; Research Personnel; Resolution; Therapeutic; Time; Tissues; Withdrawal; addiction; cell type; control theory; endogenous opioids; improved; neural circuit; neuroimaging; opioid exposure; opioid withdrawal; response; searchable database; transcriptomics; virus genetics

PROJECT SUMMARY A range of cellular and circuit-level adaptations develops in response to chronic opioid exposure, which are strongly linked to several facets of opioid addiction: tolerance, withdrawal and processes that may contribute to compulsive use and relapse. However, we still do not have a comprehensive picture of the dynamic connections and activities of neuronal networks in the brain that express the opioid receptors and peptides. Therefore, a critical need exists to map the global cell-type identity, transcriptomic trajectory, shifting connectivity, and ensemble activity of the key opioidergic networks underlying the onset and maintenance of cellular dependence, and withdrawal. This proposal aims to investigate the architecture and function of endogenous MOR-expressing neural circuits in key cortical and subcortical brain regions, in order 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 all MOR-expressing, as well as withdrawal-active MOR-expressing neurons, as a function of opioid exposure and abstinence. We will then integrate these dynamic neuroanatomical maps with cell-type information and gene expression changes by combing single-nuclei sequencing and spatial cellular-resolution transcriptomics via hyper-multiplexed in situ hybridizations to generate the anatomic localization of hundreds of dependence-related genes, targeted to cell types and retro-labeled connections. Lastly, to reveal how MOR-expressing cells within the cortical and subcortical target regions are modulated during opioid exposure in real-time, we will use miniature head-mounted microscopes to image the neural ensemble activities across weeks of opioid exposure and withdrawal. 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. Our datasets will provide formal summaries and a publicly available, searchable database logging the activity, connectivity, and gene expression as they evolve with repetitive opioid exposure, withdrawal, and abstinence.

项目总结 一系列细胞和电路水平的适应是对慢性阿片类药物暴露的反应，这些适应是 与阿片成瘾的几个方面密切相关：耐受性、戒断和可能有助于 强迫使用和故态复萌。然而，我们仍然没有对动态连接有一个全面的了解 以及大脑中表达阿片受体和多肽的神经网络的活动。因此，a 迫切需要绘制全球细胞类型身份、转录轨迹、变化的连接性和 细胞依赖发生和维持的关键阿片能网络的整体活动， 和戒烟。这项建议旨在研究内源性MOR表达的结构和功能 大脑关键皮质和皮质下区域的神经回路，以确定这些回路如何维持 细胞依赖性，并在OUD周期的不同阶段驱动全脑的适应不良可塑性。在四年内 相辅相成的目标，我们将首先映射阿片能网络不断变化的结构和功能连接性 使用病毒遗传和组织清除方法来识别所有MOR表达的单突触输入 作为戒断活跃的MOR表达神经元，作为阿片类药物暴露和戒断的函数。到时候我们会的 通过以下方式将这些动态神经解剖图与细胞类型信息和基因表达变化相结合 超多重原位杂交结合单核测序和空间细胞分辨转录技术 杂交以产生数百个针对细胞的依赖相关基因的解剖定位 类型和追溯标记的连接。最后，为了揭示大脑皮质和大脑皮质中表达MOR的细胞是如何 在阿片类药物暴露期间，皮质下靶区被实时调制，我们将使用微型头盔 显微镜可以对阿片类药物暴露和戒断数周的神经整体活动进行成像。架桥 这些实验测量并为我们的分析提供了一个通用框架，我们将采用网络 控制理论识别驱动阿片依赖状态间转换的脑结节识别 不成比例地推动每个州的潜在候选人。我们的数据集将提供正式的摘要和 公开可用的、可搜索的数据库，记录它们进化过程中的活动、连通性和基因表达 反复接触阿片类药物、戒断和戒断。