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

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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.