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

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.

Grant Number: 5R01DA054374-05
NIH Institute/Center: NIH
Principal Investigator: Julie Blendy