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

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2024 · $734,804

## Abstract

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.

## Key facts

- **NIH application ID:** 10851794
- **Project number:** 5R01DA056599-03
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** KEVIN T BEIER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $734,804
- **Award type:** 5
- **Project period:** 2022-09-01 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10851794

## Citation

> US National Institutes of Health, RePORTER application 10851794, Circuitry dynamics underlying opioid-dependence: Integrating structural, functional, and transcriptomic mechanisms (5R01DA056599-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10851794. Licensed CC0.

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