# Synaptic mechanisms of somatosensory circuit assembly

> **NIH NIH R01** · WASHINGTON UNIVERSITY · 2024 · $497,425

## Abstract

Abstract
Chronic pain is debilitating disease that affects more Americans than cancer, heart disease, and diabetes
combined. Despite this significant public health problem, effective treatments are scarce and commonly prescribed
opioids possess significant abuse liabilities. One possible reason for this poor translational success is that we still
lack a detailed understanding of how these circuits are connected to process sensory information and their plasticity
mechanisms. In our preliminary experiments we have identified important roles for trans-synaptic adhesion
molecules in regulating somatosensory synapse function in the spinal cord. Here we will determine the trans-
synaptic molecules that influence somatosensory synapse formation, understand how presynaptic adhesion
molecules in somatosensory neurons instruct the formation and function of native synapses in the spinal cord, and
establish their role in coordinating nociceptive circuit assembly to regulate pain behaviors. This proposal will use a
combination of in vitro synapse induction assays, conditional gene knockout and rescue approaches, peripheral
viral circuit tracing, optogenetic slice recordings, and somatosensory phenotyping to understand how trans-
synaptic adhesion molecules regulate somatosensory circuit assembly and function.

## Key facts

- **NIH application ID:** 10755356
- **Project number:** 5R01NS130046-02
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Bryan Copits
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $497,425
- **Award type:** 5
- **Project period:** 2023-01-01 → 2027-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10755356

## Citation

> US National Institutes of Health, RePORTER application 10755356, Synaptic mechanisms of somatosensory circuit assembly (5R01NS130046-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10755356. Licensed CC0.

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