Real-time imaging of opioid neuromodulation with novel genetically encoded fluorescent sensors

PROJECT SUMMARY/ABSTRACT Opioids are the most widely used analgesics for chronic pain and post-operative care. However, opioid misuse has resulted in a national overdose crisis fueled by untreated opioid use disorder (OUD) and the infiltration of the potent synthetic opioid, fentanyl, into the drug supply. Fentanyl primarily signals at the µ-opioid receptor (µOR), a G protein-coupled receptor (GPCR) expressed throughout many subcortical brain regions responsible for regulating pain perception, reward, stress, and emotion. However, the pharmacological and spatiotemporal profile of fentanyl in the brain is currently poorly understood. Traditional tools to monitor opioid distribution lack specificity and spatiotemporal resolution. Previously, the Berndt Lab developed a fluorescent opioid sensor, μMASS, which couples real-time opioid detection at the µOR to an increased fluorescence response. However, like the µOR, μMASS detects a variety of opioids, including endogenous met- enkephalin and exogenous fentanyl, making it difficult to discern signals between specific ligands. My goal is to engineer a fentanyl-specific fluorescent opioid sensor to investigate fentanyl pharmacology and pharmacokinetics in real-time. Previously, I combined AI-guided structure determination and molecular docking to study µMASS-fentanyl binding in silico, and observed µMASS did not maintain a critical salt bridge with amino acid D1473.32. Mutating this residue to a glutamate (D1473.32E) rendered the sensor specific only for fentanyl, which I dubbed FentMASS1.0. To engineer a next-generation fentanyl sensor with enhanced sensitivity, specificity, and expression, in Aim 1.1 I will bolster my current approach using updated structure prediction models and molecular dynamics simulations to observe sensor-fentanyl interactions and identify new mutation targets. In Aim 1.2 improved sensor variants will be rapidly generated and screened using a high-throughput protein engineering approach established in the Berndt Lab and optimized for neuronal expression in Aim 1.3. Finally, in Aim 2 I will validate the optimized fentanyl sensor (or FentMASS1.0) in µOR-expressing brain regions in ex vivo brain slices and determine fentanyl pharmacology in real-time. In the end I will have an enhanced µOR-based fentanyl sensor with optimized expression in neuronal tissue, which can then be applied to study fentanyl pharmokinetics across brain regions in vivo in freely behaving animals. This proposal is significant because there is currently no available µOR-based sensor to study fentanyl pharmacology with reliable sensitivity, kinetics, and neuronal expression. The approach is innovative as it combines AI-guided structure determination and in silico molecular modeling in combination with next-generation high-throughput sensor engineering techniques to rapidly optimize a GPCR-based sensor. This approach and resulting sensor technology will aid in studies to characterize the progression of fentanyl induced OUD while laying the groundwork for developing future specific opioid sensors.