High-throughput engineering of ligand-selective fluorescent biosensors for detecting endogenous and exogenous opioids

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PROJECT SUMMARY / ABSTRACT Neuropeptide modulation of neuronal circuits is strongly linked to many crucial behaviors such as exploration, stress, memory formation, learning, and many pathophysiological conditions. Unfortunately, neuropeptides are notoriously difficult to understand because many methods are not well-positioned to isolate neuropeptide function accurately in space and time within the brain. Genetically-encoded fluorescent protein sensors could provide precise monitoring with high-spatial and temporal resolution and cell-type specificity. However, a significant obstacle in the engineering of neuropeptide sensors is the slow throughput of current engineering approaches. Our central goal in this proposal is to develop advanced sensors specifically for monitoring opioid neuropeptides dynamics in vivo by achieving large signal amplitudes and physiological- relevant ligand binding affinities. At the same time, we will establish an efficient framework for neuropeptide sensor engineering. We will utilize our new engineering platform to screen thousands of sensor variants in a few minutes and with high efficiency. We will rapidly identify sensors with the required amplitudes and sensitivities for circuit-specific opioid detection in vivo. Furthermore, we will characterize all sensors in models of evoked endogenous opioid release in the brain of behaving mice. We have already engineered an opioid sensor prototype with improved biophysical properties that we will use as a threshold in these paradigms. Our objective is to generate multiple, specific sensors for advanced detection capabilities in neuronal circuits with a known presence of opioid receptors and/or peptides. In Aim 1, we will create large sensor variant libraries to increase signal amplitudes to combat the anticipated signal attenuation in in vivo applications. We will target specific residues with randomized mutagenesis to facilitate the transition of sensor populations into active conformations. Additionally, we will increase allosteric coupling between opioid sensing and reporter domains. In Aim 2, we will generate sensors with specific ligand-selectivity profiles, e.g. enkephalin over endorphin, etc. We will generate libraries targeting residues in or near the ligand-binding pocket. We will apply multiple ligands during our high- throughput screens to identify sensors with the desired ligand-selectivity. In Aim 3, we will validate our sensors in vivo and during behaviors that evoke opioid release. That includes monitoring endogenous opioid peptide dynamics using fiber photometry in various brain regions with cell-type and circuit-type specificity. This proposal is significant because neuropeptides are critical modulators of neuronal activity, but their dynamic actions are not well understood due to the lack of appropriate in vivo monitoring. Our project is innovative because the proposed approach will provide the fastest throughput in designing highly efficient neuropeptide sensor proteins. In addition, opioid sensors could be the keys to identify neuronal mechanisms of state-dependent enhancers for behaviors such as stress and anxiety or to probe brain circuits under conditions of opioid abuse.