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

用于检测内源性和外源性阿片类药物的配体选择性荧光生物传感器的高通量工程

基本信息

批准号：
10635413
负责人：
Andre Berndt
金额：
$ 251.02万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-15 至 2026-04-14
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10635413
关键词：
Acceleration Adaptive Behaviors Address Affinity Anxiety Area BRAIN initiative Behavior Behavioral Benchmarking Biology Biosensor Brain Brain region Cells Chimeric Proteins Coupling Data Detection Development Disease Drug Screening Endorphins Engineering Enhancers Enkephalins Ensure Fiber Fluorescence Microscopy Future Genetic Goals Green Fluorescent Proteins In Vitro Invertebrates Knock-out Learning Libraries Ligand Binding Ligands Link Location Measurement Memory Methods Modeling Molecular Molecular Conformation Monitor Mus Mutagenesis Neurons Neuropeptides Neurosciences Research Nucleus Accumbens Opioid Opioid Peptide Opioid Receptor Optics Organism Pain Partner in relationship Pathologic Performance Photometry Physiological Population Positioning Attribute Property Protein Engineering Proteins Randomized Reporter Research Research Personnel Signal Transduction Slice Specificity Speed Stress Structure of nucleus infundibularis hypothalami Testing Time Variant Vertebrates Work attenuation biophysical properties cell type combat delta opioid receptor design endogenous opioids experimental study feeding high throughput screening improved in vivo in vivo monitoring innovation innovative technologies mu opioid receptors neuronal circuitry neurotransmission next generation novel novel therapeutic intervention opioid abuse opioid withdrawal optogenetics pharmacologic prototype receptor sensor temporal measurement tool development two-photon

项目摘要

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.

项目摘要 /摘要神经元电路的神经肽调节与许多关键行为（例如探索，压力，记忆形成，学习和许多病理生理状况。很遗憾，众所周知，神经肽很难理解，因为许多方法没有很好地分离神经肽在大脑内的空间和时间上都能准确起作用。遗传编码的荧光蛋白传感器可以通过高空间和时间分辨率以及细胞类型的特异性提供精确的监测。但是，神经肽传感器工程的重要障碍是电流的缓慢吞吐量工程方法。我们在此提案中的核心目标是专门开发高级传感器通过实现大信号幅度和生理 - 相关的配体结合亲和力。同时，我们将建立一个有效的神经肽框架传感器工程。我们将利用我们的新工程平台筛选数千个传感器变体分钟，效率高。我们将迅速识别具有所需振幅和敏感性的传感器用于特定电路特异性阿片类药物检测。此外，我们将在唤起的模型中表征所有传感器内源性阿片类药物在行为小鼠的大脑中释放。我们已经设计了一个阿片类药物传感器具有改进的生物物理特性的原型，我们将用作这些范式的阈值。我们的目标是在神经元电路中生成多个特定的传感器，以使用已知的神经元电路阿片受体和/或肽的存在。在AIM 1中，我们将创建大型传感器变体库以增加信号幅度可抵抗体内应用中预期的信号衰减。我们将针对特定具有随机诱变的残基促进传感器种群向主动构象的过渡。此外，我们将增加阿片类药物传感和报告域之间的变构耦合。在AIM 2中，我们将生成具有特定配体选择曲线的传感器，例如Enkephalin在内啡肽等上等等。我们将生成针对配体结合口袋内或附近残留物的库。在高中期间，我们将应用多个配体吞吐量屏幕以识别具有所需配体选择性的传感器。在AIM 3中，我们将验证传感器体内和唤起阿片类药物释放的行为。这包括监测内源性阿片类肽使用细胞类型和电路类型特异性的各个大脑区域中的纤维光度法动力学。这个建议很重要，因为神经肽是神经活性的关键调节剂，但它们的动态作用是由于缺乏适当的体内监测，因此无法很好地理解。我们的项目具有创新性，因为提出的方法将在设计高效的神经肽传感器蛋白时提供最快的吞吐量。此外，阿片类药物传感器可能是确定状态依赖性增强子的神经元机制的关键在阿片类药物滥用的条件下，压力和焦虑或焦虑或探测脑回路等行为。