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.

项目概要/摘要神经元回路的神经肽调节与许多关键行为密切相关，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，因为许多方法无法很好地分离神经肽神经肽在大脑内的空间和时间上准确地发挥作用。基因编码荧光蛋白传感器可以提供具有高空间和时间分辨率以及细胞类型特异性的精确监测。然而，神经肽传感器工程中的一个重大障碍是电流的缓慢吞吐量。工程方法。我们在该提案中的中心目标是开发专门用于通过实现大信号幅度和生理学监测阿片类神经肽体内动态相关的配体结合亲和力。同时，我们将建立高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在短时间内筛选数千种传感器变体分钟，效率高。我们将快速识别具有所需幅度和灵敏度的传感器用于体内电路特异性阿片类药物检测。此外，我们将在诱发模型中描述所有传感器的特征行为小鼠大脑中内源性阿片类药物的释放。我们已经设计了阿片类药物传感器具有改进的生物物理特性的原型，我们将在这些范例中将其用作阈值。我们的目标是生成多个特定的传感器，以在具有已知的神经元电路中提供高级检测能力阿片受体和/或肽的存在。在目标 1 中，我们将创建大型传感器变体库以增加信号幅度以对抗体内应用中预期的信号衰减。我们将针对具体具有随机诱变的残基，以促进传感器群体转变为活性构象。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。在目标 2 中，我们将生成具有特定配体选择性特征的传感器，例如脑啡肽超过内啡肽等。我们将产生靶向配体结合袋中或附近残基的文库。我们将在我们的高通量筛选来识别具有所需配体选择性的传感器。在目标 3 中，我们将验证我们的传感器体内和引起阿片类药物释放的行为期间。这包括监测内源性阿片肽使用纤维光度测定法在具有细胞类型和电路类型特异性的各个大脑区域中进行动力学研究。这个提议很重要，因为神经肽是神经元活动的关键调节剂，但它们的动态作用是由于缺乏适当的体内监测，尚不清楚。我们的项目具有创新性，因为所提出的方法将为设计高效神经肽传感器蛋白提供最快的吞吐量。此外，阿片类传感器可能是识别状态依赖性增强剂的神经元机制的关键压力和焦虑等行为或在阿片类药物滥用的情况下探测大脑回路。