Synthetic biology for the chemogenetic manipulation of pain pathways
用于疼痛通路化学遗传学操纵的合成生物学
基本信息
- 批准号:9895148
- 负责人:
- 金额:$ 19.25万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-09-15 至 2021-08-31
- 项目状态:已结题
- 来源:
- 关键词:Absence of pain sensationAffinityAnalgesicsAnimal ModelBiological AssayBiological SciencesBiologyBrainCNR2 geneCannabidiolCannabinoidsCellsChronicClinicCollaborationsComplexCoupledCouplesCultured CellsDevelopmentDirected Molecular EvolutionDisciplineDoseElectrophysiology (science)EmulsionsEngineeringEnzymesFlying body movementFutureG-Protein-Coupled ReceptorsGenesIndividualIon ChannelLeadLearningLibrariesLigandsMemoryMethodsMinorModelingMovementMusNeural PathwaysNeurobiologyNeuronsNeurophysiology - biologic functionOilsPainPain managementPathway interactionsPatientsPerformancePheromonePolymerasePreventionProductionProteinsRadarReceptor SignalingSchemeSensory ReceptorsSignal PathwaySignal TransductionTestingVariantWaterWorkYeastsaddictioncannabinoid drugcombatdesigner receptors exclusively activated by designer drugsdopaminergic neurongene therapygenetic regulatory proteinmouse modelnanonanomolarnovelpain modelpain reliefreceptorrelating to nervous systemselective expressionside effectsynthetic biologytheoriestool
项目摘要
Project Summary
The methods of synthetic biology have transformed practice throughout the biological sciences, but have yet to
find wide application in neurobiology. This is in part because many signaling receptors and pathways in the
brains are shared, limiting the latitude for narrowly targeted engineering strategies. To create a wider range of
tools for selective cell modulation, we propose to develop directed evolution methods that will generate
orthogonal neural receptors that respond to cannabinoids and offer multiple different chemogenetic control points
across the brain and thereby open the way to a synthetic neurobiology. The proposed methods should yield very
High Affinity receptors, that have Validated Orthogonalities for their receptor:ligand Couples. Our HAVOCs will
stand in contrast to current DREADD and DART approaches in that they will allow the use of natural effectors,
but at much lower concentrations, in essence flying below the ‘radar cover’ of endogenous receptors in the brain.
In particular, we will use HAVOCs to examine the gate theory of pain, and in consequence serve as a surrogate
model for targeted nano-dosing strategies for cannabinoids to safely promote analgesia and combat addiction.
As a starting point for the development of nano-dosing strategies, we will focus on the CB2 receptor, which is
sparsely expressed in the brain, but which has known functions in inhibiting dopaminergic neurons. Using our
novel directed evolution method, Compartmentalized Partnered Replication (CPR), we will initially evolve
individual variants of CB2 that can interact with high affinity with the cannabinoids b-caryophyllene, cannabidiol
(CBD), and other minor cannabinoids (Aim 1). We will proof the utility of these compounds and their evolved
receptors with isolated neurons and directly in a mouse model for pain (Aim 2). While movement to the clinic will
ultimately require introduction of novel receptors into patients, likely via gene therapies, the ability to target
protein production in particular neural pathways may provide one of the few viable methods for the chronic
treatment of pain. Into the future, the directed evolution strategies we have developed are fungible between
multiple different receptors and receptor types, and we suggest that HAVOCs may therefore serve as
generalizable neurotechnological tools to understand and manipulate a variety of neural functions.
项目概要
合成生物学的方法已经改变了整个生物科学的实践,但尚未
在神经生物学中得到广泛应用。这部分是因为许多信号受体和通路
大脑是共享的,限制了目标狭窄的工程策略的自由度。为创造更广泛的
作为选择性细胞调节的工具,我们建议开发定向进化方法,该方法将产生
对大麻素做出反应并提供多种不同化学遗传学控制点的正交神经受体
穿过大脑,从而为合成神经生物学开辟道路。所提出的方法应该产生非常
高亲和力受体,已验证其受体的正交性:配体对。我们的破坏将会
与当前的 DREADD 和 DART 方法形成对比,因为它们允许使用自然效应器,
但浓度要低得多,本质上是在大脑内源性受体的“雷达罩”下方飞行。
特别是,我们将使用 HAVOC 来检查疼痛的门理论,并因此作为替代品
大麻素靶向纳米剂量策略的模型,以安全地促进镇痛和对抗成瘾。
作为开发纳米剂量策略的起点,我们将重点关注 CB2 受体,它是
在大脑中稀疏表达,但已知其具有抑制多巴胺能神经元的功能。使用我们的
新颖的定向进化方法,分区合作复制(CPR),我们将首先进化
CB2 的各个变体可以与大麻素 b-石竹烯、大麻二酚以高亲和力相互作用
(CBD)和其他次要大麻素(目标 1)。我们将证明这些化合物的实用性及其进化
受体与分离的神经元并直接在小鼠疼痛模型中(目标 2)。虽然前往诊所将
最终需要将新的受体引入患者体内,可能通过基因疗法,靶向的能力
特别是神经通路中的蛋白质生产可能为慢性病提供少数可行的方法之一
治疗疼痛。未来,我们开发的定向进化策略可以在
多种不同的受体和受体类型,因此我们建议 HAVOC 可以作为
用于理解和操纵各种神经功能的通用神经技术工具。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Andrew D Ellington其他文献
Endowing cells with logic and memory
赋予细胞逻辑和记忆
- DOI:
10.1038/nbt.2573 - 发表时间:
2013-05-08 - 期刊:
- 影响因子:41.700
- 作者:
Andre C Maranhao;Andrew D Ellington - 通讯作者:
Andrew D Ellington
Overview of Receptors from Combinatorial Nucleic Acid and Protein Libraries
组合核酸和蛋白质文库的受体概述
- DOI:
- 发表时间:
2007 - 期刊:
- 影响因子:0
- 作者:
Andrew D Ellington - 通讯作者:
Andrew D Ellington
Back to the future of nucleic acid self-amplification
回到核酸自扩增的未来
- DOI:
10.1038/nchembio0409-200 - 发表时间:
2009-04-01 - 期刊:
- 影响因子:13.700
- 作者:
Andrew D Ellington - 通讯作者:
Andrew D Ellington
Molecular evolution picks up the PACE
分子进化加快了步伐
- DOI:
10.1038/nbt.1884 - 发表时间:
2011-06-07 - 期刊:
- 影响因子:41.700
- 作者:
Adam J Meyer;Andrew D Ellington - 通讯作者:
Andrew D Ellington
Andrew D Ellington的其他文献
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{{ truncateString('Andrew D Ellington', 18)}}的其他基金
Directed evolution of broadly fungible biosensors
广泛可替代生物传感器的定向进化
- 批准号:
10587024 - 财政年份:2023
- 资助金额:
$ 19.25万 - 项目类别:
Directed evolution of polymerases that can read and write extremely long sequences
聚合酶的定向进化可以读取和写入极长的序列
- 批准号:
10170542 - 财政年份:2020
- 资助金额:
$ 19.25万 - 项目类别:
Directed evolution of polymerases that can read and write extremely long sequences
聚合酶的定向进化可以读取和写入极长的序列
- 批准号:
10548111 - 财政年份:2020
- 资助金额:
$ 19.25万 - 项目类别:
Directed evolution of polymerases that can read and write extremely long sequences
聚合酶的定向进化可以读取和写入极长的序列
- 批准号:
9885765 - 财政年份:2020
- 资助金额:
$ 19.25万 - 项目类别:
Synthetic biology for the chemogenetic manipulation of pain pathways
用于疼痛通路化学遗传学操纵的合成生物学
- 批准号:
10017883 - 财政年份:2019
- 资助金额:
$ 19.25万 - 项目类别:
A robust ionotropic activator for brain-wide manipulation of neuronal function
一种强大的离子型激活剂,用于全脑操纵神经元功能
- 批准号:
9145668 - 财政年份:2015
- 资助金额:
$ 19.25万 - 项目类别:
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