Molecular Pharmacology of the Lipid/pH-sensing GPCRs as modulators of immune cell function
Molecular Pharmacology of the Lipid/pH-sensing GPCRs as modulators of immune cell function
批准号:
2689624
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2021
资助国家:
英国
项目状态:
未结题
起止时间:
2021 至 --
中文摘要
脂质/ ph敏感受体家族是一个由4个G蛋白偶联受体(gpcr)组成的簇,在免疫细胞功能和癌细胞增殖中发挥作用1。它们对不同生理背景下微环境变化引起的脂质介质和/或pH水平的变化作出反应。该家族的成员存在于单核细胞和巨噬细胞等细胞中,但关于它们如何调节炎症反应的信息有限。本项目旨在通过使用最近开发的细胞内生物传感器,定义它们如何参与不同的细胞内信号通路来控制细胞反应,从而增加我们对这些gpcr的基本理解。最初,这些gpcr将使用模型细胞系统进行研究,但观察将扩展到生理相关背景,包括替代单核细胞样细胞系和单核/巨噬细胞原代培养。开发新疗法的一个主要挑战是确定驱动其生理作用的药物的分子机制。在历史上的药物发现中,当候选化合物进入临床试验时,这一差距往往得不到解决,从而增加了失败的可能性。一种新的方法是生成跨许多不同参数的数据矩阵,并利用建模和/或AI/ML数据分析方法来识别疗效预测因子。学生将利用现有的实验方法以及内部分析支持,为从脂质/ ph传感家族中选择的GPCR构建数据矩阵。
英文摘要
The Lipid/pH-sensing receptor family are a cluster of four G protein coupled receptors (GPCRs) with roles in immune cell function and cancer cell proliferation1. They respond to changes in levels of lipid mediators and/or pH caused by microenvironmental changes in different physiological contexts. Members of this family are present in cells such as monocytes and macrophages but only limited information is available concerning how they modulate the inflammatory response.This project aims to increase our fundamental understanding of these GPCRs by defining how they engage different intracellular signalling pathways to control cellular responses, using recently developed intracellular biosensors. Initially, these GPCRs will be studied using model cell systems but observations will be extended to physiologically-relevant contexts including surrogate monocyte-like cell lines and primary cultures of monocytes/macrophages.A major challenge in the development of new therapies is to define the molecular mechanisms of drugs that drive their physiological effect. In historical drug discovery, this gap is often unresolved when candidate compounds enter clinical testing, increasing the likelihood of failure. A new approach is to generate matrices of data across many different parameters, and to harness modelling and/or AI/ML data analytic approaches to identify predictors of efficacy. The student will utilise available experimental approaches along with in house analytical support to build a data matrix for a chosen GPCR from the lipid/pH-sensing family.
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