GPCR Transcriptomics SIGNED

Project "GPCR Transcriptomics" data sheet

The following table provides information about the project.

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 Project objective

G protein coupled receptors (GPCRs) are key regulators of cell homeostasis. Given their importance in cell physiology, they have been extensively exploited as drug targets. Despite this, many discordant observations on receptor signalling and on GPCR drug response remain unexplained. Changes in receptor expression in different human tissues can be a key contributor to such differences in GPCR behaviour. And still, no comprehensive study has characterized human GPCR transcriptomics and its impact on receptor function. Such an analysis could help answer fundamental questions on GPCR pharmacology such as: How are different receptor types distributed across human tissues and how does this affect receptor function? How does age and gender-related GPCR differential expression affect receptor pharmacology? And how does splice variant distribution in different tissues influence receptor signalling? In this project, I will address these questions by applying a computational biology approach combining publicly available transcriptomics data with information on receptor structure, intracellular coupling, and pharmacogenomics. This will allow obtaining for the first time a GPCR-wide distribution map across human tissues; determining how differential expression according to age and gender can affect GPCR function; and assessing the impact of alternative splicing on receptor integrity, regulation, and coupling. At the end of this project, these insights will be made publicly available to the research community through the development of a web resource associated with the widely-used GPCR database. Using such a multidisciplinary approach, the proposed analysis will help clarify the importance of different receptor transcriptional profiles in cell physiology and drug response, point to more relevant systems to study GPCR signalling and to characterise new drug candidates, and foster a more personalised medicine by considering expression variability in the general human population.