Analysis of the expression of odorant receptors in heterologous HEK293T cells, and the impact of the presence of SNPs on the transport of these receptors to the plasmatic membrane

• Grant number: 24/23483-1
• Support Opportunities: Scholarships abroad - Research Internship - Scientific Initiation
• Start date: May 03, 2025
• End date: July 02, 2025
• Field of knowledge: Biological Sciences - Biochemistry - Molecular Biology
• Principal Investigator: Bettina Malnic
• Grantee: Isabella de Oliveira Escarminio
• Supervisor: Bensafi
• Host Institution: Instituto de Química (IQ). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Institution abroad: Centre De Recherche En Neurosciences De Lyon, France
• Associated to the scholarship: 23/02892-8 - Analysis of odorant receptor expression in heterologous cells and the impact of SNPs on their targeting to the plasma membrane., BP.IC

Abstract

Odorant receptors (ORs) compose the biggest gene family of G protein-coupled receptors in mammals. They are expressed in the olfactory epithelium andfunction as detectors of odorant molecules,leading to signal transduction in the olfactory neuron. Exclusive sets of ORs are activated by certain odorants, and it is possible to determine this activation through the expression of these receptors in heterologous systems. However, when expressed in heterologous cells, there is no expression or little expression of ORs in the plasmatic membrane, because the receptor is retained in the endoplasmic reticulum. In face of this problem, previous studies demonstrated that the addition of a Rho-tag at the N-terminal end of ORs, as well as the presence of receptor transporting proteins (RTPs) can promote the functional expression of ORs in HEK293T cells and increase the response to odorant stimuli.Considering that ORs genes are genetically distinct among individuals, this project aims to evaluate the relationship between molecular features to odor perceptions, by analysing molecular structures of odorants and olfactory properties.The aim is also to use computational methods, including machine learning, to detect patterns linking molecular features to odor perceptions.