Modulation and Ligand Selectivity of Mammalian Odorant Receptors
In mammals, the perception of smell starts with the activation of odorant receptors (ORs) by volatile molecules in the environment. Mammalian genomes typically encode large numbers of ORs, with approximately 400 intact ORs in human and more than 1000 in mouse. Central to the question of how olfactory stimuli are represented at the peripheral level is defining the ligand selectivity and activity regulation of ORs.
Processing of chemosensory signals in the brain is dynamically regulated in part by an animal’s physiological state. The Matsunami lab previously reported that type 3 muscarinic acetylcholine receptors (M3-Rs) physically interact with odorant receptors (ORs) to promote odor-induced responses in a heterologous expression system. However, it is not known how M3-Rs affect the ability of olfactory sensory neurons (OSNs) to respond to odors. In chapter 2, I demonstrate that the activation of M3-Rs inhibits the recruitment of β-arrestin-2 to ORs, resulting in a potentiation of odor-induced response in OSNs. These results suggest a role for acetylcholine in modulating olfactory processing at the initial stages of signal transduction in the olfactory system.
Understanding odor coding requires comprehensive mapping between odorant receptors and corresponding odorants. In chapter 3, I present a high-throughput in vivo method to identify repertoires of odorant receptors activated by odorants, using phosphorylated ribosome immunoprecipitation of mRNA from olfactory epithelium of odor-stimulated mice followed by RNA-Seq. This approach screens endogenously expressed odorant receptors against an odorant in one set of experiments, using awake and freely behaving mice. In combination with validations in a heterologous system, we identify sets of odorant receptors for two odorants, acetophenone and 2,5-dihydro-2,4,5-trimethylthiazoline (TMT), encompassing 69 receptor-odorant pairs. I also identified shared amino acid residues specific to the acetophenone or TMT receptors, and developed a model to predict receptor activation. This study provides a means to understand the combinatorial coding of odors in vivo.
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