Identification of odorant-receptor interactions by global mapping of the human odorome

Abstract

The human olfactory system recognizes a broad spectrum of odorants using approximately 400 different olfactory receptors (hORs). Although significant improvements of heterologous expression systems used to study interactions between ORs and odorant molecules have been made, screening the olfactory repertoire of hORs remains a tremendous challenge. We therefore developed a chemical systems level approach based on protein-protein association network to investigate novel hOR-odorant relationships. Using this new approach, we proposed and validated new bioactivities for odorant molecules and OR2W1, OR51E1 and OR5P3. As it remains largely unknown how human perception of odorants influence or prevent diseases, we also developed an odorant-protein matrix to explore global relationships between chemicals, biological targets and disease susceptibilities. We successfully experimentally demonstrated interactions between odorants and the cannabinoid receptor 1 (CB1) and the peroxisome proliferator-activated receptor gamma (PPARγ). Overall, these results illustrate the potential of integrative systems chemical biology to explore the impact of odorant molecules on human health, i.e. human odorome.

Figure 1. Workflow of the study.

Strategy to improve knowledge of olfactory perception and biological roles of odorant molecules. First an OR-OR association network identifies novel odorant-OR interactions for odorant candidates. Second, pathways linked to proteins are integrated in the OR-OR network allowing deciphering odor-disease connections. The last step involves scoring and ranking of odorant candidates for biological targets within the pharmacological space.

Figure 2. View and mapping of the odor in the OR-OR association.

(a) View of the human odorome. Nodes and edges represent the human ORs and the connections between the ORs, respectively. The node size corresponds to the number of odorant molecules known to bind to a particular OR. A weighted score, represented by the width of the edges, was assigned to each OR-OR association. It represents the strength of the link between two ORs as defined by the number of shared compounds for both ORs. (b) Mapping of odor descriptions on the human odorome using the association score (AS). Odor(s) tendency for ORs were integrated into the human odorome map. (N.D. =  non-determined odor for OR7D4).

Figure 3. Schema of the OR-odorant prediction concept.

In this example, C3 is an odorant predicted to bind to OR2 because is binding to OR1 like C1 and C2.

Figure 4. Concentration-response curves of odorants for human ORs.

Odorants predicted as agonists ( =  predicted compounds) and odorants previously shown to be agonists by Saito et al. 2009 (positive controls) activated four human ORs: (a) OR2W1, (b) OR51E1 (c) OR5P3. Data points and EC₅₀ values are means ± s.e.m. from at least three experiments.

Figure 5. Concentration-response curves of odorants for the human cannabinoid receptor CB1.

Predicted compounds, tributyl acetyl citrate, 2-nonanone and 2-phenylethyl hexanoate acted as inverse agonists. GloSensor assays were carried out in the absence (•) or in the presence (○) of pertussis toxin-treated cells. Data points and EC₅₀ values are means ± s.e.m. from three experiments.

Figure 6. Results of bio-activation of three odorants on the PPARγ receptor.

(a) Concentration dependent ligand displacement of three odorants predicted as ligands for the PPARγ receptor. (b) Transcriptional activation of PPARγ by three odorants. Results are shown as the average ± standard deviation of 2 individual experiments with each of the experiments performed with 8 replicas. Activation is given as fold activation relative to the DMSO vehicle. Rosiglitazone (not shown) was used as positive control.