Predicting Human Olfactory Perception from Activities of Odorant Receptors

Predicting human olfactory perception by odorant structure and receptor activation profile

Humans possess a remarkable ability to discriminate a wide range of odors with high precision. This process begins with olfactory receptors (ORs) detecting and responding to the molecular structures of odorants. Recent studies have aimed to associate the activity of a single OR to an odor descriptor or predict odor descriptors using 2D molecular representation. However, predicting a limited number of odor descriptors is insufficient to fully understand the widespread and elaborate olfactory perception process. Therefore, we conducted structure-activity relationship analyses for ORs of eugenol, vanillin, and structurally similar compounds, investigating the correlation between molecular structures, OR activity profiles, and perceptual odor similarity. Our results indicated that these structurally similar compounds primarily activated 6 ORs, and the activity profiles of these ORs correlated with their perception. This enabled the development of a prediction model for the eugenol-similarity score from OR activity profiles (coefficient of determination, R2 = 0.687). Furthermore, the molecular structures of odorants were represented as 3D shapes and pharmacophore fingerprints, considering the 3D structural similarities between various odorants with multiple conformations. These 3D shape and pharmacophore fingerprints could also predict the perceptual odor similarity (R2 = 0.514). Finally, we identified key molecular structural features that contributed to predicting sensory similarities between compounds structurally similar to eugenol and vanillin. Our models, which predict odor from OR activity profiles and similarities in the 3D structure of odorants, may aid in understanding olfactory perception by compressing the information from a vast number of odorants into the activity profiles of 400 ORs.

M2OR: a database of olfactory receptor-odorant pairs for understanding the molecular mechanisms of olfaction

Mammalian sense of smell is triggered by interaction between odorant molecules and a class of proteins, called olfactory receptors (ORs). These receptors, expressed at the surface of olfactory sensory neurons, encode myriad of distinct odors via a sophisticated activation pattern. However, determining the molecular recognition spectrum of ORs remains a major challenge. The Molecule to Olfactory Receptor database (M2OR, https://m2or.chemsensim.fr/) provides curated data that allows an easy exploration of the current state of the research on OR-molecule interaction. We have gathered a database of 75,050 bioassay experiments for 51 395 distinct OR-molecule pairs. Drawn from published literature and public databases, M2OR contains information about OR responses to molecules and their mixtures, receptor sequences and experimental details. Users can obtain information on the activity of a chosen molecule or a group of molecules, or search for agonists for a specific OR or a group of ORs. Advanced search allows for fine-grained queries using various metadata such as species or experimental assay system, and the database can be queried by multiple inputs via a batch search. Finally, for a given search query, users can access and download a curated aggregation of the experimental data into a binarized combinatorial code of olfaction.

A status report on human odorant receptors and their allocated agonists

Olfactory perception begins when odorous substances interact with specialized receptors located on the surface of dedicated sensory neurons. The recognition of smells depends on a complex mechanism involving a combination of interactions between an odorant and a set of odorant receptors (ORs), where molecules are recognized according to a combinatorial activation code of ORs. Although these interactions have been studied for decades, the rules governing this ligand recognition remain poorly understood, and the complete combinatorial code is only known for a handful of odorants. We have carefully analyzed experimental results regarding the interactions between ORs and molecules to provide a status report on the deorphanization of ORs, i.e. the identification of the first agonist for a given sequence. This meticulous analysis highlights the influence of experimental methodology (cell line or readout) on molecule-receptor association results and shows that 83% of the results are conserved regardless of experimental conditions. The distribution of another key parameter, EC50, indicates that most OR ligand activities are in the micromolar range and that impurities could lead to erroneous conclusions. Focusing on the human ORs, our study shows that 88% of the documented sequences still need to be deorphanized. Finally, we also estimate the size of the ORs' recognition range, or broadness, as the number of odorants activating a given OR. By analogously estimating molecular broadness and combining the two estimates we propose a basic framework that can serve as a comparison point for future machine learning algorithms predicting OR-molecule activity.

A critical review on odor measurement and prediction

Odor pollution has become a global environmental issue of increasing concern in recent years. Odor measurements are the basis of assessing and solving odor problems. Olfactory and chemical analysis can be used for odor and odorant measurements. Olfactory analysis reflects the subjective perception of human, and chemical analysis reveals the chemical composition of odors. As an alternative to olfactory analysis, odor prediction methods have been developed based on chemical and olfactory analysis results. The combination of olfactory and chemical analysis is the best way to control odor pollution, evaluate the performances of the technologies, and predict odor. However, there are still some limitations and obstacles for each method, their combination, and the prediction. Here, we present an overview of odor measurement and prediction. Different olfactory analysis methods (namely, the dynamic olfactometry method and the triangle odor bag method) are compared in detail, the latest revisions of the standard olfactometry methods are summarized, and the uncertainties of olfactory measurement results (i.e., the odor thresholds) are analyzed. The researches, applications, and limitations of chemical analysis and odor prediction are introduced and discussed. Finally, the development and application of odor databases and algorithms for optimizing odor measurement and prediction methods are prospected, and a preliminary framework for an odor database is proposed. This review is expected to provide insights into odor measurement and prediction.

Metabolic activity organizes olfactory representations

Hearing and vision sensory systems are tuned to the natural statistics of acoustic and electromagnetic energy on earth and are evolved to be sensitive in ethologically relevant ranges. But what are the natural statistics of odors, and how do olfactory systems exploit them? Dissecting an accurate machine learning model (Lee et al., 2022) for human odor perception, we find a computable representation for odor at the molecular level that can predict the odor-evoked receptor, neural, and behavioral responses of nearly all terrestrial organisms studied in olfactory neuroscience. Using this olfactory representation (principal odor map [POM]), we find that odorous compounds with similar POM representations are more likely to co-occur within a substance and be metabolically closely related; metabolic reaction sequences (Caspi et al., 2014) also follow smooth paths in POM despite large jumps in molecular structure. Just as the brain's visual representations have evolved around the natural statistics of light and shapes, the natural statistics of metabolism appear to shape the brain's representation of the olfactory world.

Olfactory-gustatory simultaneity judgments: A preliminary study on the congruency-dependent temporal window of multisensory binding

BACKGROUND

A greater congruency of audio and video expands the temporal binding window (TBW). A similar phenomenon may occur with a combination of odor and taste, which are the main components of flavor.

OBJECTIVE

TBW is defined as the temporal resolution of synchrony perception. The larger the TBW, the lower the resolution. We hypothesized that the more congruent the odor and taste, the lower the temporal resolution of synchrony perception.

METHODS

To examine this hypothesis, 10 female participants performed simultaneity judgment (SJ) tasks under congruent (soy sauce odor with saline) and incongruent (cherry tree leaf odor with saline) conditions and evaluated the congruency with saltiness for the two odors. In the SJ tasks, participants reported whether odor and taste were presented simultaneously or successively. We assumed a Gaussian distribution for the temporal distributions of the simultaneous response rates and calculated the approximations. In addition, we computed the half width at half height (HWHH) as an index of TBW based on the coefficient of approximation for the temporal distribution of the simultaneous response rates.

RESULTS

HWHH was significantly larger under congruent condition than under incongruent condition. In addition, congruency with saltiness had a significant moderate positive correlation with HWHH.

CONCLUSION

The larger the HWHH, the lower the temporal resolution of synchrony perception, supporting the hypothesis. This study suggests that the width of TBW depends on the cross-modal congruency similar to the case for audio-visual SJs. However, methodological improvements, including a larger sample size and gender-independent recruitment of participants, are essential to enhance the reliability of the findings because some of the results did not provide sufficient ESs or power.

Genetic and functional odorant receptor variation in the Homo lineage

Humans, Neanderthals, and Denisovans independently adapted to a wide range of geographic environments and their associated food odors. Using ancient DNA sequences, we explored the in vitro function of thirty odorant receptor genes in the genus Homo. Our extinct relatives had highly conserved olfactory receptor sequence, but humans did not. Variations in odorant receptor protein sequence and structure may have produced variation in odor detection and perception. Variants led to minimal changes in specificity but had more influence on functional sensitivity. The few Neanderthal variants disturbed function, whereas Denisovan variants increased sensitivity to sweet and sulfur odors. Geographic adaptations may have produced greater functional variation in our lineage, increasing our olfactory repertoire and expanding our adaptive capacity. Our survey of olfactory genes and odorant receptors suggests that our genus has a shared repertoire with possible local ecological adaptations.

Application of artificial intelligence to decode the relationships between smell, olfactory receptors and small molecules

Deciphering the relationship between molecules, olfactory receptors (ORs) and corresponding odors remains a challenging task. It requires a comprehensive identification of ORs responding to a given odorant. With the recent advances in artificial intelligence and the growing research in decoding the human olfactory perception from chemical features of odorant molecules, the applications of advanced machine learning have been revived. In this study, Convolutional Neural Network (CNN) and Graphical Convolutional Network (GCN) models have been developed on odorant molecules-odors and odorant molecules-olfactory receptors using a large set of 5955 molecules, 160 odors and 106 olfactory receptors. The performance of such models is promising with a Precision/Recall Area Under Curve of 0.66 for the odorant-odor and 0.91 for the odorant-olfactory receptor GCN models respectively. Furthermore, based on the correspondence of odors and ORs associated for a set of 389 compounds, an odor-olfactory receptor pairwise score was computed for each odor-OR combination allowing to suggest a combinatorial relationship between olfactory receptors and odors. Overall, this analysis demonstrate that artificial intelligence may pave the way in the identification of the smell perception and the full repertoire of receptors for a given odorant molecule.

Recent trends in aroma release and perception during food oral processing: A review

The dynamic and complex peculiarities of the oral environment present several challenges for controlling the aroma release during food consumption. They also pose higher requirements for designing food with better sensory quality. This requires a comprehensive understanding of the basic rules of aroma transmission and aroma perception during food oral processing and its behind mechanism. This review summarized the latest developments in aroma release from food to retronasal cavity, aroma release and delivery influencing factors, aroma perception mechanisms. The individual variance is the most important factor affecting aroma release and perception. Therefore, the intelligent chewing simulator is the key to establish a standard analytical method. The key odorants perceived from the retronasal cavity should be given more attention during food oral processing. Identification of the olfactory receptor activated by specific odorants and its binding mechanisms are still the bottleneck. Electrophysiology and image technology are the new noninvasive technologies in elucidating the brain signals among multisensory, which can fill the gap between aroma perception and other senses. Moreover, it is necessary to develop a new approach to integrate the relationship among aroma binding parameters, aroma concentration, aroma attributes and cross-modal reactions to make the aroma prediction model more accurate.

A biomimetic olfactory recognition system for the discrimination of Chinese liquor aromas

Aroma is an important attribute influencing the perceived quality of Chinese liquors, with each liquor characterized by a unique collection of volatile chemicals. Here, a biomimetic olfactory recognition system combining an optimal panel of 10 mouse odorant receptors with back propagation neural network model was designed to discriminate the aromas of Chinese liquors. Our system shows an excellent predictive capacity with an average accuracy of 96.5% to discriminate liquors of different aroma types, as well as those of different brands and ageing years within the same aroma type. A total of 124 interactions between liquor aroma compounds and odorant receptors were further elucidated to understand odorant coding at the molecular level, including 14 newly deorphaned odorant receptors. Our work represents a proof of concept for combining receptors and machine learning in the discrimination of complex odorant stimuli.

Mapping odorant sensitivities reveals a sparse but structured representation of olfactory chemical space by sensory input to the mouse olfactory bulb

In olfactory systems, convergence of sensory neurons onto glomeruli generates a map of odorant receptor identity. How glomerular maps relate to sensory space remains unclear. We sought to better characterize this relationship in the mouse olfactory system by defining glomeruli in terms of the odorants to which they are most sensitive. Using high-throughput odorant delivery and ultrasensitive imaging of sensory inputs, we imaged responses to 185 odorants presented at concentrations determined to activate only one or a few glomeruli across the dorsal olfactory bulb. The resulting datasets defined the tuning properties of glomeruli - and, by inference, their cognate odorant receptors - in a low-concentration regime, and yielded consensus maps of glomerular sensitivity across a wide range of chemical space. Glomeruli were extremely narrowly tuned, with ~25% responding to only one odorant, and extremely sensitive, responding to their effective odorants at sub-picomolar to nanomolar concentrations. Such narrow tuning in this concentration regime allowed for reliable functional identification of many glomeruli based on a single diagnostic odorant. At the same time, the response spectra of glomeruli responding to multiple odorants was best predicted by straightforward odorant structural features, and glomeruli sensitive to distinct odorants with common structural features were spatially clustered. These results define an underlying structure to the primary representation of sensory space by the mouse olfactory system.

Insight into the Structure-Odor Relationship of Molecules: A Computational Study Based on Deep Learning

Molecules with pleasant odors, unacceptable odors, and even serious toxicity are closely related to human social life. It is impractical to identify the odors of molecules in large quantities (particularly hazardous odors) using experimental methods. Computer-aided methods have currently attracted increasing attention for the prediction of molecular odors. Here, through models based on multilayer perceptron (MLP) and physicochemical descriptors (MLP-Des), MLP and molecular fingerprint, and convolutional neural network (CNN), we conduct the two-class prediction of odor/no odor, fruity/no odor, floral/no odor, and woody/no odor, and the multi-class prediction of fruity/flowery/woody/no odor on our newly refined molecular odor datasets. We show that three kinds of predictors can robustly predict molecular odors. The MLP-Des model not only exhibits the best prediction results (the AUC values are 0.99 and 0.86 for the two- and multi-classification models, respectively) but can also well reflect the characteristics of the structure–odor relationship of molecules. The CNN model takes 2D molecular images as input and can automatically extract the structural features related to molecular odors. The proposed models are of great help for the prediction of molecular odorants, understanding the underlying relationship between chemical structure and odor perception, and the discovery of new odorous and/or hazardous molecules.

Behavioural regulator and molecular reception of a double-edge-sword hunter beetle

BACKGROUND

The black carabid beetle Calosoma maximoviczi is a successful predator that serves as both a beneficial insect and a severe threat to economic herbivores. Its hunting technique relies heavily on olfaction, but the underlying mechanism has not been studied. Here, we report the electrophysiological, ecological and molecular traits of bioactive components identified from a comprehensive panel of natural odorants in the beetle-prey-plant system. The aim of this work was to investigate olfactory perceptions and their influence on the behaviours of C. maximoviczi.

RESULTS

Among the 200 identified volatiles, 18 were concentrated in beetle and prey samples, and 14 were concentrated in plants. Insect feeding damage to plants led to a shift in the emission fingerprint. Twelve volatiles were selected using successive electrophysiological tests. Field trials showed that significant sex differences existed when trapping with a single chemical or chemical mixture. Expression profiles indicated that sex-biased catches were related to the expression of 15 annotated CmaxOBPs and 40 CmaxORs across 12 chemosensory organs. In silico evaluations were conducted with 16 CmaxORs using modelling and docking. Better recognition was predicted for the pairs CmaxOR5-(Z)-3-hexenyl acetate, CmaxOR6-β-caryophyllene, CmaxOR18-(E)-β-ocimene and CmaxOR18-tetradecane, with higher binding affinity and a suitable binding pocket. Lastly, 168Y in CmaxOR6 and 142Y in CmaxOR18 were predicted as key amino acid residues for binding β-caryophyllene and tetradecane, respectively.

CONCLUSION

This work provides an example pipeline for de novo investigation in C. maximoviczi baits and the underlying olfactory perceptions. The results will benefit the future development of trapping-based integrated pest management strategies and the deorphanization of odorant receptors in ground beetles. © 2022 Society of Chemical Industry.

Behavior Individuality: A Focus on Drosophila melanogaster

Among individuals, behavioral differences result from the well-known interplay of nature and nurture. Minute differences in the genetic code can lead to differential gene expression and function, dramatically affecting developmental processes and adult behavior. Environmental factors, epigenetic modifications, and gene expression and function are responsible for generating stochastic behaviors. In the last decade, the advent of high-throughput sequencing has facilitated studying the genetic basis of behavior and individuality. We can now study the genomes of multiple individuals and infer which genetic variations might be responsible for the observed behavior. In addition, the development of high-throughput behavioral paradigms, where multiple isogenic animals can be analyzed in various environmental conditions, has again facilitated the study of the influence of genetic and environmental variations in animal personality. Mainly, Drosophila melanogaster has been the focus of a great effort to understand how inter-individual behavioral differences emerge. The possibility of using large numbers of animals, isogenic populations, and the possibility of modifying neuronal function has made it an ideal model to search for the origins of individuality. In the present review, we will focus on the recent findings that try to shed light on the emergence of individuality with a particular interest in D. melanogaster.

Occurrence of Marine Ingredients in Fragrance: Update on the State of Knowledge

The fragrance field of perfumes has attracted considerable scientific, industrial, cultural, and civilizational interest. The marine odor is characterized by the specific smell of sea breeze, seashore, algae, and oyster, among others. Marine odor is a more recent fragrance and is considered as one of the green and modern fragrances. The smells reproducing the marine environment are described due to their content of Calone 1951 (7-methyl-2H-1,5-benzodioxepin-3(4H)-one), which is a synthetic compound. In addition to the synthetic group of benzodioxepanes, such as Calone 51 and its derivatives, three other groups of chemical compounds seem to represent the marine smell. The first group includes the polyunsaturated cyclic ((+)-Dictyopterene A) and acyclic (giffordene) hydrocarbons, acting as pheromones. The second group corresponds to polyunsaturated aldehydes, such as the (Z,Z)-3,6-nonadienal, (E,Z)-2,6-nonadienal, which are most likely derived from the degradation of polyunsaturated fatty acids. The third group is represented by small molecules such as sulfur compounds and halogenated phenols which are regarded as the main flavor compounds of many types of seafood. This review exposes, most notably, the knowledge state on the occurrence of marine ingredients in fragrance. We also provide a detailed discussion on several aspects of essential oils, which are the most natural ingredients from various marine sources used in fragrance and cosmetics, including synthetic and natural marine ingredients.

Modification of the Peripheral Olfactory System by Electronic Cigarettes

Electronic cigarettes (e-cigs) are used by millions of adolescents and adults worldwide. Commercial e-liquids typically contain flavorants, propylene glycol, and vegetable glycerin with or without nicotine. These chemical constituents are detected and evaluated by chemosensory systems to guide and modulate vaping behavior and product choices of e-cig users. The flavorants in e-liquids are marketing tools. They evoke sensory percepts of appealing flavors through activation of chemical sensory systems to promote the initiation and sustained use of e-cigs. The vast majority of flavorants in e-liquids are volatile odorants, and as such, the olfactory system plays a dominant role in perceiving these molecules that enter the nasal cavity either orthonasally or retronasally during vaping. In addition to flavorants, e-cig aerosol contains a variety of by-products generated through heating the e-liquids, including odorous irritants, toxicants, and heavy metals. These harmful substances can directly and adversely impact the main olfactory epithelium (MOE). In this article, we first discuss the olfactory contribution to e-cig flavor perception. We then provide information on MOE cell types and their major functions in olfaction and epithelial maintenance. Olfactory detection of flavorants, nicotine, and odorous irritants and toxicants are also discussed. Finally, we discuss the cumulated data on modification of the MOE by flavorant exposure and toxicological impacts of formaldehyde, acrolein, and heavy metals. Together, the information presented in this overview may provide insight into how e-cig exposure may modify the olfactory system and adversely impact human health through the alteration of the chemosensory factor driving e-cig use behavior and product selections. © 2021 American Physiological Society. Compr Physiol 11:2621-2644, 2021.

Parsing Sage and Rosemary in Time: The Machine Learning Race to Crack Olfactory Perception

Color and pitch perception are largely understandable from characteristics of physical stimuli: the wavelengths of light and sound waves, respectively. By contrast, understanding olfactory percepts from odorous stimuli (volatile molecules) is much more challenging. No intuitive set of molecular features is up to the task. Here in Chemical Senses, the Ray lab reports using a predictive modeling framework-first breaking molecular structure into thousands of features and then using this to train a predictive statistical model on a wide range of perceptual descriptors-to create a tool for predicting the odor character of hundreds of thousands of available but previously uncharacterized molecules (Kowalewski et al. 2021). This will allow future investigators to representatively sample the space of odorous molecules as well as identify previously unknown odorants with a target odor character. Here, I put this work into the context of other modeling efforts and highlight the urgent need for large new datasets and transparent benchmarks for the field to make and evaluate modeling breakthroughs, respectively.

Prolonged activation of carbon dioxide-sensitive neurons in mosquitoes

Many insects can detect carbon dioxide (CO₂) plumes using a conserved receptor made up of members of the gustatory receptor (Gr) family Gr1, Gr2 and Gr3. Mosquitoes are attracted to host animals for blood meals using plumes of CO₂ in the exhaled breath using the receptor expressed in the A neuron of the capitate peg sensilla type on the maxillary palps. The receptor is known to also detect several other classes of odorants, including ones emitted from human skin. Here, we discover that a common skin odorant, butyric acid, can cause a phasic activation followed by an unusually prolonged tonic activity after the stimulus is over in the CO₂ neurons of mosquitoes. The effect is conserved in both Aedes aegypti and Anopheles gambiae mosquitoes. This raises a question about its role in a mosquito's preference for the skin odour of different individuals. Butyric acid belongs to a small number of odorants known to cause the prolonged activation of the CO₂ receptor. A chemical informatic analysis identifies a specific set of physico-chemical features that can be used in a machine learning predictive model for the prolonged activators. Interestingly, this set is different from physico-chemical features selected for activators or inhibitors, indicating that each has a distinct structural basis. The structural understanding opens up an opportunity to find novel ligands to manipulate the CO₂ receptor and mosquito behaviour.