The coming future: The role of the oral-microbiota-brain axis in aroma release and perception

The field of aroma release and perception during the oral process has been well studied. However, the traditional approaches have not fully explored the integration of oral biology, microbiology, and neurology to further understand aroma release and perception mechanisms. Herein, to address the existing challenges in this field, we introduce the oral-microbiota-brain axis (OMBA), an innovative framework that encapsulates the interactive relationships among saliva and the oral mucosa, the oral microbiota, and the brain in aroma release and perception. This review introduces the OMBA and highlights its role as a key interface facilitating the sensory experience of aroma. Based on a comprehensive literature survey, the specific roles of the oral mucosa, oral microbiota, saliva, and brain in the OMBA are discussed. This integrated approach reveals the importance of each component and the interconnected relationships within this axis in the overall process of aroma release and perception. Saliva and the oral mucosa play fundamental roles in aroma release and perception; the oral microbiota regulates aroma release and impacts olfactory perception; and the brain's intricate neural circuitry is central to the decoding and interpretation of aroma signals. The components of this axis are interdependent, and imbalances can disrupt aroma perception. The OMBA framework not only enhances our comprehension of aroma release and perception but also paves the way for innovative applications that could heighten sensory experiences.

General Chemical Reaction Network Theory for Olfactory Sensing Based on G-Protein-Coupled Receptors: Elucidation of Odorant Mixture Effects and Agonist-Synergist Threshold

This work presents a general chemical reaction network theory for olfactory sensing processes that employ G-protein-coupled receptors as olfactory receptors (ORs). The theory can be applied to general mixtures of odorants and an arbitrary number of ORs. Reactions of ORs with G-proteins, in both the presence and absence of odorants, are explicitly considered. A unique feature of the theory is the definition of an odor activity vector consisting of strengths of odorant-induced signals from ORs relative to those due to background G-protein activity in the absence of odorants. It is demonstrated that each component of the odor activity defined this way reduces to a Michaelis-Menten form capable of accounting for cooperation or competition effects between different odorants. The main features of the theory are illustrated for a two-odorant mixture. Known and potential mixture effects, such as suppression, shadowing, inhibition, and synergy, are quantitatively described. Effects of relative values of rate constants, basal activity, and G-protein concentration are also demonstrated.

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.

A new conceptual and quantitative approach to exploring and defining potential open-access olfactory information

All organisms emit odour, providing 'open-access' olfactory information for any receiver with the right sensory apparatus. Characterizing open-access information emitted by groups of organisms, such as plant species, provides the means to answer significant questions about ecological interactions and their evolution. We present a new conceptual framework defining information reliability and a practical method to characterize and recover information from amongst olfactory noise. We quantified odour emissions from two tree species, one focal group and one outgroup, to demonstrate our approach using two new R statistical functions. We explore the consequences of relaxing or tightening criteria defining information and, from thousands of odour combinations, we identify and quantify those few likely to be informative. Our method uses core general principles characterizing information while incorporating knowledge of how receivers detect and discriminate odours. We can now map information in consistency-precision reliability space, explore the concept of information, and test information-noise boundaries, and between cues and signals.

Decoding olfactory EEG signals for different odor stimuli identification using wavelet-spatial domain feature

BACKGROUND

Decoding olfactory-induced electroencephalography (olfactory EEG) signals has gained significant attention in recent years, owing to its potential applications in several fields, such as disease diagnosis, multimedia applications, and brain-computer interaction (BCI). Extracting discriminative features from olfactory EEG signals with low spatial resolution and poor signal-to-noise ratio is vital but challenging for improving decoding accuracy.

NEW METHODS

By combining discrete wavelet transform (DWT) with one-versus-rest common spatial pattern (OVR-CSP), we develop a novel feature, named wavelet-spatial domain feature (WSDF), to decode the olfactory EEG signals. First, DWT is employed on EEG signals for multilevel wavelet decomposition. Next, the DWT coefficients obtained at a specific level are subjected to OVR-CSP for spatial filtering. Correspondingly, the variance is extracted to generate a discriminative feature set, labeled as WSDF.

RESULTS

To verify the effectiveness of WSDF, a classification of olfactory EEG signals was conducted on two data sets, i.e., a public EEG dataset 'Odor Pleasantness Perception Dataset (OPPD)', and a self-collected dataset, by using support vector machine (SVM) trained based on different cross-validation methods. Experimental results showed that on OPPD dataset, the proposed method achieved a best average accuracy of 100% and 94.47% for the eyes-open and eyes-closed conditions, respectively. Moreover, on our own dataset, the proposed method gave a highest average accuracy of 99.50%.

COMPARISON WITH EXISTING METHODS

Compared with a wide range of EEG features and existing works on the same dataset, our WSDF yielded superior classification performance.

CONCLUSIONS

The proposed WSDF is a promising candidate for decoding olfactory EEG signals.

Smell compounds classification using UMAP to increase knowledge of odors and molecular structures linkages

This study aims to highlight the relationships between the structure of smell compounds and their odors. For this purpose, heterogeneous data sources were screened, and 6038 odorant compounds and their known associated odors (162 odor notes) were compiled, each individual molecule being represented with a set of 1024 structural fingerprint. Several dimensional reduction techniques (PCA, MDS, t-SNE and UMAP) with two clustering methods (k-means and agglomerative hierarchical clustering AHC) were assessed based on the calculated fingerprints. The combination of UMAP with k-means and AHC methods allowed to obtain a good representativeness of odors by clusters, as well as the best visualization of the proximity of odorants on the basis of their molecular structures. The presence or absence of molecular substructures has been calculated on odorant in order to link chemical groups to odors. The results of this analysis bring out some associations for both the odor notes and the chemical structures of the molecules such as "woody" and "spicy" notes with allylic and bicyclic structures, "balsamic" notes with unsaturated rings, both "sulfurous" and "citrus" with aldehydes, alcohols, carboxylic acids, amines and sulfur compounds, and "oily", "fatty" and "fruity" characterized by esters and with long carbon chains. Overall, the use of UMAP associated to clustering is a promising method to suggest hypotheses on the odorant structure-odor relationships.

Exploring the Characteristics of an Aroma-Blending Mixture by Investigating the Network of Shared Odors and the Molecular Features of Their Related Odorants

The perception of aroma mixtures is based on interactions beginning at the peripheral olfactory system, but the process remains poorly understood. The perception of a mixture of ethyl isobutyrate (Et-iB, strawberry-like odor) and ethyl maltol (Et-M, caramel-like odor) was investigated previously in both human and animal studies. In those studies, the binary mixture of Et-iB and Et-M was found to be configurally processed. In humans, the mixture was judged as more typical of a pineapple odor, similar to allyl hexanoate (Al-H, pineapple-like odor), than the odors of the individual components. To explore the key features of this aroma blend, we developed an in silico approach based on molecules having at least one of the odors—strawberry, caramel or pineapple. A dataset of 293 molecules and their related odors was built. We applied the notion of a “social network” to describe the network of the odors. Additionally, we explored the structural properties of the molecules in this dataset. The network of the odors revealed peculiar links between odors, while the structural study emphasized key characteristics of the molecules. The association between “strawberry” and “caramel” notes, as well as the structural diversity of the “strawberry” molecules, were notable. Such elements would be key to identifying potential odors/odorants to form aroma blends.

Development of a Piezoelectric-Based Odor Reproduction System

Odor reproduction, a branch of machine olfaction, is a technology through which a machine represents various odors by blending several odor sources in different proportions and releases them. In this paper, an odor reproduction system is proposed. The system includes an atomization-based odor dispenser using 16 micro-porous piezoelectric transducers. The authors propose the use of an electronic nose combined with a Principal Component Analysis–Linear Discriminant Analysis (PCA–LDA) model to evaluate the effectiveness of the system. The results indicate that the model can be used to evaluate the system.

Systematic review of olfactory shifts related to obesity

OBJECTIVE

The modern food environment is a key driver of rising levels of obesity. While olfaction is known to play a major role in food choice; however, its relationship to obesity is yet to be understood. This review assesses current knowledge of the interaction between obesity and olfaction.

METHODS

This review is based on observational studies comparing olfactory abilities across weight groups (N = 10) and clinical studies evaluating olfactory changes following bariatric surgery (N = 9). Meta-analyses were performed on data collected by a standard olfactory assessment tool (Sniffin΄ Sticks), to test whether olfaction has any association with body weight or bariatric surgery.

RESULTS

This review synthesizes findings derived from 38 datasets, with a total of 1432 individual olfactory assessments. The meta-analyses suggest that olfactory function is negatively correlated with body weight. In addition, Roux-en-Y gastric bypass patients frequently report olfactory changes, yet more pronounced and immediate shifts have been observed among sleeve gastrectomy recipients.

CONCLUSIONS

Our review finds strong evidence for the link between olfaction and obesity and indicates that bariatric surgery (particularly the sleeve gastrectomy) is effective in reversing olfactory decline associated with obesity. In conclusion, we present mechanistic models to underpin the observed relationship between olfaction and obesity.

Molecular Basis of Mammalian Odor Discrimination: A Status Report

Humans have 396 unique, intact olfactory receptors (ORs), G-protein coupled receptors (GPCRs) containing receptor-specific binding sites; other mammals have more. Activation of these transmembrane proteins by an odorant initiates a signaling cascade, evoking an action potential leading to perception of a smell. Because the number of distinguishable odorants vastly exceeds the number of ORs, research has focused on mechanisms of recognition and signaling processes for classes of odorants. In this review, selected recent examples will be presented of "deorphaned" mammalian receptors, where the OR ligands (odorants) as well as key aspects of receptor-odorant interactions were identified using odorant-mediated receptor activation data together with site-directed mutagenesis and molecular modeling. Based on cumulative evidence from OR deorphaning and olfactory receptor neuron activation studies, a receptor-ligand docking model rather than an alternative bond vibration model is suggested to best explain the molecular basis of the exquisitely sensitive odor discrimination in mammals.

Could the Olfactory System Be a Target for Homeopathic Remedies as Nanomedicines?

Homeopathic remedies (HRs) contain odorant molecules such as flavonoids or terpenes and can lose their efficiency in presence of some competitive odors. Such similarities, along with extreme sensitivity of the olfactory system, widespread presence of olfactory receptors over all organic tissues (where they have metabolic roles besides perception of odors), and potential direct access to the brain through olfactory nerves (ONs) and trigeminal nerves, may suggest the olfactory system as target for HRs. Recent works highlighted that HRs exist in a dual form, that is, a still molecular form at low dilution and a nanoparticulate form at high dilution, and that remnants of source remedy persist in extremely high dilutions. From the literature, both odorants and nanoparticles (NPs) can enter the body through inhalation, digestive absorption, or through the skin, especially, NPs or viruses can directly reach the brain through axons of nerves. Assuming that HRs are recognized by olfactory receptors, their information could be transmitted to numerous tissues through receptor-ligand interaction, or to the brain by either activating the axon potential of ONs and trigeminal nerves or, in their nanoparticulate form, by translocating through axons of these nerves. Moreover, the nanoparticulate form may activate the immune system at multiple levels, induce systemic various biological responses through the pituitary axis and inflammation factors, or modulate gene expression at the cellular level. As immunity, inflammation, pituitary axis, and olfactory system are closely linked together, their permanent interaction triggered by olfactory receptors may thus ensure homeostasis.