Competitive and noncompetitive odorant interactions in the early neural coding of odorant mixtures

Functional Characterization of a Female-Biased Chemoreceptor of the Codling Moth (Cydia pomonella) Responding to Aldehydes and Other Volatile Compounds

With the advent of semiochemical-based control strategies used to mitigate damage of agricultural pest moths, many studies have focused on the function of male-specific putative pheromone receptors (PRs). In this investigation, we instead isolated, heterologously expressed, and functionally characterized a female-biased candidate PR, CpomOR22, from the codling moth, Cydia pomonella. Using transgenic Drosophila melanogaster for single sensillum recording (SSR) and gas-chromatographic SSR, we tested both synthetic ligands and various apple headspace extracts, identifying saturated and unsaturated aldehydes (nonanal, decanal, undecanal, dodecanal; (Z)-4-undecenal and (Z)-6-undecenal) among the most active ligands. Parallel experiments expressing CpomOR22 in Xenopus oocytes confirmed the binding of nonanal, decanal and undecanal and revealed lactones (γ-undecalactone and δ-dodecalactone) and several carboxylic acids as additional active compounds. The renowned ecological importance of aldehydes for the codling moth and the potential for newly identified ligands, such as lactones, may inform innovative control strategies based on novel semiochemicals to interfere with the female-specific chemosensory systems of this insect.

Analyzing lognormal data: A nonmathematical practical guide

Lognormal distributions are pervasive in pharmacology and elsewhere in biomedical science, arising naturally when biological effects multiply rather than add. Despite their ubiquity in pharmacological parameters (eg, EC50, IC50, Kd, and Km), lognormal distributions are often overlooked or misunderstood, leading to flawed data analysis. This largely nonmathematical review explains why lognormal distributions are common, how to recognize them, and how to analyze them appropriately. We show that many measured variables are lognormal. So are many derived parameters, particularly those defined as ratios of lognormal variables. Through examples and simulations accessible to working scientists, we demonstrate how misidentifying lognormal distributions as normal leads to reduced statistical power, unnecessarily large sample sizes, false identification of outliers, and inappropriate reporting of effects as differences rather than ratios. We challenge the common practice of using normality tests to decide how to analyze data, showing that many data sets pass both normality and lognormality tests, especially with small sample sizes. Instead, we advocate for assuming lognormality based on the nature of the variable. This review provides practical guidance on recognizing and presenting lognormal data, and comparing data sets sampled from lognormal distributions. Based on Monte Carlo simulations, we recommend the lognormal Welch's t test or nonparametric Brunner-Munzel test for comparing 2 unpaired groups, the lognormal ratio paired t test for paired comparisons, and lognormal ANOVA for ≥3 groups. By recognizing and properly handling lognormal distributions, pharmacologists can design more efficient experiments, obtain more reliable statistical inferences, and communicate their results more effectively. SIGNIFICANCE STATEMENT: Lognormal distributions are common in pharmacology and many scientific fields, but they are often misunderstood or overlooked. This review provides a detailed guide to recognizing and analyzing lognormal data, aiming to help pharmacologists perform more appropriate and more powerful statistical analyses, draw more meaningful conclusions from their data, and communicate their results more effectively.

Olfactory perception complexity induced by key odorants perceptual interactions of alcoholic beverages: Wine as a focus case example

The odorants in alcoholic beverages are frequently experienced as complex mixtures, and there is a complex array of influence factors and interactions involved during consumption that deeply increase its olfactory perception complexity, especially the complexity induced by perceptual interactions between different odorants. In this review, the effect of olfactory perceptual interactions and other factors related to the complexity of olfactory perception of alcoholic beverages are discussed. The classification, influencing factors, and mechanisms of olfactory perceptual interactions are outlined. Recent research progress as well as the methodologies applied in these studies on perceptual interactions between odorants observed in representative alcoholic beverages, especially wine, are briefly summarized. In the future, unified theory or systematic research methodology need to be established, since up to now, the rules of perceptual interaction between multiple odorants, which is critical to the alcoholic beverage industry to improve the flavor of their products, are still not revealed.

Modeling and characterization of pure and odorant mixture processing in the Drosophila mushroom body calyx

Associative memory in the Mushroom Body of the fruit fly brain depends on the encoding and processing of odorants in the first three stages of the Early Olfactory System: the Antenna, the Antennal Lobe and the Mushroom Body Calyx. The Kenyon Cells (KCs) of the Calyx provide the Mushroom Body compartments the identity of pure and odorant mixtures encoded as a train of spikes. Characterizing the code underlying the KC spike trains is a major challenge in neuroscience. To address this challenge we start by explicitly modeling the space of odorants using constructs of both semantic and syntactic information. Odorant semantics concerns the identity of odorants while odorant syntactics pertains to their concentration amplitude. These odorant attributes are multiplicatively coupled in the process of olfactory transduction. A key question that early olfactory systems must address is how to disentangle the odorant semantic information from the odorant syntactic information. To address the untanglement we devised an Odorant Encoding Machine (OEM) modeling the first three stages of early olfactory processing in the fruit fly brain. Each processing stage is modeled by Divisive Normalization Processors (DNPs). DNPs are spatio-temporal models of canonical computation of brain circuits. The end-to-end OEM is constructed as cascaded DNPs. By extensively modeling and characterizing the processing of pure and odorant mixtures in the Calyx, we seek to answer the question of its functional significance. We demonstrate that the DNP circuits in the OEM combinedly reduce the variability of the Calyx response to odorant concentration, thereby separating odorant semantic information from syntactic information. We then advance a code, called first spike sequence code, that the KCs make available at the output of the Calyx. We show that the semantics of odorants can be represented by this code in the spike domain and is ready for easy memory access in the Mushroom Body compartments.

The Ca2+-activated Cl- channel TMEM16B shapes the response time course of olfactory sensory neurons

Mammalian olfactory sensory neurons (OSNs) generate an odorant-induced response by sequentially activating two ion channels, which are in their ciliary membranes. First, a cationic, Ca2+-permeable cyclic nucleotide-gated channel is opened following odorant stimulation via a G protein-coupled transduction cascade and an ensuing rise in cAMP. Second, the increase in ciliary Ca2+ opens the excitatory Ca2+-activated Cl- channel TMEM16B, which carries most of the odorant-induced receptor current. While the role of TMEM16B in amplifying the response has been well established, it is less understood how this secondary ion channel contributes to response kinetics and action potential generation during single as well as repeated stimulation and, on the other hand, which response properties the cyclic nucleotide-gated (CNG) channel determines. We first demonstrate that basic membrane properties such as input resistance, resting potential and voltage-gated currents remained unchanged in OSNs that lack TMEM16B. The CNG channel predominantly determines the response delay and adaptation during odorant exposure, while the absence of the Cl- channels shortens both the time the response requires to reach its maximum and the time to terminate after odorant stimulation. This faster response termination in Tmem16b knockout OSNs allows them, somewhat counterintuitively despite the large reduction in receptor current, to fire action potentials more reliably when stimulated repeatedly in rapid succession, a phenomenon that occurs both in isolated OSNs and in OSNs within epithelial slices. Thus, while the two olfactory ion channels act in concert to generate the overall response, each one controls specific aspects of the odorant-induced response. KEY POINTS: Mammalian olfactory sensory neurons (OSNs) generate odorant-induced responses by activating two ion channels sequentially in their ciliary membranes: a Na+, Ca2⁺-permeable cyclic nucleotide-gated (CNG) channel and the Ca2⁺-activated Cl⁻ channel TMEM16B. The CNG channel controls response delay and adaptation during odorant exposure, while TMEM16B amplifies the response and influences the time required for the response to reach its peak and terminate. OSNs lacking TMEM16B display faster response termination, allowing them to fire action potentials more reliably during rapid repeated stimulation. The CNG and TMEM16B channels have distinct and complementary roles in shaping the kinetics and reliability of odorant-induced responses in OSNs.

Object-oriented olfaction: challenges for chemosensation and for chemosensory research

Many animal species use olfaction to extract information about objects in their environment. Yet, the specific molecular signature that any given object emits varies due to various factors. Here, we detail why such variability makes chemosensory-mediated object recognition such a hard problem, and we propose that a major function of the elaborate chemosensory network is to overcome it. We describe previous work addressing different elements of the problem and outline future research directions that we consider essential for a full understanding of object-oriented olfaction. In particular, we call for extensive representation of olfactory object variability in chemical, behavioral, and electrophysiological analyses. While written with an emphasis on macrosmatic mammalian species, our arguments apply to all organisms that employ chemosensation to navigate complex environments.

The primacy model and the structure of olfactory space

Understanding sensory processing involves relating the stimulus space, its neural representation, and perceptual quality. In olfaction, the difficulty in establishing these links lies partly in the complexity of the underlying odor input space and perceptual responses. Based on the recently proposed primacy model for concentration invariant odor identity representation and a few assumptions, we have developed a theoretical framework for mapping the odor input space to the response properties of olfactory receptors. We analyze a geometrical structure containing odor representations in a multidimensional space of receptor affinities and describe its low-dimensional implementation, the primacy hull. We propose the implications of the primacy hull for the structure of feedforward connectivity in early olfactory networks. We test the predictions of our theory by comparing the existing receptor-ligand affinity and connectivity data obtained in the fruit fly olfactory system. We find that the Kenyon cells of the insect mushroom body integrate inputs from the high-affinity (primacy) sets of olfactory receptors in agreement with the primacy theory.

Intracellular Allosteric Antagonist of the Olfactory Receptor OR51E2

Olfactory receptors are members of class A (rhodopsin-like) family of G protein-coupled receptors (GPCRs). Their expression and function have been increasingly studied in nonolfactory tissues, and many have been identified as potential therapeutic targets. In this manuscript, we focus on the discovery of novel ligands for the olfactory receptor family 51 subfamily E2 (OR51E2). We performed an artificial intelligence-based virtual drug screen of a ∼2.2 million small molecule library. Cell-based functional assay identified compound 80 (C80) as an antagonist and inverse agonist, and detailed pharmacological analysis revealed C80 acts as a negative allosteric modulator by significantly decreasing the agonist efficacy, while having a minimal effect on receptor affinity for agonist. C80 binds to an allosteric binding site formed by a network of nine residues localized in the intracellular parts of transmembrane domains 3, 5, 6, 7, and H8, which also partially overlaps with a G protein binding site. Mutational experiments of residues involved in C80 binding uncovered the significance of the C2406.37 position in blocking the activation-related conformational change and keeping the receptor in the inactive form. Our study provides a mechanistic understanding of the negative allosteric action of C80 on agonist-ctivated OR51E2. We believe the identification of the antagonist of OR51E2 will enable a multitude of studies aiming to determine the functional role of this receptor in specific biologic processes. SIGNIFICANCE STATEMENT: OR51E2 has been implicated in various biological processes, and its antagonists that can effectively modulate its activity have therapeutic potential. Here we report the discovery of a negative allosteric modulator of OR51E2 and provide a mechanistic understanding of its action. We demonstrate that this modulator has an inhibitory effect on the efficacy of the agonist for the receptor and reveal a network of nine residues that constitute its binding pocket, which also partially overlaps with the G protein binding site.

The physiological roles of anoctamin2/TMEM16B and anoctamin1/TMEM16A in chemical senses

Chemical senses allow animals to detect and discriminate a vast array of molecules. The olfactory system is responsible of the detection of small volatile molecules, while water dissolved molecules are detected by taste buds in the oral cavity. Moreover, many animals respond to signaling molecules such as pheromones and other semiochemicals through the vomeronasal organ. The peripheral organs dedicated to chemical detection convert chemical signals into perceivable information through the employment of diverse receptor types and the activation of multiple ion channels. Two ion channels, TMEM16B, also known as anoctamin2 (ANO2) and TMEM16A, or anoctamin1 (ANO1), encoding for Ca2+-activated Cl¯ channels, have been recently described playing critical roles in various cell types. This review aims to discuss the main properties of TMEM16A and TMEM16B-mediated currents and their physiological roles in chemical senses. In olfactory sensory neurons, TMEM16B contributes to amplify the odorant response, to modulate firing, response kinetics and adaptation. TMEM16A and TMEM16B shape the pattern of action potentials in vomeronasal sensory neurons increasing the interspike interval. In type I taste bud cells, TMEM16A is activated during paracrine signaling mediated by ATP. This review aims to shed light on the regulation of diverse signaling mechanisms and neuronal excitability mediated by Ca-activated Cl¯ channels, hinting at potential new roles for TMEM16A and TMEM16B in the chemical senses.

Distinct information conveyed to the olfactory bulb by feedforward input from the nose and feedback from the cortex

Sensory systems are organized hierarchically, but feedback projections frequently disrupt this order. In the olfactory bulb (OB), cortical feedback projections numerically match sensory inputs. To unravel information carried by these two streams, we imaged the activity of olfactory sensory neurons (OSNs) and cortical axons in the mouse OB using calcium indicators, multiphoton microscopy, and diverse olfactory stimuli. Here, we show that odorant mixtures of increasing complexity evoke progressively denser OSN activity, yet cortical feedback activity is of similar sparsity for all stimuli. Also, representations of complex mixtures are similar in OSNs but are decorrelated in cortical axons. While OSN responses to increasing odorant concentrations exhibit a sigmoidal relationship, cortical axonal responses are complex and nonmonotonic, which can be explained by a model with activity-dependent feedback inhibition in the cortex. Our study indicates that early-stage olfactory circuits have access to local feedforward signals and global, efficiently formatted information about odor scenes through cortical feedback.

Microbial, proteomic, and metabolomic profiling of the estrous cycle in wild house mice

Symbiotic microbial communities affect the host immune system and produce molecules contributing to the odor of an individual. In many mammalian species, saliva and vaginal fluids are important sources of chemical signals that originate from bacterial metabolism and may act as honest signals of health and reproductive status. In this study, we aimed to define oral and vaginal microbiomes and their dynamics throughout the estrous cycle in wild house mice. In addition, we analyzed a subset of vaginal proteomes and metabolomes to detect potential interactions with microbiomes. 16S rRNA sequencing revealed that both saliva and vagina are dominated by Firmicutes and Proteobacteria but differ at the genus level. The oral microbiome is more stable during the estrous cycle and most abundant bacteria belong to the genera Gemella and Streptococcus, while the vaginal microbiome shows higher bacterial diversity and dynamics during the reproductive cycle and is characterized by the dominance of Muribacter and Rodentibacter. These two genera cover around 50% of the bacterial community during estrus. Proteomic profiling of vaginal fluids revealed specific protein patterns associated with different estrous phases. Highly expressed proteins in estrus involve the keratinization process thus providing estrus markers (e.g., Hrnr) while some proteins are downregulated such as immune-related proteins that limit bacterial growth (Camp, Clu, Elane, Lyz2, and Ngp). The vaginal metabolome contains volatile compounds potentially involved in chemical communication, for example, ketones, aldehydes, and esters of carboxylic acids. Data integration of all three OMICs data sets revealed high correlations, thus providing evidence that microbiomes, host proteomes, and metabolomes may interact.IMPORTANCEOur data revealed dynamic changes in vaginal, but not salivary, microbiome composition during the reproductive cycle of wild mice. With multiple OMICs platforms, we provide evidence that changes in microbiota in the vaginal environment are accompanied by changes in the proteomic and metabolomics profiles of the host. This study describes the natural microbiota of wild mice and may contribute to a better understanding of microbiome-host immune system interactions during the hormonal and cellular changes in the female reproductive tract. Moreover, analysis of volatiles in the vaginal fluid shows particular substances that can be involved in chemical communication and reproductive behavior.

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.

Geosmin suppresses defensive behaviour and elicits unusual neural responses in honey bees

Geosmin is an odorant produced by bacteria in moist soil. It has been found to be extraordinarily relevant to some insects, but the reasons for this are not yet fully understood. Here we report the first tests of the effect of geosmin on honey bees. A stinging assay showed that the defensive behaviour elicited by the bee's alarm pheromone component isoamyl acetate (IAA) is strongly suppressed by geosmin. Surprisingly, the suppression is, however, only present at very low geosmin concentrations, and disappears at higher concentrations. We investigated the underlying mechanisms at the level of the olfactory receptor neurons by means of electroantennography, finding the responses to mixtures of geosmin and IAA to be lower than to pure IAA, suggesting an interaction of both compounds at the olfactory receptor level. Calcium imaging of the antennal lobe (AL) revealed that neuronal responses to geosmin decreased with increasing concentration, correlating well with the observed behaviour. Computational modelling of odour transduction and coding in the AL suggests that a broader activation of olfactory receptor types by geosmin in combination with lateral inhibition could lead to the observed non-monotonic increasing-decreasing responses to geosmin and thus underlie the specificity of the behavioural response to low geosmin concentrations.

Allosteric modulation of a human odorant receptor

Odor perception is first determined by how the myriad of environmental volatiles are detected at the periphery of the olfactory system. The combinatorial activation of dedicated odorant receptors generates enough encoding power for the discrimination of tens of thousands of odorants. Recent studies have revealed that odorant receptors undergo widespread inhibitory modulation of their activity when presented with mixtures of odorants, a property likely required to maintain discrimination and ensure sparsity of the code for complex mixtures. Here, we establish the role of human OR5AN1 in the detection of musks and identify distinct odorants capable of enhancing its activity in binary mixtures. Chemical and pharmacological characterization indicate that specific α-β unsaturated aliphatic aldehydes act as positive allosteric modulators. Sensory experiments show decreased odor detection threshold in humans, suggesting that allosteric modulation of odorant receptors is perceptually relevant and likely adds another layer of complexity to how odors are encoded in the peripheral olfactory system.

The Effects of Background Noise on a Biophysical Model of Olfactory Bulb Mitral Cells

The spiking activity of mitral cells (MC) in the olfactory bulb is a key attribute in olfactory sensory information processing to downstream cortical areas. A more detailed understanding of the modulation of MC spike statistics could shed light on mechanistic studies of olfactory bulb circuits and olfactory coding. We study the spike response of a recently developed single-compartment biophysical MC model containing seven known ionic currents and calcium dynamics subject to constant current input with background white noise. We observe rich spiking dynamics even with constant current input, including multimodal peaks in the interspike interval distribution (ISI). Although weak-to-moderate background noise for a fixed current input does not change the firing rate much, the spike dynamics can change dramatically, exhibiting non-monotonic spike variability not commonly observed in standard neuron models. We explain these dynamics with a phenomenological model of the ISI probability density function. Our study clarifies some of the complexities of MC spiking dynamics.

Biological constraints on configural odour mixture perception

Animals, including humans, detect odours and use this information to behave efficiently in the environment. Frequently, odours consist of complex mixtures of odorants rather than single odorants, and mixtures are often perceived as configural wholes, i.e. as odour objects (e.g. food, partners). The biological rules governing this 'configural perception' (as opposed to the elemental perception of mixtures through their components) remain weakly understood. Here, we first review examples of configural mixture processing in diverse species involving species-specific biological signals. Then, we present the original hypothesis that at least certain mixtures can be processed configurally across species. Indeed, experiments conducted in human adults, newborn rabbits and, more recently, in rodents and honeybees show that these species process some mixtures in a remarkably similar fashion. Strikingly, a mixture AB (A, ethyl isobutyrate; B, ethyl maltol) induces configural processing in humans, who perceive a mixture odour quality (pineapple) distinct from the component qualities (A, strawberry; B, caramel). The same mixture is weakly configurally processed in rabbit neonates, which perceive a particular odour for the mixture in addition to the component odours. Mice and honeybees also perceive the AB mixture configurally, as they respond differently to the mixture compared with its components. Based on these results and others, including neurophysiological approaches, we propose that certain mixtures are convergently perceived across various species of vertebrates/invertebrates, possibly as a result of a similar anatomical organization of their olfactory systems and the common necessity to simplify the environment's chemical complexity in order to display adaptive behaviours.

Understanding responses to chemical mixtures: looking forward from the past

Our goal in this article is to provide a perspective on how to understand the nature of responses to chemical mixtures. In studying responses to mixtures, researchers often identify "mixture interactions"-responses to mixtures that are not accurately predicted from the responses to the mixture's individual components. Critical in these studies is how to predict responses to mixtures and thus to identify a mixture interaction. We explore this issue with a focus on olfaction and on the first level of neural processing-olfactory sensory neurons-although we use examples from taste systems as well and we consider responses beyond sensory neurons, including behavior and psychophysics. We provide a broadly comparative perspective that includes examples from vertebrates and invertebrates, from genetic and nongenetic animal models, and from literature old and new. In the end, we attempt to recommend how to approach these problems, including possible future research directions.

Paradoxical relationship between speed and accuracy in olfactory figure-background segregation

In natural settings, many stimuli impinge on our sensory organs simultaneously. Parsing these sensory stimuli into perceptual objects is a fundamental task faced by all sensory systems. Similar to other sensory modalities, increased odor backgrounds decrease the detectability of target odors by the olfactory system. The mechanisms by which background odors interfere with the detection and identification of target odors are unknown. Here we utilized the framework of the Drift Diffusion Model (DDM) to consider possible interference mechanisms in an odor detection task. We first considered pure effects of background odors on either signal or noise in the decision-making dynamics and showed that these produce different predictions about decision accuracy and speed. To test these predictions, we trained mice to detect target odors that are embedded in random background mixtures in a two-alternative choice task. In this task, the inter-trial interval was independent of behavioral reaction times to avoid motivating rapid responses. We found that increased backgrounds reduce mouse performance but paradoxically also decrease reaction times, suggesting that noise in the decision making process is increased by backgrounds. We further assessed the contributions of background effects on both noise and signal by fitting the DDM to the behavioral data. The models showed that background odors affect both the signal and the noise, but that the paradoxical relationship between trial difficulty and reaction time is caused by the added noise.

Sensory systems are constantly stimulated by signals from many objects in the environment. Segmentation of important signals from the cluttered background is therefore a task that is faced by all sensory systems. For many mammalians, the sense of smell is the primary sense that guides many daily behaviors. As such, the olfactory system must be able to detect and identify odors of interest against varying and dynamic backgrounds. Here we studied how background odors interfere with the detection of target odors. We trained mice on a task in which they are presented with odor mixtures and are required to report whether they include either of two target odors. We analyze the behavioral data using a common model of sensory-guided decision-making—the drift-diffusion-model. In this model, decisions are influenced by two elements: a drift which is the signal produced by the stimulus, and noise. We show that the addition of background odors has a dual effect—a reduction in the drift, as well as an increase in the noise. The increased noise also causes more rapid decisions, thereby producing a paradoxical relationship between trial difficulty and decision speed; mice make faster decisions on more difficult trials.

Non-synaptic interactions between olfactory receptor neurons, a possible key feature of odor processing in flies

When flies explore their environment, they encounter odors in complex, highly intermittent plumes. To navigate a plume and, for example, find food, they must solve several challenges, including reliably identifying mixtures of odorants and their intensities, and discriminating odorant mixtures emanating from a single source from odorants emitted from separate sources and just mixing in the air. Lateral inhibition in the antennal lobe is commonly understood to help solving these challenges. With a computational model of the Drosophila olfactory system, we analyze the utility of an alternative mechanism for solving them: Non-synaptic ("ephaptic") interactions (NSIs) between olfactory receptor neurons that are stereotypically co-housed in the same sensilla. We find that NSIs improve mixture ratio detection and plume structure sensing and do so more efficiently than the traditionally considered mechanism of lateral inhibition in the antennal lobe. The best performance is achieved when both mechanisms work in synergy. However, we also found that NSIs decrease the dynamic range of co-housed ORNs, especially when they have similar sensitivity to an odorant. These results shed light, from a functional perspective, on the role of NSIs, which are normally avoided between neurons, for instance by myelination.

Finding the Brain in the Nose

Olfaction is fundamentally distinct from other sensory modalities. Natural odor stimuli are complex mixtures of volatile chemicals that interact in the nose with a receptor array that, in rodents, is built from more than 1,000 unique receptors. These interactions dictate a peripheral olfactory code, which in the brain is transformed and reformatted as it is broadcast across a set of highly interconnected olfactory regions. Here we discuss the problems of characterizing peripheral population codes for olfactory stimuli, of inferring the specific functions of different higher olfactory areas given their extensive recurrence, and of ultimately understanding how odor representations are linked to perception and action. We argue that, despite the differences between olfaction and other sensory modalities, addressing these specific questions will reveal general principles underlying brain function.

What the odor is not: Estimation by elimination

Olfactory systems use a small number of broadly sensitive receptors to combinatorially encode a vast number of odors. We propose a method of decoding such distributed representations by exploiting a statistical fact: Receptors that do not respond to an odor carry more information than receptors that do because they signal the absence of all odorants that bind to them. Thus, it is easier to identify what the odor is not rather than what the odor is. For realistic numbers of receptors, response functions, and odor complexity, this method of elimination turns an underconstrained decoding problem into a solvable one, allowing accurate determination of odorants in a mixture and their concentrations. We construct a neural network realization of our algorithm based on the structure of the olfactory pathway.

A physicochemical model of odor sampling

We present a general physicochemical sampling model for olfaction, based on established pharmacological laws, in which arbitrary combinations of odorant ligands and receptors can be generated and their individual and collective effects on odor representations and olfactory performance measured. Individual odor ligands exhibit receptor-specific affinities and efficacies; that is, they may bind strongly or weakly to a given receptor, and can act as strong agonists, weak agonists, partial agonists, or antagonists. Ligands interacting with common receptors compete with one another for dwell time; these competitive interactions appropriately simulate the degeneracy that fundamentally defines the capacities and limitations of odorant sampling. The outcome of these competing ligand-receptor interactions yields a pattern of receptor activation levels, thereafter mapped to glomerular presynaptic activation levels based on the convergence of sensory neuron axons. The metric of greatest interest is the mean discrimination sensitivity, a measure of how effectively the olfactory system at this level is able to recognize a small change in the physicochemical quality of a stimulus. This model presents several significant outcomes, both expected and surprising. First, adding additional receptors reliably improves the system's discrimination sensitivity. Second, in contrast, adding additional ligands to an odorscene initially can improve discrimination sensitivity, but eventually will reduce it as the number of ligands increases. Third, the presence of antagonistic ligand-receptor interactions produced clear benefits for sensory system performance, generating higher absolute discrimination sensitivities and increasing the numbers of competing ligands that could be present before discrimination sensitivity began to be impaired. Finally, the model correctly reflects and explains the modest reduction in odor discrimination sensitivity exhibited by transgenic mice in which the specificity of glomerular targeting by primary olfactory neurons is partially disrupted.

Position Review: Functional Selectivity in Mammalian Olfactory Receptors

There is increasing appreciation that G-protein-coupled receptors (GPCRs) can initiate diverse cellular responses by activating multiple G proteins, arrestins, and other biochemical effectors. Structurally different ligands targeting the same receptor are thought to stabilize the receptor in multiple distinct active conformations such that specific subsets of signaling effectors are engaged at the exclusion of others, creating a bias toward a particular outcome, which has been referred to as ligand-induced selective signaling, biased agonism, ligand-directed signaling, and functional selectivity, among others. The potential involvement of functional selectivity in mammalian olfactory signal transduction has received little attention, notwithstanding the fact that mammalian olfactory receptors comprise the largest family of mammalian GPCRs. This position review considers the possibility that, although such complexity in G-protein function may have been lost in the specialization of olfactory receptors to serve as sensory receptors, the ability of olfactory receptor neurons (ORNs) to function as signal integrators and growing appreciation that this functionality is widespread in the receptor population suggest otherwise. We pose that functional selectivity driving 2 opponent inputs have the potential to generate an output that reflects the balance of ligand-dependent signaling, the direction of which could be either suppressive or synergistic and, as such, needs to be considered as a mechanistic basis for signal integration in mammalian ORNs.

Effects of Multi-Component Backgrounds of Volatile Plant Compounds on Moth Pheromone Perception

Simple Summary

It is well acknowledged that some of the volatile plant compounds (VPC) naturally present in insect natural habitats alter the perception of their own pheromone when presented individually as a background to pheromone. However, the effects of mixing VPCs as they appear to insects in natural olfactory landscapes are poorly understood. We measured the activity of brain neurons and neurons that detect a sex pheromone component in a moth antenna, while exposed to simple or composite backgrounds of VPCs representative of the odorant variety encountered by this moth. Maps of activities were built using calcium imaging to visualize which brain areas were most affected by VPCs. In the antenna, we observed differences in VPC capacity to elicit firing response that cannot be explained by differences in stimulus intensities because we adjusted concentrations according to volatility. The neuronal network, which reformats the input from antenna neurons in the brain, did not improve pheromone salience. We postulate that moth olfactory system evolved to increase sensitivity and encode fast changes of concentration at some cost for signal extraction. Comparing blends to single compounds indicated that a blend shows the activity of its most active component, VPC salience seems more important than background complexity.

Abstract

The volatile plant compounds (VPC) alter pheromone perception by insects but mixture effects inside insect olfactory landscapes are poorly understood. We measured the activity of receptor neurons tuned to Z7-12Ac (Z7-ORN), a pheromone component, in the antenna and central neurons in male Agrotis ipsilon while exposed to simple or composite backgrounds of a panel of VPCs representative of the odorant variety encountered by a moth. Maps of activities were built using calcium imaging to visualize which areas in antennal lobes (AL) were affected by VPCs. We compared the VPC activity and their impact as backgrounds at antenna and AL levels, individually or in blends. At periphery, VPCs showed differences in their capacity to elicit Z7-ORN firing response that cannot be explained by differences in stimulus intensities because we adjusted concentrations according to vapor pressures. The AL neuronal network, which reformats the ORN input, did not improve pheromone salience. We postulate that the AL network evolved to increase sensitivity and to encode for fast changes of pheromone at some cost for signal extraction. Comparing blends to single compounds indicated that a blend shows the activity of its most active component. VPC salience seems to be more important than background complexity.

Mixture interactions at mammalian olfactory receptors are dependent on the cellular environment

Functional characterization of mammalian olfactory receptors (ORs) remains a major challenge to ultimately understanding the olfactory code. Here, we compare the responses of the mouse Olfr73 ectopically expressed in olfactory sensory neurons using AAV gene delivery in vivo and expressed in vitro in cell culture. The response dynamics and concentration-dependence of agonists for the ectopically expressed Olfr73 were similar to those reported for the endogenous Olfr73, however the antagonism previously reported between its cognate agonist and several antagonists was not replicated in vivo. Expressing the OR in vitro reproduced the antagonism reported for short odor pulses, but not for prolonged odor exposure. Our findings suggest that both the cellular environment and the stimulus dynamics shape the functionality of Olfr73 and argue that characterizing ORs in 'native' conditions, rather than in vitro, provides a more relevant understanding of ligand-OR interactions.

Brief olfactory learning drives perceptive sensitivity in newborn rabbits: New insights in peripheral processing of odor mixtures and induction

Perception of the wide, complex and moving odor world requires that the olfactory system engages processing mechanisms ensuring detection, discrimination and environment adaptation, as early as the peripheral stages. Odor items are mainly elicited by odorant mixtures which give rise to either elemental or configural perceptions. Here, we first explored the contribution of the peripheral olfactory system to configural and elemental perception through odorant interactions at the olfactory receptor (OR) level. This was done in newborn rabbits, which offer the opportunity to pair peripheral electrophysiology and well characterized behavioral responses to two binary mixtures, AB and A'B', which differ in their component ratio (A: ethyl isobutyrate, B: ethyl maltol), and that rabbit pups respectively perceived configurally and elementally. Second, we studied the influence on peripheral reactivity of the brief but powerful learning of one mixture component (odorant B), conditioned by association with the mammary pheromone (MP), which allowed us to assess the possible implication of the phenomenon called induction in neonatal odor learning. Induction is a plasticity mechanism expected to alter both the peripheral electrophysiological responses to, and perceptual detection threshold of, the conditioned stimulus. The results reveal that perceptual modes are partly rooted in differential peripheral processes, the AB configurally perceived mixture mirroring odorant antagonist interactions at OR level to a lesser extent than the A'B' elementally perceived mixture. Further, the results highlight that a single and brief MP-induced odor learning episode is sufficient to alter peripheral responses to the conditioned stimulus and mixtures including it, and shifts the conditioned stimulus detection threshold towards lower concentrations. Thus, MP-induced odor learning relies on induction phenomenon in newborn rabbits.

The cyclic AMP signaling pathway in the rodent main olfactory system

Odor perception begins with the detection of odorant molecules by the main olfactory epithelium located in the nasal cavity. Odorant molecules bind to and activate a large family of G-protein-coupled odorant receptors and trigger a cAMP-mediated transduction cascade that converts the chemical stimulus into an electrical signal transmitted to the brain. Morever, odorant receptors and cAMP signaling plays a relevant role in olfactory sensory neuron development and axonal targeting to the olfactory bulb. This review will first explore the physiological response of olfactory sensory neurons to odorants and then analyze the different components of cAMP signaling and their different roles in odorant detection and olfactory sensory neuron development.

Odor coding in the mammalian olfactory epithelium

Noses are extremely sophisticated chemical detectors allowing animals to use scents to interpret and navigate their environments. Odor detection starts with the activation of odorant receptors (ORs), expressed in mature olfactory sensory neurons (OSNs) populating the olfactory mucosa. Different odorants, or different concentrations of the same odorant, activate unique ensembles of ORs. This mechanism of combinatorial receptor coding provided a possible explanation as to why different odorants are perceived as having distinct odors. Aided by new technologies, several recent studies have found that antagonist interactions also play an important role in the formation of the combinatorial receptor code. These findings mark the start of a new era in the study of odorant-receptor interactions and add a new level of complexity to odor coding in mammals.

Inhibitory signaling in mammalian olfactory transduction potentially mediated by Gαo

Olfactory GPCRs (ORs) in mammalian olfactory receptor neurons (ORNs) mediate excitation through the Gα_s family member Gα_olf. Here we tentatively associate a second G protein, Gα_o, with inhibitory signaling in mammalian olfactory transduction by first showing that odor evoked phosphoinositide 3-kinase (PI3K)-dependent inhibition of signal transduction is absent in the native ORNs of mice carrying a conditional OMP-Cre based knockout of Gα_o. We then identify an OR from native rat ORNs that are activated by octanol through cyclic nucleotide signaling and inhibited by citral in a PI3K-dependent manner. We show that the OR activates cyclic nucleotide signaling and PI3K signaling in a manner that reflects its functionality in native ORNs. Our findings lay the groundwork to explore the interesting possibility that ORs can interact with two different G proteins in a functionally identified, ligand-dependent manner to mediate opponent signaling in mature mammalian ORNs.

A biohybrid nose for evaluation of odor masking in the peripheral olfactory system

Olfaction is a synthetic sense in which odor mixtures elicit emergent perceptions at the expense of perceiving the individual components. The most common result of mixing two odors is masking one component by another. However, there is lack of analytical techniques for measuring the sense of smell, which is mediated by cross-odorant interactions. Here, we propose a biohybrid nose for objective and quantitative evaluation of malodor masking efficiency of perfumed products. This biohybrid nose is constructed by integrating mammalian olfactory epithelium with microelectrode array chip to read out the olfactory information as electrical signal from multiple tissue sites. The intrinsic odor response of olfactory epithelium is found to be represented by widespread spatiotemporal oscillatory activity. The masking efficiency of fragrance is quantified by calculating the relative difference between the malodor and the binary mixture (malodor + fragrance) response patterns. Results indicate that masking efficiency of fragrance is concentration-dependent, whereas completely masking may occurs when fragrance is employed at a concentration 2-3 orders of magnitude higher than malodor. This study demonstrates for the first time that capitalizing on the biological sense of smell to create biohybrid system provides an effective technique to resolve more complex biosensing-related issues such as odor interactions in mixtures.

Non-synaptic interactions between olfactory receptor neurons, a possible key feature of odor processing in flies.. [DOI: 10.1101/2020.07.23.217216]

When flies explore their environment, they encounter odors in complex, highly intermittent plumes. To navigate a plume and, for example, find food, they must solve several challenges, including reliably identifying mixtures of odorants and their intensities, and discriminating odorant mixtures emanating from a single source from odorants emitted from separate sources and just mixing in the air. Lateral inhibition in the antennal lobe is commonly understood to help solving these challenges. With a computational model of the Drosophila olfactory system, we analyze the utility of an alternative mechanism for solving them: Non-synaptic (“ephaptic”) interactions (NSIs) between olfactory receptor neurons that are stereotypically co-housed in the same sensilla.

We found that NSIs improve mixture ratio detection and plume structure sensing and they do so more efficiently than the traditionally considered mechanism of lateral inhibition in the antennal lobe. However, we also found that NSIs decrease the dynamic range of co-housed ORNs, especially when they have similar sensitivity to an odorant. These results shed light, from a functional perspective, on the role of NSIs, which are normally avoided between neurons, for instance by myelination.

Author summary

Myelin is important to isolate neurons and avoid disruptive electrical interference between them; it can be found in almost any neural assembly. However, there are a few exceptions to this rule and it remains unclear why. One particularly interesting case is the electrical interaction between olfactory sensory neurons co-housed in the sensilla of insects. Here, we created a computational model of the early stages of the Drosophila olfactory system and observed that the electrical interference between olfactory receptor neurons can be a useful trait that can help flies, and other insects, to navigate the complex plumes of odorants in their natural environment.

With the model we were able to shed new light on the trade-off of adopting this mechanism: We found that the non-synaptic interactions (NSIs) improve both the identification of the concentration ratio in mixtures of odorants and the discrimination of odorant mixtures emanating from a single source from odorants emitted from separate sources – both highly advantageous. However, they also decrease the dynamic range of the olfactory sensory neurons – a clear disadvantage.

Olfaction: Receptor Antagonistes

Odor receptors of the mammalian olfactory system have long been known to be activated in combinatorial fashion by odorants. A large-scale study now reveals that inhibition of receptors by odorants is comparably prevalent and combinatorial.

Odorant Receptor Inhibition Is Fundamental to Odor Encoding

Most natural odors are complex mixtures of volatile components, competing to bind odorant receptors (ORs) expressed in olfactory sensory neurons (OSNs) of the nose. To date, surprisingly little is known about how OR antagonism shapes neuronal representations in the detection layer of the olfactory system. Here, we investigated its prevalence, the degree to which it disrupts OR ensemble activity, and its conservation across phylogenetically related ORs. Calcium imaging microscopy of dissociated OSNs revealed significant inhibition, often complete attenuation, of responses to indole-a commonly occurring volatile associated with both floral and fecal odors-by a set of 36 tested odorants. To confirm an OR mechanism for the observed inhibition, we performed single-cell transcriptomics on OSNs exhibiting specific response profiles to a diagnostic panel of odorants and identified three paralogous receptors-Olfr740, Olfr741, and Olfr743-which, when tested in vitro, recapitulated OSN responses. We screened ten ORs from the Olfr740 gene family with ∼800 perfumery-related odorants spanning a range of chemical scaffolds and functional groups. Over half of these compounds (430) antagonized at least one of the ten ORs. OR activity fitted a mathematical model of competitive receptor binding and suggests normalization of OSN ensemble responses to odorant mixtures is the rule rather than the exception. In summary, we observed OR antagonism occurred frequently and in a combinatorial manner. Thus, extensive receptor-mediated computation of mixture information appears to occur in the olfactory epithelium prior to transmission of odor information to the olfactory bulb.

Antagonistic odor interactions in olfactory sensory neurons are widespread in freely breathing mice

Odor landscapes contain complex blends of molecules that each activate unique, overlapping populations of olfactory sensory neurons (OSNs). Despite the presence of hundreds of OSN subtypes in many animals, the overlapping nature of odor inputs may lead to saturation of neural responses at the early stages of stimulus encoding. Information loss due to saturation could be mitigated by normalizing mechanisms such as antagonism at the level of receptor-ligand interactions, whose existence and prevalence remains uncertain. By imaging OSN axon terminals in olfactory bulb glomeruli as well as OSN cell bodies within the olfactory epithelium in freely breathing mice, we find widespread antagonistic interactions in binary odor mixtures. In complex mixtures of up to 12 odorants, antagonistic interactions are stronger and more prevalent with increasing mixture complexity. Therefore, antagonism is a common feature of odor mixture encoding in OSNs and helps in normalizing activity to reduce saturation and increase information transfer.

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.

Mixture and concentration effects on odorant receptor response patterns in vivo

Natural odors are mixtures of volatile chemicals (odorants). Odors are encoded as responses of distinct subsets of the hundreds of odorant receptors and trace amine-associated receptors expressed monogenically by olfactory sensory neurons. This is an elegantly simple mechanism for differentially encoding odors but it is susceptible to complex dose-response relationships and interactions between odorants at receptors, which may help explain olfactory phenomena such as mixture suppression, synthetic versus elemental odor processing, and poorly predictable perceptual outcomes of new odor mixtures. In this study in vivo tests in freely behaving mice confirm evidence of a characteristic receptor response pattern consisting of a few receptors with strong responses and a greater number of weakly responding receptors. Odorant receptors responsive to an odor are often unrelated and widely divergent in sequence, even when the odor consists of a single species of odorant. Odorant receptor response patterns to a citrus odor broaden with concentration. Some highly sensitive receptors respond only to a low concentration but others respond in proportion to concentration, a feature that may be critical for concentration-invariant perception. Other tests find evidence of interactions between odorants in vivo. All of the odorant receptor responses to a moderate concentration of the fecal malodor indole are suppressed by a high concentration of the floral odorant, α-ionone. Such suppressive effects are consistent with prior evidence that odorant interactions at individual odorant receptors are common.

Widespread receptor-driven modulation in peripheral olfactory coding

Olfactory responses to single odors have been well characterized but in reality we are continually presented with complex mixtures of odors. We performed high-throughput analysis of single-cell responses to odor blends using Swept Confocally Aligned Planar Excitation (SCAPE) microscopy of intact mouse olfactory epithelium, imaging ~10,000 olfactory sensory neurons in parallel. In large numbers of responding cells, mixtures of odors did not elicit a simple sum of the responses to individual components of the blend. Instead, many neurons exhibited either antagonism or enhancement of their response in the presence of another odor. All eight odors tested acted as both agonists and antagonists at different receptors. We propose that this peripheral modulation of responses increases the capacity of the olfactory system to distinguish complex odor mixtures.

A molecular odorant transduction model and the complexity of spatio-temporal encoding in the Drosophila antenna

Over the past two decades, substantial amount of work has been conducted to characterize different odorant receptors, neuroanatomy and odorant response properties of the early olfactory system of Drosophila melanogaster. Yet many odorant receptors remain only partially characterized, and the odorant transduction process and the axon hillock spiking mechanism of the olfactory sensory neurons (OSNs) have yet to be fully determined. Identity and concentration, two key characteristics of the space of odorants, are encoded by the odorant transduction process. Detailed molecular models of the odorant transduction process are, however, scarce for fruit flies. To address these challenges we advance a comprehensive model of fruit fly OSNs as a cascade consisting of an odorant transduction process (OTP) and a biophysical spike generator (BSG). We model odorant identity and concentration using an odorant-receptor binding rate tensor, modulated by the odorant concentration profile, and an odorant-receptor dissociation rate tensor, and quantitatively describe the mechanics of the molecular ligand binding/dissociation of the OTP. We model the BSG as a Connor-Stevens point neuron. The resulting spatio-temporal encoding model of the Drosophila antenna provides a theoretical foundation for understanding the neural code of both odorant identity and odorant concentration and advances the state-of-the-art in a number of ways. First, it quantifies on the molecular level the spatio-temporal level of complexity of the transformation taking place in the antennae. The concentration-dependent spatio-temporal code at the output of the antenna circuits determines the level of complexity of olfactory processing in the downstream neuropils, such as odorant recognition and olfactory associative learning. Second, the model is biologically validated using multiple electrophysiological recordings. Third, the model demonstrates that the currently available data for odorant-receptor responses only enable the estimation of the affinity of the odorant-receptor pairs. The odorant-dissociation rate is only available for a few odorant-receptor pairs. Finally, our model calls for new experiments for massively identifying the odorant-receptor dissociation rates of relevance to flies.

Identity and concentration, intrinsically embedded in the odorant space, are two key characteristics of olfactory coding that define the level of complexity of neural processing throughout the olfactory system in the fruit fly. In this paper we advance a theoretical foundation for understanding these two characteristics by quantifying mathematically the odorant space and devising a biophysical model of the olfactory sensory neurons (OSNs). To validate our modeling approach, we propose and apply an algorithm to estimate the affinity value and the dissociation rate, the two characteristics that define odorant identity, of multiple odorant-receptor pairs. We then evaluate our model with a multitude of odorant waveforms and demonstrate that the model output reproduces the temporal responses of OSNs obtained from in vivo electrophysiology recordings. Furthermore, we evaluate the model at the OSN population level and quantify on the molecular level the spatio-temporal level of complexity of the transformation taking place between the odorant space and the OSNs. The resulting concentration-dependent spatio-temporal code determines the level of complexity of the input space driving olfactory processing in the downstream neuropils. Lastly, our model demonstrates that the currently available data for OSN responses only enables estimation of affinity value. This calls for new experiments for massively identifying the odorant-receptor dissociation rates of relevance to flies.

Rapid online learning and robust recall in a neuromorphic olfactory circuit

We present a neural algorithm for the rapid online learning and identification of odourant samples under noise, based on the architecture of the mammalian olfactory bulb and implemented on the Intel Loihi neuromorphic system. As with biological olfaction, the spike timing-based algorithm utilizes distributed, event-driven computations and rapid (one-shot) online learning. Spike timing-dependent plasticity rules operate iteratively over sequential gamma-frequency packets to construct odour representations from the activity of chemosensor arrays mounted in a wind tunnel. Learned odourants then are reliably identified despite strong destructive interference. Noise resistance is further enhanced by neuromodulation and contextual priming. Lifelong learning capabilities are enabled by adult neurogenesis. The algorithm is applicable to any signal identification problem in which high-dimensional signals are embedded in unknown backgrounds.

Modulation of the combinatorial code of odorant receptor response patterns in odorant mixtures

The perception of odors relies on combinatorial codes consisting of odorant receptor (OR) response patterns to encode odor identity. Modulation of these patterns by odorant interactions at ORs potentially explains several olfactory phenomena: mixture suppression, unpredictable sensory outcomes, and the perception of odorant mixtures as unique objects. We determined OR response patterns to 4 odorants and 3 binary mixtures in vivo in mice, identifying 30 responsive ORs. These patterns typically had a few strongly responsive ORs and a greater number of weakly responsive ORs. ORs responsive to an odorant were often unrelated sequences distributed across several OR subfamilies. Mixture responses predicted pharmacological interactions between odorants, which were tested in vitro by heterologous expression of ORs in cultured cells, providing independent evidence confirming odorant agonists for 13 ORs and identifying both suppressive and additive effects. This included 11 instances of antagonism of ORs by an odorant, 1 instance of additive responses to a binary mixture, 1 instance of suppression of a strong agonist by a weak agonist, and the discovery of an inverse agonist for an OR. Interactions between odorants at ORs are common even when the odorants are not known to interact perceptually in humans, and in some cases interactions at mouse ORs correlate with the ability of humans to perceive an odorant in a mixture.

Odor Stimuli: Not Just Chemical Identity

In most sensory modalities the underlying physical phenomena are well understood, and stimulus properties can be precisely controlled. In olfaction, the situation is different. The presence of specific chemical compounds in the air (or water) is the root cause for perceived odors, but it remains unknown what organizing principles, equivalent to wavelength for light, determine the dimensions of odor space. Equally important, but less in the spotlight, odor stimuli are also complex with respect to their physical properties, including concentration and time-varying spatio-temporal distribution. We still lack a complete understanding or control over these properties, in either experiments or theory. In this review, we will concentrate on two important aspects of the physical properties of odor stimuli beyond the chemical identity of the odorants: (1) The amplitude of odor stimuli and their temporal dynamics. (2) The spatio-temporal structure of odor plumes in a natural environment. Concerning these issues, we ask the following questions: (1) Given any particular experimental protocol for odor stimulation, do we have a realistic estimate of the odorant concentration in the air, and at the olfactory receptor neurons? Can we control, or at least know, the dynamics of odorant concentration at olfactory receptor neurons? (2) What do we know of the spatio-temporal structure of odor stimuli in a natural environment both from a theoretical and experimental perspective? And how does this change if we consider mixtures of odorants? For both topics, we will briefly summarize the underlying principles of physics and review the experimental and theoretical Neuroscience literature, focusing on the aspects that are relevant to animals’ physiology and behavior. We hope that by bringing the physical principles behind odor plume landscapes to the fore we can contribute to promoting a new generation of experiments and models.

Eucalyptol Masks the Olfactory But Not the Trigeminal Sensation of Ammonia

Eucalyptol is a substance with rather pleasant olfactory and trigeminal characteristics and is thus suggested as an efficient tool for malodor coverage. In this study ammonia would be the malodor substance such as is found in cat litter or hair coloration. We investigated the potential of eucalyptol to inhibit both the olfactory as well as the trigeminal sensation of ammonia. For this purpose, we mixed eucalyptol and ammonia and compared odor component intensities. After being presented with either the pure odors or a binary mixture thereof, 21 young and healthy participants had to lateralize the odors and rate component (eucalyptol and ammonia) and total intensity. Analysis of intensity ratings revealed hypoadditivity (total mixture intensity was less than the sum of the total intensity of the single components). Significant interaction effects verified that mixing eucalyptol and ammonia only affected the perceived intensity of ammonia. Comparing the odor components within the pure and mixed stimuli, the ammonia component was rated as significantly less intense in the mixture compared to pure ammonia whereas the eucalyptol component was rated equal in the pure and mixed condition. On the basis of lateralization scores, we observed trigeminal mixture enhancement. We conclude that eucalyptol is a suitable masking agent to cover the unpleasant smell of ammonia; however, it fails to serve as an ammonia counterirritant because it lacks the ability to mask the trigeminal sensation of ammonia.

Competitive binding predicts nonlinear responses of olfactory receptors to complex mixtures

In color vision, the quantitative rules for mixing lights to make a target color are well understood. By contrast, the rules for mixing odorants to make a target odor remain elusive. A solution to this problem in vision relied on characterizing receptor responses to different wavelengths of light and subsequently relating these responses to perception. In olfaction, experimentally measuring receptor responses to a representative set of complex mixtures is intractable due to the vast number of possibilities. To meet this challenge, we develop a biophysical model that predicts mammalian receptor responses to complex mixtures using responses to single odorants. The dominant nonlinearity in our model is competitive binding (CB): Only one odorant molecule can attach to a receptor binding site at a time. This simple framework predicts receptor responses to mixtures of up to 12 monomolecular odorants to within 15% of experimental observations and provides a powerful method for leveraging limited experimental data. Simple extensions of our model describe phenomena such as synergy, overshadowing, and inhibition. We demonstrate that the presence of such interactions can be identified via systematic deviations from the competitive-binding model.

Odor Stimuli: Not Just Chemical Identity

In most sensory modalities the underlying physical phenomena are well understood, and stimulus properties can be precisely controlled. In olfaction, the situation is different. The presence of specific chemical compounds in the air (or water) is the root cause for perceived odors, but it remains unknown what organizing principles, equivalent to wavelength for light, determine the dimensions of odor space. Equally important, but less in the spotlight, odor stimuli are also complex with respect to their physical properties, including concentration and time-varying spatio-temporal distribution. We still lack a complete understanding or control over these properties, in either experiments or theory. In this review, we will concentrate on two important aspects of the physical properties of odor stimuli beyond the chemical identity of the odorants: (1) The amplitude of odor stimuli and their temporal dynamics. (2) The spatio-temporal structure of odor plumes in a natural environment. Concerning these issues, we ask the following questions: (1) Given any particular experimental protocol for odor stimulation, do we have a realistic estimate of the odorant concentration in the air, and at the olfactory receptor neurons? Can we control, or at least know, the dynamics of odorant concentration at olfactory receptor neurons? (2) What do we know of the spatio-temporal structure of odor stimuli in a natural environment both from a theoretical and experimental perspective? And how does this change if we consider mixtures of odorants? For both topics, we will briefly summarize the underlying principles of physics and review the experimental and theoretical Neuroscience literature, focusing on the aspects that are relevant to animals' physiology and behavior. We hope that by bringing the physical principles behind odor plume landscapes to the fore we can contribute to promoting a new generation of experiments and models.

Odorant mixtures elicit less variable and faster responses than pure odorants

In natural environments, odors are typically mixtures of several different chemical compounds. However, the implications of mixtures for odor processing have not been fully investigated. We have extended a standard olfactory receptor model to mixtures and found through its mathematical analysis that odorant-evoked activity patterns are more stable across concentrations and first-spike latencies of receptor neurons are shorter for mixtures than for pure odorants. Shorter first-spike latencies arise from the nonlinear dependence of binding rate on odorant concentration, commonly described by the Hill coefficient, while the more stable activity patterns result from the competition between different ligands for receptor sites. These results are consistent with observations from numerical simulations and physiological recordings in the olfactory system of insects. Our results suggest that mixtures allow faster and more reliable olfactory coding, which could be one of the reasons why animals often use mixtures in chemical signaling.

Antagonism in olfactory receptor neurons and its implications for the perception of odor mixtures

Natural environments feature mixtures of odorants of diverse quantities, qualities and complexities. Olfactory receptor neurons (ORNs) are the first layer in the sensory pathway and transmit the olfactory signal to higher regions of the brain. Yet, the response of ORNs to mixtures is strongly non-additive, and exhibits antagonistic interactions among odorants. Here, we model the processing of mixtures by mammalian ORNs, focusing on the role of inhibitory mechanisms. We show how antagonism leads to an effective ‘normalization’ of the ensemble ORN response, that is, the distribution of responses of the ORN population induced by any mixture is largely independent of the number of components in the mixture. This property arises from a novel mechanism involving the distinct statistical properties of receptor binding and activation, without any recurrent neuronal circuitry. Normalization allows our encoding model to outperform non-interacting models in odor discrimination tasks, leads to experimentally testable predictions and explains several psychophysical experiments in humans.

When ordering in a coffee shop, you probably recognize and enjoy the aroma of freshly roasted coffee beans. But as well as coffee, you can also smell the croissants behind the counter and maybe even the perfume or cologne of the person next to you. Each of these scents consists of a collection of chemicals, or odorants. To distinguish between the aroma of coffee and that of croissants, your brain must group the odorants appropriately and then keep the groups separate from each other.

This is not a trivial task. Odorants bind to proteins called odorant receptors found on the surface of cells in the nose called olfactory receptor neurons. But each odorant does not have its own dedicated receptor. Instead, a single odorant will bind to multiple types of odorant receptors, and thus, each olfactory receptor neuron may respond to multiple odorants. So how does the brain encode mixtures of odorants in a way that allows us to distinguish one aroma from another?

Reddy, Zak et al. have developed a computational model to explain how this process works. The model assumes that an odorant triggers a response in an olfactory receptor neuron via two steps. First, the odorant binds to an odorant receptor. Second, the bound odorant activates the receptor. But the odorant that binds most strongly to a receptor will not necessarily be the odorant that is best at activating that receptor.

This allows a phenomenon called competitive antagonism to occur. This is when one odorant in a mixture binds more strongly to a receptor than the other odorants, but only weakly activates that receptor. In so doing, the strongly bound odorant prevents the other odorants from binding to and activating the receptor. This helps tame the dominating influence of background odors, which might otherwise saturate the responses of individual olfactory receptor neurons.

Reddy, Zak et al. show that processes such as competitive antagonism enable olfactory receptor neurons to encode all of the odors within a mixture. The model can explain various phenomena observed in experiments and it adds to our understanding of how the brain generates our sense of smell. The model may also be relevant to other biological systems that must filter weak signals from a dominant background. These include the immune system, which must distinguish a small set of foreign proteins from the much larger number of proteins that make up our bodies.

In Vivo Electrophysiological Recordings of Olfactory Receptor Neuron Units and Electro-olfactograms in Anesthetized Rats

In vivo recordings of single olfactory receptor neurons and electro-olfactograms (EOG, field potentials from the olfactory mucosa) provide insights into the olfactory processing properties of the olfactory peripheral stage. Because the olfactory receptor neurons are very small electrical generators, it is not easy to unitarily record them in amphibians, reptilians, and fishes. In mammals such recordings are even more difficult to obtain: primarily due to the anatomical configuration in complex turbinates of the olfactory mucosa and its propensity to hemorrhage during surgery; secondarily due to the fact that olfactory receptor neurons are held in closely packed clusters in the olfactory mucosa and are difficult to isolate, from the electrophysiological recording point of view. Here we describe the material and methods we used in vivo, in rats-occasionally, also tested in mice-to get simultaneously receptor neuron single and electro-olfactogram recordings, from septal region or the endoturbinate II, in freely breathing or tracheotomized anesthetized animals. Recording EOG in parallel with receptor neuron units provide, by reflecting the population response to the olfactory stimulus, the continuous assurance of the good physiological state and reactivity of the olfactory epithelium. This configuration will ensure that when a single ORN does not respond to a stimulus it resulted from its qualitative selectivity and not from the olfactory mucosa damaged status.

Odor-evoked inhibition of olfactory sensory neurons drives olfactory perception in Drosophila

Inhibitory response occurs throughout the nervous system, including the peripheral olfactory system. While odor-evoked excitation in peripheral olfactory cells is known to encode odor information, the molecular mechanism and functional roles of odor-evoked inhibition remain largely unknown. Here, we examined Drosophila olfactory sensory neurons and found that inhibitory odors triggered outward receptor currents by reducing the constitutive activities of odorant receptors, inhibiting the basal spike firing in olfactory sensory neurons. Remarkably, this odor-evoked inhibition of olfactory sensory neurons elicited by itself a full range of olfactory behaviors from attraction to avoidance, as did odor-evoked olfactory sensory neuron excitation. These results indicated that peripheral inhibition is comparable to excitation in encoding sensory signals rather than merely regulating excitation. Furthermore, we demonstrated that a bidirectional code with both odor-evoked inhibition and excitation in single olfactory sensory neurons increases the odor-coding capacity, providing a means of efficient sensory encoding.

It is well established that odor-evoked excitation in olfactory sensory neurons (OSNs) encodes odor information. Here the authors report that odor-evoked inhibition in OSNs of Drosophila also encodes odor identity, and can in itself drive both attraction and avoidance behaviors.