Mixture and concentration effects on odorant receptor response patterns in vivo

Odor mixtures: A chord with silent notes

The olfactory world is one of complex mixtures and blends containing up to hundreds of molecules. Many of those molecules can act as agonists, antagonists or enhancers at different receptors. This complicates the mechanism by which higher centers construct perceptions of complex mixtures. We propose that along with structural chemistry, psychophysics, the techniques of medicinal chemistry and machine learning can begin to shed light on this difficult neural problem.

Effects of 3-octen-2-one on human olfactory receptor responses to vanilla flavor

Most of the odors that humans perceive daily are complex odors. It is believed that the modulation, enhancement, and suppression of overall complex odors are caused by interactions between odor molecules. In this study, to understand the interaction between odor molecules at the level of human olfactory receptor responses, the effects of 3-octen-2-one, which has been shown to modulate vanilla flavors, were analyzed using a human olfactory receptor sensor that uses all human olfactory receptors (388 types) as sensing molecules. As a result, the response intensity of 1 common receptor (OR1D2) was synergistically enhanced in vanilla flavor with 3-octen-2-one compared with vanilla flavor, and the response of 1 receptor (OR5K1) to vanilla flavor was completely suppressed. These results strongly suggested that the response of human olfactory receptors to complex odors is enhanced or suppressed by relatively few other odor molecules.

More than meets the AI: The possibilities and limits of machine learning in olfaction

Can machine learning crack the code in the nose? Over the past decade, studies tried to solve the relation between chemical structure and sensory quality with Big Data. These studies advanced computational models of the olfactory stimulus, utilizing artificial intelligence to mine for clear correlations between chemistry and psychophysics. Computational perspectives promised to solve the mystery of olfaction with more data and better data processing tools. None of them succeeded, however, and it matters as to why this is the case. This article argues that we should be deeply skeptical about the trend to black-box the sensory system's biology in our theories of perception. Instead, we need to ground both stimulus models and psychophysical data on real causal-mechanistic explanations of the olfactory system. The central question is: Would knowledge of biology lead to a better understanding of the stimulus in odor coding than the one utilized in current machine learning models? That is indeed the case. Recent studies about receptor behavior have revealed that the olfactory system operates by principles not captured in current stimulus-response models. This may require a fundamental revision of computational approaches to olfaction, including its psychological effects. To analyze the different research programs in olfaction, we draw on Lloyd's "Logic of Research Questions," a philosophical framework which assists scientists in explicating the reasoning, conceptual commitments, and problems of a modeling approach in question.

The 3-iodothyronamine (T1AM) and the 3-iodothyroacetic acid (TA1) indicate a novel connection with the histamine system for neuroprotection

The 3-iodothyronamine (T1AM) and 3-iodothryoacetic acid (TA1), are endogenous occurring compounds structurally related with thyroid hormones (THs, the pro-hormone T4 and the active hormone T3) initially proposed as possible mediators of the rapid effects of T3. However, after years from their identification, the physio-pathological meaning of T1AM and TA1 tissue levels remains an unsolved issue while pharmacological evidence indicates both compounds promote in rodents central and peripheral effects with mechanisms which remain mostly elusive. Pharmacodynamics of T1AM includes the recognition of G-coupled receptors, ion channels but also biotransformation into an active metabolite, i.e. the TA1. Furthermore, long term T1AM treatment associates with post-translational modifications of cell proteins. Such array of signaling may represent an added value, rather than a limit, equipping T1AM to play different functions depending on local expression of targets and enzymes involved in its biotransformation. Up to date, no information regarding TA1 mechanistic is available. We here review some of the main findings describing effects of T1AM (and TA1) which suggest these compounds interplay with the histaminergic system. These data reveal T1AM and TA1 are part of a network of signals involved in neuronal plasticity including neuroprotection and suggest T1AM and TA1 as lead compounds for a novel class of atypical psychoactive drugs.

The Sniffing Kidney: Roles for Renal Olfactory Receptors in Health and Disease

Olfactory receptors (ORs) represent the largest gene family in the human genome. Despite their name, functions exist for these receptors outside of the nose. Among the tissues known to take advantage of OR signaling is the kidney. From mouse to man, the list of renal ORs continues to expand, and they have now been linked to a variety of processes involved in the maintenance of renal homeostasis, including the modulation of blood pressure, response to acidemia, and the development of diabetes. In this review, we highlight the recent progress made on the growing appreciation for renal ORs in physiology and pathophysiology.

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.

We understand most sensory systems by comparing the responses of the system against objective external physical measurements. For example, we know that our ability to distinguish small changes in color is greater for some colors than for others, and that we can distinguish sounds more acutely when they are within the range of pitches used for speech. Similar principles presumably apply to the sense of smell, but odorous chemicals are harder to physically quantify than light or sound because they cannot be organized in terms of a straightforward physical variable like wavelength or frequency. That said, the physical properties of interactions between chemicals and cellular receptors (such as those in the olfactory system) are well understood. What we lack is a systematic framework within which these pharmacological principles can be organized to study odor sampling in the way that we have long studied visual and auditory sampling. We here propose and describe such a framework for odor sampling, and show that it successfully replicates some established but unexplained experimental results.

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.

A System-Wide Understanding of the Human Olfactory Percept Chemical Space

The fundamental units of olfactory perception are discrete 3D structures of volatile chemicals that each interact with specific subsets of a very large family of hundreds of odorant receptor proteins, in turn activating complex neural circuitry and posing a challenge to understand. We have applied computational approaches to analyze olfactory perceptual space from the perspective of odorant chemical features. We identify physicochemical features associated with ~150 different perceptual descriptors and develop machine-learning models. Validation of predictions shows a high success rate for test set chemicals within a study, as well as across studies more than 30 years apart in time. Due to the high success rates, we are able to map ~150 percepts onto a chemical space of nearly 0.5 million compounds, predicting numerous percept-structure combinations. The chemical structure-to-percept prediction provides a system-level view of human olfaction and opens the door for comprehensive computational discovery of fragrances and flavors.

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.

Encoding the Odor of Cigarette Smoke

The encoding of odors is believed to begin as a combinatorial code consisting of distinct patterns of responses from odorant receptors (ORs), trace-amine associated receptors (TAARs), or both. To determine how specific response patterns arise requires detecting patterns in vivo and understanding how the components of an odor, which are nearly always mixtures of odorants, give rise to parts of the pattern. Cigarette smoke, a common and clinically relevant odor consisting of >400 odorants, evokes responses from 144 ORs and 3 TAARs in freely behaving male and female mice, the first example of in vivo responses of both ORs and TAARs to an odor. As expected, a simplified artificial mimic of cigarette smoke odor tested at low concentration to identify highly sensitive receptors evokes responses from four ORs, all also responsive to cigarette smoke. Human subjects of either sex identify 1-pentanethiol as the odorant most critical for perception of the artificial mimic; and in mice the OR response patterns to these two odors are significantly similar. Fifty-eight ORs respond to the headspace above 25% 1-pentanethiol, including 9 ORs responsive to cigarette smoke. The response patterns to both cigarette smoke and 1-pentanethiol have strongly responsive ORs spread widely across OR sequence diversity, consistent with most other combinatorial codes previously measured in vivo The encoding of cigarette smoke is accomplished by a broad receptor response pattern, and 1-pentanethiol is responsible for a small subset of the responsive ORs in this combinatorial code.SIGNIFICANCE STATEMENT Complex odors are usually perceived as distinct odor objects. Cigarette smoke is the first complex odor whose in vivo receptor response pattern has been measured. It is also the first pattern shown to include responses from both odorant receptors and trace-amine associated receptors, confirming that the encoding of complex odors can be enriched by signals coming through both families of receptors. Measures of human perception and mouse receptor physiology agree that 1-pentanethiol is a critical component of a simplified odorant mixture designed to mimic cigarette smoke odor. Its receptor response pattern helps to link those of the artificial mimic and real cigarette smoke, consistent with expectations about perceptual similarity arising from shared elements in receptor response patterns.

Encoding the Odor of Cigarette Smoke

The encoding of odors is believed to begin as a combinatorial code consisting of distinct patterns of responses from odorant receptors (ORs), trace-amine associated receptors (TAARs), or both. To determine how specific response patterns arise requires detecting patterns in vivo and understanding how the components of an odor, which are nearly always mixtures of odorants, give rise to parts of the pattern. Cigarette smoke, a common and clinically relevant odor consisting of >400 odorants, evokes responses from 144 ORs and 3 TAARs in freely behaving male and female mice, the first example of in vivo responses of both ORs and TAARs to an odor. As expected, a simplified artificial mimic of cigarette smoke odor tested at low concentration to identify highly sensitive receptors evokes responses from four ORs, all also responsive to cigarette smoke. Human subjects of either sex identify 1-pentanethiol as the odorant most critical for perception of the artificial mimic; and in mice the OR response patterns to these two odors are significantly similar. Fifty-eight ORs respond to the headspace above 25% 1-pentanethiol, including 9 ORs responsive to cigarette smoke. The response patterns to both cigarette smoke and 1-pentanethiol have strongly responsive ORs spread widely across OR sequence diversity, consistent with most other combinatorial codes previously measured in vivo The encoding of cigarette smoke is accomplished by a broad receptor response pattern, and 1-pentanethiol is responsible for a small subset of the responsive ORs in this combinatorial code.SIGNIFICANCE STATEMENT Complex odors are usually perceived as distinct odor objects. Cigarette smoke is the first complex odor whose in vivo receptor response pattern has been measured. It is also the first pattern shown to include responses from both odorant receptors and trace-amine associated receptors, confirming that the encoding of complex odors can be enriched by signals coming through both families of receptors. Measures of human perception and mouse receptor physiology agree that 1-pentanethiol is a critical component of a simplified odorant mixture designed to mimic cigarette smoke odor. Its receptor response pattern helps to link those of the artificial mimic and real cigarette smoke, consistent with expectations about perceptual similarity arising from shared elements in receptor response patterns.

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.