Odorant Receptor Inhibition Is Fundamental to Odor Encoding

Pfister, Patrick; Smith, Benjamin; Evans, Barry J.; Brann, Jessica H.; Trimmer, Casey; Sheikh, Mushhood; Arroyave, Randy; Reddy, Gautam; Jeong, Hwan Jeong; Raps, Daniel A.; Peterlin, Zita; Vergassola, Massimo; Rogers, Matthew E.

doi:10.1016/j.cub.2020.04.086

Cited by 125 publications

(51 citation statements)

References 78 publications

(101 reference statements)

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“…As discussed in [28], additional odor-receptor interactions such as synergy, suppression, antagonism, inhibition, etc., can readily be included in the receptor response model. The prevalence of such interaction are only beginning to be investigated on large scales [44]. If the statistics of such odor-receptor interactions are known, they can be included in our model (see below).…”

Section: Decoder Of Odor Compositionmentioning

confidence: 99%

What the odor is not: Estimation by elimination

Singh

Tchernookov

Balasubramanian

2019

Preprint

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The olfactory system uses the responses of a small number of broadly sensitive receptors to combinatorially encode a vast number of odors. Here, we propose a method for decoding such a distributed representation. Our main idea is that a receptor that does not respond to an odor carries more information than a receptor that does, because a typical receptor binds to many odorants. So a response below threshold signals the absence of all such odorants. As a result, it is easier to identify what the odor is not, rather than what the odor is. We demonstrate that, for biologically realistic numbers of receptors, response functions, and odor mixture complexity, this remarkably simple method of elimination turns an underdetermined decoding problem into an overdetermined one, allowing accurate determination of the odorants in a mixture and their concentrations. We give a simple neural network realization of our algorithm which resembles the known circuit architecture of the piriform cortex.Olfaction | Receptors | Odor DecodingThe olfactory system enables animals to sense, perceive, and respond to mixtures of volatile molecules that carry messages about the world. There are many monomolecular odorants, perhaps 10 4 or more (1-3), far more than the number of receptor types available to animals (∼ 50 in fly, ∼ 300 in human, ∼ 1000 in rat, mouse and dog (4-7)). The problem of representing such a high-dimensional chemical space in such a low-dimensional response space may be solved by the presence of many receptors that bind to numerous odorants (8-14), leading to a distributed, combinatorial representation of odors (see, e.g., (12,(15)(16)(17)(18)(19)(20)(21)(22)(23)).We focus on the inverse problem: the estimation of odor composition from the response of olfactory receptors. We use a realistic competitive binding model of odor encoding by receptors (24)(25)(26)(27), and propose a scheme to decode odor composition from such responses. The scheme works over a large range of biologically relevant parameters, does not require any special constraints on receptor-odorant interactions, and works for systems with few receptors, suggesting why the relatively small olfactory receptor repertoires of most organisms are sufficient for detecting complex natural odors.Our main idea is that a receptor that does not respond to an odor carries a lot more information about the odor than a receptor that does respond to it. This is because a receptor that does not respond to an odor signals that none of the odorants (individual chemicals) that could bind to this receptor are present in the odor. With just a few such non-responding receptors, most of the odorants that are not present can be identified and eliminated. Thus, it is easier to identify what the mixture is not, rather than what the mixture is. For a large range of biologically relevant parameters, this elimination turns the estimation of odor concentration from an underdetermined problem to an overdetermined one. Thus, the concentration of the rest of the odorants can be estimated from...

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Section: Decoder Of Odor Compositionmentioning

confidence: 99%

What the odor is not: Estimation by elimination

Singh

Tchernookov

Balasubramanian

2019

Preprint

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“…Contemporary studies revisiting or rediscovering this phenomenon have made broad measurements of odor and mixture effects upon OSN activation levels in the intact epithelium that reflect these pharmacological principles, illustrating that odorant receptor antagonism is a common and widespread phenomenon [37-39, 41, 42]. In particular, it has been observed that this antagonism has benefits for representing mixtures containing larger numbers of different odorants without saturation [39], as illustrated here in Figure 4. The improved "encoding capacity" that these authors suggest arises from antagonism, however, would not arise from sparseness -which is not directly relevant here -but for the reasons outlined in Effects of different receptor efficacy Gaussian functions and illustrated in Figure 4.…”

Section: The Diversity Of Pharmacological Interactionsmentioning

confidence: 93%

“…Notably, we found that the highest absolute discrimination sensitivities were attained with narrower efficacy Gaussians -that is, ligand-receptor interactions with relatively high likelihoods of competitive antagonism ( Figure 4C, left panel). Primary olfactory receptors, like any receptor, certainly have weak and antagonist ligands [32,33,[37][38][39][41][42][43][44], though the probabilities of each of these different ligand-receptor interactions for any given receptor complement clearly depend on the statistics of the odor environment in which it is deployed. In the present simulations, the maximum peak discrimination sensitivity was observed when the efficacy standard deviations were roughly 0.10 q-units ( Figure 4C).…”

Section:  mentioning

confidence: 99%

“…Odors may comprise one or many molecular types in characteristic concentration ratios; additionally, each component molecule may present multiple ligands to the complement of receptors expressed by a given animal. Individual odor ligands exhibit receptorspecific 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 [31][32][33][34][35][36][37][38][39][40][41][42][43][44]. 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.…”

Section: Introductionmentioning

confidence: 99%

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A physicochemical model of odor sampling

Gronowitz

Liu

Cleland

2020

Preprint

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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 odor scene 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 disrupted. Author SummaryWe 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 in 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 framewo...

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“…Nevertheless, the precise pathway(s) involved in the homogeneous perception of odor mixtures remains poorly understood [6,10]. To date, they are mainly target approaches, which concern the interactions of odorants at the OR and olfactory sensory neuron levels [11][12][13][14][15][16][17][18][19][20][21][22], evoking odorants that are involved as agonists/antagonists of their biological targets.By contrast, we focused on a ligand approach, which is complementary to the target approach. In the context of aroma blending, our approach consisted of considering a set of odorants, whose selection was based on aroma blending and whose perceptual and configural characteristics have been previously carefully investigated in studies performed with animals [23,24] and humans [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Odorant Receptor Inhibition Is Fundamental to Odor Encoding

Cited by 125 publications

References 78 publications

What the odor is not: Estimation by elimination

What the odor is not: Estimation by elimination

A physicochemical model of odor sampling

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

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