Annotation and Analysis of 3902 Odorant Receptor Protein Sequences from 21 Insect Species Provide Insights into the Evolution of Odorant Receptor Gene Families in Solitary and Social Insects.

The gene family of insect olfactory receptors (ORs) has expanded greatly over the course of evolution. ORs enable insects to detect volatile chemicals and therefore play an important role in social interactions, enemy and prey recognition, and foraging. The sequences of several thousand ORs are known, but their specific function or their ligands have only been identified for very few of them. To advance the functional characterization of ORs, we have assembled, curated, and aligned the sequences of 3902 ORs from 21 insect species, which we provide as an annotated online resource. Using functionally characterized proteins from the fly Drosophila melanogaster, the mosquito Anopheles gambiae and the ant Harpegnathos saltator, we identified amino acid positions that best predict response to ligands. We examined the conservation of these predicted relevant residues in all OR subfamilies; the results showed that the subfamilies that expanded strongly in social insects had a high degree of conservation in their binding sites. This suggests that the ORs of social insect families are typically finely tuned and exhibit sensitivity to very similar odorants. Our novel approach provides a powerful tool to exploit functional information from a limited number of genes to study the functional evolution of large gene families.

First-order visual interneurons distribute distinct contrast and luminance information across ON and OFF pathways to achieve stable behavior.

The accurate processing of contrast is the basis for all visually guided behaviors. Visual scenes with rapidly changing illumination challenge contrast computation because photoreceptor adaptation is not fast enough to compensate for such changes. Yet, human perception of contrast is stable even when the visual environment is quickly changing, suggesting rapid post receptor luminance gain control. Similarly, in the fruit fly Drosophila, such gain control leads to luminance invariant behavior for moving OFF stimuli. Here, we show that behavioral responses to moving ON stimuli also utilize a luminance gain, and that ON-motion guided behavior depends on inputs from three first-order interneurons L1, L2, and L3. Each of these neurons encodes contrast and luminance differently and distributes information asymmetrically across both ON and OFF contrast-selective pathways. Behavioral responses to both ON and OFF stimuli rely on a luminance-based correction provided by L1 and L3, wherein L1 supports contrast computation linearly, and L3 non-linearly amplifies dim stimuli. Therefore, L1, L2, and L3 are not specific inputs to ON and OFF pathways but the lamina serves as a separate processing layer that distributes distinct luminance and contrast information across ON and OFF pathways to support behavior in varying conditions.

Stimulus Driven Functional Transformations in the Early Olfactory System.

Olfactory stimuli are encountered across a wide range of odor concentrations in natural environments. Defining the neural computations that support concentration invariant odor perception, odor discrimination, and odor-background segmentation across a wide range of stimulus intensities remains an open question in the field. In principle, adaptation could allow the olfactory system to adjust sensory representations to the current stimulus conditions, a well-known process in other sensory systems. However, surprisingly little is known about how adaptation changes olfactory representations and affects perception. Here we review the current understanding of how adaptation impacts processing in the first two stages of the vertebrate olfactory system, olfactory receptor neurons (ORNs), and mitral/tufted cells.

Adaptive temporal processing of odor stimuli.

The olfactory system translates chemical signals into neuronal signals that inform behavioral decisions of the animal. Odors are cues for source identity, but if monitored long enough, they can also be used to localize the source. Odor representations should therefore be robust to changing conditions and flexible in order to drive an appropriate behavior. In this review, we aim at discussing the main computations that allow robust and flexible encoding of odor information in the olfactory neural pathway.

Controlling and measuring dynamic odorant stimuli in the laboratory.

Animals experience complex odorant stimuli that vary widely in composition, intensity and temporal properties. However, stimuli used to study olfaction in the laboratory are much simpler. This mismatch arises from the challenges in measuring and controlling them precisely and accurately. Even simple pulses can have diverse kinetics that depend on their molecular identity. Here, we introduce a model that describes how stimulus kinetics depend on the molecular identity of the odorant and the geometry of the delivery system. We describe methods to deliver dynamic odorant stimuli of several types, including broadly distributed stimuli that reproduce some of the statistics of naturalistic plumes, in a reproducible and precise manner. Finally, we introduce a method to calibrate a photo-ionization detector to any odorant it can detect, using no additional components. Our approaches are affordable and flexible and can be used to advance our understanding of how olfactory neurons encode real-world odor signals.

Slow presynaptic mechanisms that mediate adaptation in the olfactory pathway of Drosophila.

The olfactory system encodes odor stimuli as combinatorial activity of populations of neurons whose response depends on stimulus history. How and on which timescales previous stimuli affect these combinatorial representations remains unclear. We use in vivo optical imaging in Drosophila to analyze sensory adaptation at the first synaptic step along the olfactory pathway. We show that calcium signals in the axon terminals of olfactory receptor neurons (ORNs) do not follow the same adaptive properties as the firing activity measured at the antenna. While ORNs calcium responses are sustained on long timescales, calcium signals in the postsynaptic projection neurons (PNs) adapt within tens of seconds. We propose that this slow component of the postsynaptic response is mediated by a slow presynaptic depression of vesicle release and enables the combinatorial population activity of PNs to adjust to the mean and variance of fluctuating odor stimuli.

SIFamide Translates Hunger Signals into Appetitive and Feeding Behavior in Drosophila.

Animal behavior is, on the one hand, controlled by neuronal circuits that integrate external sensory stimuli and induce appropriate motor responses. On the other hand, stimulus-evoked or internally generated behavior can be influenced by motivational conditions, e.g., the metabolic state. Motivational states are determined by physiological parameters whose homeostatic imbalances are signaled to and processed within the brain, often mediated by modulatory peptides. Here, we investigate the regulation of appetitive and feeding behavior in the fruit fly, Drosophila melanogaster. We report that four neurons in the fly brain that release SIFamide are integral elements of a complex neuropeptide network that regulates feeding. We show that SIFamidergic cells integrate feeding stimulating (orexigenic) and feeding suppressant (anorexigenic) signals to appropriately sensitize sensory circuits, promote appetitive behavior, and enhance food intake. Our study advances the cellular dissection of evolutionarily conserved signaling pathways that convert peripheral metabolic signals into feeding-related behavior.

Functional diversity among sensory receptors in a Drosophila olfactory circuit.

The ability of an animal to detect, discriminate, and respond to odors depends on the function of its olfactory receptor neurons (ORNs), which in turn depends ultimately on odorant receptors. To understand the diverse mechanisms used by an animal in olfactory coding and computation, it is essential to understand the functional diversity of its odor receptors. The larval olfactory system of Drosophila melanogaster contains 21 ORNs and a comparable number of odorant receptors whose properties have been examined in only a limited way. We systematically screened them with a panel of ∼500 odorants, yielding >10,000 receptor-odorant combinations. We identify for each of 19 receptors an odorant that excites it strongly. The responses elicited by each of these odorants are analyzed in detail. The odorants elicited little cross-activation of other receptors at the test concentration; thus, low concentrations of many of these odorants in nature may be signaled by a single ORN. The receptors differed dramatically in sensitivity to their cognate odorants. The responses showed diverse temporal dynamics, with some odorants eliciting supersustained responses. An intriguing question in the field concerns the roles of different ORNs and receptors in driving behavior. We found that the cognate odorants elicited behavioral responses that varied across a broad range. Some odorants elicited strong physiological responses but weak behavioral responses or weak physiological responses but strong behavioral responses.

Intensity invariant dynamics and odor-specific latencies in olfactory receptor neuron response.

Odors elicit spatiotemporal patterns of activity in the brain. Spatial patterns arise from the specificity of the interaction between odorants and odorant receptors expressed in different olfactory receptor neurons (ORNs), but the origin of temporal patterns of activity and their role in odor coding remain unclear. We investigate how physiological aspects of ORN response and physical aspects of odor stimuli give rise to diverse responses in Drosophila ORNs. We show that odor stimuli have intrinsic dynamics that depend on odor type and strongly affect ORN response. Using linear-nonlinear modeling to remove the contribution of the stimulus dynamics from the ORN dynamics, we study the physiological properties of the response to different odorants and concentrations. For several odorants and receptor types, the ORN response dynamics normalized by the peak response are independent of stimulus intensity for a large portion of the dynamic range of the neuron. Adaptation to a background odor changes the gain and dynamic range of the response but does not affect normalized response dynamics. Stimulating ORNs with various odorants reveals significant odor-dependent delays in the ORN response functions. However, these differences can be dominated by differences in stimulus dynamics. In one case the response of one ORN to two odorants is predicted solely from measurements of the odor signals. Within a large portion of their dynamic range, ORNs can capture information about stimulus dynamics independently from intensity while introducing odor-dependent delays. How insects might use odor-specific stimulus dynamics and ORN dynamics in discrimination and navigation tasks remains an open question.

Temporal coding of odor mixtures in an olfactory receptor neuron.

Most natural odors are mixtures and often elicit percepts distinct from those elicited by their constituents. This emergence of a unique odor quality has long been attributed to central processing. Here we show that sophisticated integration of olfactory information begins in olfactory receptor neurons (ORNs) in Drosophila. Odor mixtures are encoded in the temporal dynamics as well as in the magnitudes of ORN responses. ORNs can respond to an inhibitory odorant with different durations depending on the level of background excitation. ORNs respond to mixtures with distinctive temporal dynamics that reflect the physicochemical properties of the constituent odorants. The insect repellent DEET (N,N-diethyl-m-toluamide), which attenuates odor responses of multiple ORNs, differs from an ORN-specific inhibitor in its effects on temporal dynamics. Our analysis reveals a means by which integration of information from odor mixtures begins in ORNs and provides insight into the contribution of inhibitory stimuli to sensory coding.

Identifying essential genes in Escherichia coli from a metabolic optimization principle.

Understanding the organization of reaction fluxes in cellular metabolism from the stoichiometry and the topology of the underlying biochemical network is a central issue in systems biology. In this task, it is important to devise reasonable approximation schemes that rely on the stoichiometric data only, because full-scale kinetic approaches are computationally affordable only for small networks (e.g., red blood cells, approximately 50 reactions). Methods commonly used are based on finding the stationary flux configurations that satisfy mass-balance conditions for metabolites, often coupling them to local optimization rules (e.g., maximization of biomass production) to reduce the size of the solution space to a single point. Such methods have been widely applied and have proven able to reproduce experimental findings for relatively simple organisms in specific conditions. Here, we define and study a constraint-based model of cellular metabolism where neither mass balance nor flux stationarity are postulated and where the relevant flux configurations optimize the global growth of the system. In the case of Escherichia coli, steady flux states are recovered as solutions, although mass-balance conditions are violated for some metabolites, implying a nonzero net production of the latter. Such solutions furthermore turn out to provide the correct statistics of fluxes for the bacterium E. coli in different environments and compare well with the available experimental evidence on individual fluxes. Conserved metabolic pools play a key role in determining growth rate and flux variability. Finally, we are able to connect phenomenological gene essentiality with "frozen" fluxes (i.e., fluxes with smaller allowed variability) in E. coli metabolism.

Asymptotic states and topological structure of an activation-deactivation chemical network.

The influence of the topology on the asymptotic states of a network of interacting chemical species has been studied by simulating its time evolution. Random and scale-free networks have been designed to support relevant features of activation-deactivation reactions networks (mapping signal transduction networks) and the system of ordinary differential equations associated to the dynamics has been numerically solved. We analysed stationary states of the dynamics as a function of the network's connectivity and of the distribution of the chemical species on the network; we found important differences between the two topologies in the regime of low connectivity. In particular, only for low connected scale-free networks it is possible to find zero activity patterns as stationary states of the dynamics which work as signal off-states. Asymptotic features of random and scale-free networks become similar as the connectivity increases.