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Cook, O. M.

Publications and source records attributed to Cook, O. M..

2 recordsLinked to original sources

Individual olfactory channels shape distinct parameters of sleep architecture

AbstractAcross the animal kingdom, olfactory dysfunction and anosmia have been associated with disruptions in sleep. In the fruit fly Drosophila melanogaster, various studies have demonstrated that broadly inhibiting olfactory receptor neurons (ORNs) similarly disrupts sleep/wake cycles, suggesting that baseline ORN signaling is an integral component of olfactory modulation of sleep. However, due to the diversity of ORNs and combinatorial nature of olfactory processing, many of the cellular and molecular mechanisms by which ORNs modulate sleep remain unclear. In this study, we addressed this gap of knowledge by characterizing the contributions of different ensembles of ORNs, individual ORN types, and a known modulator of ORNs on baseline sleep architecture. We find that the activity of distinct ORN types are important for day and nighttime sleep and heterogeneously shape parameters of sleep architecture. Importantly, the effects of ORN signaling on sleep are adjusted across mating status, suggesting that distinct ORN types are recruited within the context of sleep depending on the demands of the animal. Furthermore, the effects of ORN signaling on sleep are in part shaped by heterogeneous serotonin (5-HT) receptor expression. Together, this work identifies cellular and molecular pathways bridging olfaction and sleep, and helps establish a circuit model that can be used to further characterize the behavioral consequences of sensory dysfunction.

neuroscience↗

Connectivity of serotonin neurons reveals a constrained inhibitory subnetwork within the olfactory system.

Inhibitory local interneurons (LNs) play an essential role in sensory processing by refining stimulus representations via a diverse collection of mechanisms. The morphological and physiological traits of individual LN types, as well as their connectivity within sensory networks, enable each LN type to support different computations such as lateral inhibition or gain control and are therefore ideal targets for modulatory neurons to have widespread impacts on network activity. In this study, we combined detailed connectivity analyses, serotonin receptor expression, neurophysiology, and computational modeling to demonstrate the functional impact of serotonin on a constrained LN network in the olfactory system of Drosophila. This subnetwork is composed of three LN types and we describe each of their distinctive morphology, connectivity, biophysical properties and odor response properties. We demonstrate that each LN type expresses different combinations of serotonin receptors and that serotonin differentially impacts the excitability of each LN type. Finally, by applying these serotonin induced changes in excitability to a computational model that simulates the impact of inhibition exerted by each LN-type, we predict a role for serotonin in adjusting the dynamic range of antennal lobe output neurons and in noise reduction in odor representations. Thus, a single modulatory system can differentially impact LN types that subserve distinct roles within the olfactory system. Significance StatementInhibitory interneurons refine information processing within sensory networks by enforcing distinct local computations. They are therefore ideal targets for modulatory neurons to efficiently alter sensory processing by up- or downregulating the computations each interneuron class subserves. We identify an interconnected network of three interneuron types in the olfactory system of Drosophila that receive a large amount of serotonergic synaptic input. Each interneuron type differs in their biophysical and response properties and serotonin differentially impacts their excitability. Finally, using a computational model, we predict that the combined effects of serotonin on these inhibitory neurons enables noise reduction in the olfactory system. Thus, modulation of individual cell types collectively adjusts distinct network computations to enable flexible sensory coding.

neuroscience↗