Dataset Information

Olfactory Neurons and Brain Centers Directing Oviposition Decisions in Drosophila.

ABSTRACT: The sense of smell influences many behaviors, yet how odors are represented in the brain remains unclear. A major challenge to studying olfaction is the lack of methods allowing activation of specific types of olfactory neurons in an ethologically relevant setting. To address this, we developed a genetic method in Drosophila called olfactogenetics in which a narrowly tuned odorant receptor, Or56a, is ectopically expressed in different olfactory neuron types. Stimulation with geosmin (the only known Or56a ligand) in an Or56a mutant background leads to specific activation of only target olfactory neuron types. We used this approach to identify olfactory sensory neurons (OSNs) that directly guide oviposition decisions. We identify 5 OSN-types (Or71a, Or47b, Or49a, Or67b, and Or7a) that, when activated alone, suppress oviposition. Projection neurons partnering with these OSNs share a region of innervation in the lateral horn, suggesting that oviposition site selection might be encoded in this brain region.

SUBMITTER: Chin SG

PROVIDER: S-EPMC6290906 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Publications

Olfactory Neurons and Brain Centers Directing Oviposition Decisions in Drosophila.

Chin Sonia G SG Maguire Sarah E SE Huoviala Paavo P Jefferis Gregory S X E GSXE Potter Christopher J CJ

Cell reports 20180801 6

The sense of smell influences many behaviors, yet how odors are represented in the brain remains unclear. A major challenge to studying olfaction is the lack of methods allowing activation of specific types of olfactory neurons in an ethologically relevant setting. To address this, we developed a genetic method in Drosophila called olfactogenetics in which a narrowly tuned odorant receptor, Or56a, is ectopically expressed in different olfactory neuron types. Stimulation with geosmin (the only kn ...[more]

PMID: 30089274

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Project description:The lack of a deeper understanding of how olfactory sensory neurons (OSNs) encode odors has hindered the progress in understanding the olfactory signal processing in higher brain centers. Here we employ methods of system identification to investigate the encoding of time-varying odor stimuli and their representation for further processing in the spike domain by Drosophila OSNs. In order to apply system identification techniques, we built a novel low-turbulence odor delivery system that allowed us to deliver airborne stimuli in a precise and reproducible fashion. The system provides a 1% tolerance in stimulus reproducibility and an exact control of odor concentration and concentration gradient on a millisecond time scale. Using this novel setup, we recorded and analyzed the in-vivo response of OSNs to a wide range of time-varying odor waveforms. We report for the first time that across trials the response of OR59b OSNs is very precise and reproducible. Further, we empirically show that the response of an OSN depends not only on the concentration, but also on the rate of change of the odor concentration. Moreover, we demonstrate that a two-dimensional (2D) Encoding Manifold in a concentration-concentration gradient space provides a quantitative description of the neuron's response. We then use the white noise system identification methodology to construct one-dimensional (1D) and two-dimensional (2D) Linear-Nonlinear-Poisson (LNP) cascade models of the sensory neuron for a fixed mean odor concentration and fixed contrast. We show that in terms of predicting the intensity rate of the spike train, the 2D LNP model performs on par with the 1D LNP model, with a root mean-square error (RMSE) increase of about 5 to 10%. Surprisingly, we find that for a fixed contrast of the white noise odor waveforms, the nonlinear block of each of the two models changes with the mean input concentration. The shape of the nonlinearities of both the 1D and the 2D LNP model appears to be, for a fixed mean of the odor waveform, independent of the stimulus contrast. This suggests that white noise system identification of Or59b OSNs only depends on the first moment of the odor concentration. Finally, by comparing the 2D Encoding Manifold and the 2D LNP model, we demonstrate that the OSN identification results depend on the particular type of the employed test odor waveforms. This suggests an adaptive neural encoding model for Or59b OSNs that changes its nonlinearity in response to the odor concentration waveforms.

Dataset Information

Olfactory Neurons and Brain Centers Directing Oviposition Decisions in Drosophila.

Publications

Olfactory Neurons and Brain Centers Directing Oviposition Decisions in Drosophila.

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