Dataset Information

A 3'UTR pumilio-binding element directs translational activation in olfactory sensory neurons.

ABSTRACT: Prolonged stimulation leads to specific and stable changes in an animal's behavior. In interneurons, this plasticity requires spatial and temporal control of neuronal protein synthesis. Whether such translational control occurs in sensory neurons is not known. Adaptation of the AWC olfactory sensory neurons of C. elegans requires the cGMP-dependent protein kinase EGL-4. Here, we show that the RNA-binding PUF protein FBF-1 is required in the adult AWC for adaptation. In the odor-adapted animal, it increases translation via binding to the egl-4 3' UTR. Further, the PUF protein may localize translation near the sensory cilia and cell body. Although the RNA-binding PUF proteins have been shown to promote plasticity in development by temporally and spatially repressing translation, this work reveals that in the adult nervous system, they can work in a different way to promote experience-dependent plasticity by activating translation in response to environmental stimulation.

SUBMITTER: Kaye JA

PROVIDER: S-EPMC4274156 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature

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A 3'UTR pumilio-binding element directs translational activation in olfactory sensory neurons.

Kaye Julia A JA Rose Natalie C NC Goldsworthy Brett B Goga Andrei A L'Etoile Noelle D ND

Neuron 20090101 1

Prolonged stimulation leads to specific and stable changes in an animal's behavior. In interneurons, this plasticity requires spatial and temporal control of neuronal protein synthesis. Whether such translational control occurs in sensory neurons is not known. Adaptation of the AWC olfactory sensory neurons of C. elegans requires the cGMP-dependent protein kinase EGL-4. Here, we show that the RNA-binding PUF protein FBF-1 is required in the adult AWC for adaptation. In the odor-adapted animal, i ...[more]

PMID: 19146813

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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

A 3'UTR pumilio-binding element directs translational activation in olfactory sensory neurons.

Publications

A 3'UTR pumilio-binding element directs translational activation in olfactory sensory neurons.

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