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Olfactory Sensory Neurons Control Dendritic Complexity of Mitral Cells via Notch Signaling.

ABSTRACT: Mitral cells (MCs) of the mammalian olfactory bulb have a single primary dendrite extending into a single glomerulus, where they receive odor information from olfactory sensory neurons (OSNs). Molecular mechanisms for controlling dendritic arbors of MCs, which dynamically change during development, are largely unknown. Here we found that MCs displayed more complex dendritic morphologies in mouse mutants of Maml1, a crucial gene in Notch signaling. Similar phenotypes were observed by conditionally misexpressing a dominant negative form of MAML1 (dnMAML1) in MCs after their migration. Conversely, conditional misexpression of a constitutively active form of Notch reduced their dendritic complexity. Furthermore, the intracellular domain of Notch1 (NICD1) was localized to nuclei of MCs. These findings suggest that Notch signaling at embryonic stages is involved in the dendritic complexity of MCs. After the embryonic misexpression of dnMAML1, many MCs aberrantly extended dendrites to more than one glomerulus at postnatal stages, suggesting that Notch signaling is essential for proper formation of olfactory circuits. Moreover, dendrites in cultured MCs were shortened by Jag1-expressing cells. Finally, blocking the activity of Notch ligands in OSNs led to an increase in dendritic complexity as well as a decrease in NICD1 signals in MCs. These results demonstrate that the dendritic complexity of MCs is controlled by their presynaptic partners, OSNs.

SUBMITTER: Muroyama Y

PROVIDER: S-EPMC5189955 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

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Olfactory Sensory Neurons Control Dendritic Complexity of Mitral Cells via Notch Signaling.

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