Dataset Information

Patterns of transcriptional parallelism and variation in the developing olfactory system of Drosophila species.

ABSTRACT: Organisms have evolved strikingly parallel phenotypes in response to similar selection pressures suggesting that there may be shared constraints limiting the possible evolutionary trajectories. For example, the behavioral adaptation of specialist Drosophila species to specific host plants can exhibit parallel changes in their adult olfactory neuroanatomy. We investigated the genetic basis of these parallel changes by comparing gene expression during the development of the olfactory system of two specialist Drosophila species to that of four other generalist species. Our results suggest that the parallelism observed in the adult olfactory neuroanatomy of ecological specialists extends more broadly to their developmental antennal expression profiles, and to the transcription factor combinations specifying olfactory receptor neuron (ORN) fates. Additionally, comparing general patterns of variation for the antennal transcriptional profiles in the adult and developing olfactory system of the six species suggest the possibility that specific, non-random components of the developmental programs underlying the Drosophila olfactory system harbor a disproportionate amount of interspecies variation. Further examination of these developmental components may be able to inform a deeper understanding of how traits evolve.

SUBMITTER: Pan JW

PROVIDER: S-EPMC5562767 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Patterns of transcriptional parallelism and variation in the developing olfactory system of Drosophila species.

Pan Jia Wern JW Li Qingyun Q Barish Scott S Okuwa Sumie S Zhao Songhui S Soeder Charles C Kanke Matthew M Jones Corbin D CD Volkan Pelin Cayirlioglu PC

Scientific reports 20170818 1

Organisms have evolved strikingly parallel phenotypes in response to similar selection pressures suggesting that there may be shared constraints limiting the possible evolutionary trajectories. For example, the behavioral adaptation of specialist Drosophila species to specific host plants can exhibit parallel changes in their adult olfactory neuroanatomy. We investigated the genetic basis of these parallel changes by comparing gene expression during the development of the olfactory system of two ...[more]

PMID: 28821769

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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

Patterns of transcriptional parallelism and variation in the developing olfactory system of Drosophila species.

Publications

Patterns of transcriptional parallelism and variation in the developing olfactory system of Drosophila species.

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