Adaptive traits in the drosophila mushroom body
Thu-S4-001
Presented by: Sophie J. C. Caron
How the brain adapts in response to changes in the environment remains largely unknown. Thus far, adaptive traits have been primarily identified in sensory systems, suggesting that sensory neurons might be more malleable to evolutionary pressures than neurons embedded in higher brain centers. In Drosophila, comparative studies of closely related species that live in drastically different chemosensory ecology revealed that olfactory sensory neurons can rapidly evolve new detection capabilities. For instance, the obligate noni specialist Drosophila sechellia, a close relative of the generalists Drosophila melanogaster and Drosophila simulans, is equipped with olfactory sensory neurons finely tuned to noni volatiles. In this presentation, I will summarize our recent findings showing that adaptive traits can be found downstream of the olfactory sensory neurons. We used a neuronal tracing technique to determine how the projection neurons of the olfactory system connect to the Kenyon cell of the mushroom body. We mapped over 2000 projection neuron—Kenyon cell connections in Drosophila melanogaster, Drosophila sechellia and Drosophila simulans. Statistical analyses of these connections revealed global architectural features that are species-specific. Specifically, we found that the projection neurons activated by noni volatiles connect more frequently to Kenyon cells in Drosophila sechellia than they do in the generalist species. We also found that this increased connectivity results from a larger number of projection neurons as well as a larger number of pre-synaptic sites. Finally, we have evidence suggesting that increased connectivity in the mushroom body leads to differences in learning abilities. Altogether, this study shows that higher brain centers are just as malleable to evolutionary pressures as sensory systems, suggesting that the brain might adapt to novel sensory environments through cellular changes distributed along neuronal circuits.