Mechanosensitive neurons on the internal reproductive tract contribute to egg-laying-induced acetic acid attraction in Drosophila.

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Selecting a suitable site to deposit their eggs is an important reproductive need of Drosophila females. Although their choosiness toward egg-laying sites is well documented, the specific neural mechanism that activates females' search for attractive egg-laying sites is not known. Here, we show that distention and contraction of females' internal reproductive tract triggered by egg delivery through the tract plays a critical role in activating such search. We found that females start to exhibit acetic acid (AA) attraction prior to depositing each egg but no attraction when they are not laying eggs. Artificially distending the reproductive tract triggers AA attraction in non-egg-laying females, whereas silencing the mechanosensitive neurons we identified that can sense the contractile status of the tract eliminates such attraction. Our work uncovers the circuit basis of an important reproductive need of Drosophila females and provides a simple model for dissecting the neural mechanism that underlies a reproductive need-induced behavioral modification.





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Gou, B, Y Liu, A Guntur, U Stern and C Yang (2014). Mechanosensitive neurons on the internal reproductive tract contribute to egg-laying-induced acetic acid attraction in Drosophila. Cell Rep, 9(2). pp. 522–530. 10.1016/j.celrep.2014.09.033 Retrieved from

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Rebecca Chung-Hui Yang

Associate Professor of Neurobiology

Our lab is interested in understanding the neural basis of simple decision-making processes.  We use Drosophila egg-laying site selection as our model system.  To understand how the Drosophila brain assesses and ranks the values of egg-laying options, we use a combined approach that includes high-throughput optogenetics-based behavioral screen, automated (machine vision) behavioral tracking of single animals, molecular genetic tools to identify critical circuit components, and calcium imaging and anatomical tracing techniques to determine the physical and functional connectivity of identified circuit components.  

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