Identification of membrane-based mechanisms of learning and memory using functional imaging in Drosophila
Jan Pielage - Timo Mühlhaus- Christina Surulescu
The ability of animals to selectively adjust behavior in response to external stimuli is critical for the survival and success of all higher species. This process of learning and memory depends on precise adjustments of chemical signal transduction at synapses, the membrane-based interaction sites between neurons. In response to selective neuronal activations synaptic transmission can either be potentiated or depressed over prolonged periods of time. These functional plasticity phenomena are commonly described as long-term potentiation (LTP) and long-term depression (LTD) (Bourne and Harris, 2008). At a mechanistic level these changes are induced presynaptically through alterations in presynaptic release properties or postsynaptically through modulations in neurotransmitter receptor function or abundance. These functional changes are then consolidated via mechanisms of structural synaptic plasticity resulting in either the formation of new synapses or the disassembly of previously functional synaptic connections (Bourne and Harris, 2008; Caroni et al., 2012). Despite recent progress, the precise molecular mechanisms mediating these trans-synaptic changes to induce functional and structural synaptic plasticity remain largely unknown. To identify the molecular and cellular code controlling acquisition, storage and retrieval of memory we use olfactory appetitive conditioning of adult Drosophila as a model system. In this assay, starved flies learn to distinguish between two neutral odors by a single, one time pairing of one of the odors with sucrose. This training results in robust learning with memory persisting for periods up to 48h (Busto et al., 2010). Appetitive learning and memory depends on mushroom body neurons that receive direct olfactory input from the olfactory bulb (Siegenthaler et al., 2015). Selective activation paired with the conditioned stimulus (sucrose) then results in alterations of neuronal physiology and/or connectivity to mushroom body output neurons (Owald et al., 2015; Plaçais et al., 2013). As a consequence, a distinct elevated calcium trace emerges in mushroom neurons that indicates changes in network behavior and is part of the memory trace (Davis, 2011; Owald et al., 2015; Plaçais et al., 2013). Here, we aim to unravel the cellular and molecular basis of this process by combining functional Ca2+-imaging in combination with cell-specific genetic screens. We will focus our genetic approaches on integral membrane proteins (ion channels, transmembrane signaling and adhesion molecules) as these have the potential to directly induce and maintain the short-term and long-term changes underlying learning and memory. Candidate molecules include genes that we previously identified essential for synapse stability (Enneking et al., 2012, Frank et al, 2009). To monitor neuronal and synaptic activity we use the transgenic Ca2+-reporter GCaMP6 (Chen et al., 2013). We will target expression to either populations or individual mushroom body and mushroom body output neurons to monitor and quantify Ca2+-traces (and thus neuronal activity) at high temporal and spatial resolution. The recordings will be performed in vivo using an open head preparation at a 2-Photon setup. Comparison of Ca2+-traces in wild type and mutant backgrounds will allow identification of the relevant neuronal population encoding the memory engram and help to uncover novel molecular pathways essential for learning and memory. These experiments critically depend on high-resolution temporal and spatial Ca2+ signal detection, a feature that can only be achieved in a collaborative and interactive approach with a bioinformatics group. Focus will be on the establishment and refinement of methods for identification, de-noising and visualization of Ca2+-traces. Our goal will be to set up a highly efficient and user friendly bioinformatics pipeline that ideally is also applicable to in vivo Ca2+-imaging analyses of different model systems. Together, our approaches will greatly improve our knowledge and analytical description of functional learning and memory paradigms in Drosophila and beyond.
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