@article {69045, title = {Neural-circuit basis of song preference learning in fruit flies}, journal = {iScience}, volume = {27}, year = {2024}, month = {2024 Jul 19}, abstract = {

As observed in human language learning and song learning in birds, the fruit fly Drosophila melanogaster changes its auditory behaviors according to prior sound experiences. This phenomenon, known as song preference learning in flies, requires GABAergic input to pC1 neurons in the brain, with these neurons playing a key role in mating behavior. The neural circuit basis of this GABAergic input, however, is not known. Here, we find that GABAergic neurons expressing the sex-determination gene doublesex are necessary for song preference learning. In the brain, only four doublesex-expressing GABAergic neurons exist per hemibrain, identified as pCd-2 neurons. pCd-2 neurons directly, and in many cases mutually, connect with pC1 neurons, suggesting the existence of reciprocal circuits between them. Moreover, GABAergic and dopaminergic inputs to doublesex-expressing GABAergic neurons are necessary for song preference learning. Together, this study provides a neural circuit model that underlies experience-dependent auditory plasticity at a single-cell resolution.

}, doi = {10.1016/j.isci.2024.110266}, url = {https://doi.org/10.1016/j.isci.2024.110266}, author = {Keisuke Imoto and Yuki Ishikawa and Yoshinori Aso and Jan Funke and Ryoya Tanaka and Azusa Kamikouchi} } @article {69329, title = {A split-GAL4 driver line resource for Drosophila CNS cell types}, journal = {bioRxiv}, year = {2024}, month = {2024 Jan 10}, abstract = {

Techniques that enable precise manipulations of subsets of neurons in the fly central nervous system have greatly facilitated our understanding of the neural basis of behavior. Split-GAL4 driver lines allow specific targeting of cell types in Drosophila melanogaster and other species. We describe here a collection of 3060 lines targeting a range of cell types in the adult Drosophila central nervous system and 1373 lines characterized in third-instar larvae. These tools enable functional, transcriptomic, and proteomic studies based on precise anatomical targeting. NeuronBridge and other search tools relate light microscopy images of these split-GAL4 lines to connectomes reconstructed from electron microscopy images. The collections are the result of screening over 77,000 split hemidriver combinations. In addition to images and fly stocks for these well-characterized lines, we make available 300,000 new 3D images of other split-GAL4 lines.

}, doi = {10.1101/2024.01.09.574419}, url = {https://www.biorxiv.org/content/early/2024/04/01/2024.01.09.574419}, author = {Geoffrey W Meissner and Allison Vannan and Jennifer Jeter and Kari Close and Gina M DePasquale and Zachary Dorman and Kaitlyn Forster and Jaye Anne Beringer and Theresa V Gibney and Joanna H Hausenfluck and Yisheng He and Kristin Henderson and Lauren Johnson and Rebecca M Johnston and Gudrun Ihrke and Nirmala Iyer and Rachel Lazarus and Kelley Lee and Hsing-Hsi Li and Hua-Peng Liaw and Brian Melton and Scott Miller and Reeham Motaher and Alexandra Novak and Omotara Ogundeyi and Alyson Petruncio and Jacquelyn Price and Sophia Protopapas and Susana Tae and Jennifer Taylor and Rebecca Vorimo and Brianna Yarbrough and Kevin Xiankun Zeng and Christopher T Zugates and Heather Dionne and Claire Angstadt and Kelly Ashley and Amanda Cavallaro and Tam Dang and Guillermo A Gonzalez III and Karen L Hibbard and Cuizhen Huang and Jui-Chun Kao and Todd Laverty and Monti Mercer and Brenda Perez and Scarlett Pitts and Danielle Ruiz and Viruthika Vallanadu and Grace Zhiyu Zheng and Cristian Goina and Hideo Otsuna and Konrad Rokicki and Robert R Svirskas and Han SJ Cheong and Michael-John Dolan and Erica Ehrhardt and Kai Feng and Basel El Galfi and Jens Goldammer and Stephen J Huston and Nan Hu and Masayoshi Ito and Claire McKellar and Ryo Minegishi and Shigehiro Namiki and Aljoscha Nern and Catherine E Schretter and Gabriella R Sterne and Lalanti Venkatasubramanian and Kaiyu Wang and Tanya Wolff and Ming Wu and Reed George and Oz Malkesman and Yoshinori Aso and Gwyneth M Card and Barry J Dickson and Wyatt Korff and Kei Ito and James W Truman and Marta Zlatic and Gerald M Rubin and FlyLight Project Team} } @article {69007, title = {Driver lines for studying associative learning in Drosophila}, journal = {bioRxiv}, year = {2023}, month = {2023 Sep 16}, abstract = {

The mushroom body (MB) is the center for associative learning in insects. In Drosophila, intersectional split-GAL4 drivers and electron microscopy (EM) connectomes have laid the foundation for precise interrogation of the MB neural circuits. However, many cell types upstream and downstream of the MB remained to be investigated due to lack of driver lines. Here we describe a new collection of over 800 split-GAL4 and split-LexA drivers that cover approximately 300 cell types, including sugar sensory neurons, putative nociceptive ascending neurons, olfactory and thermo-/hygro-sensory projection neurons, interneurons connected with the MB-extrinsic neurons, and various other cell types. We characterized activation phenotypes for a subset of these lines and identified the sugar sensory neuron line most suitable for reward substitution. Leveraging the thousands of confocal microscopy images associated with the collection, we analyzed neuronal morphological stereotypy and discovered that one set of mushroom body output neurons, MBON08/MBON09, exhibits striking individuality and asymmetry across animals. In conjunction with the EM connectome maps, the driver lines reported here offer a powerful resource for functional dissection of neural circuits for associative learning in adult Drosophila.

}, doi = {https://doi.org/10.7554/elife.94168.4}, url = {https://doi.org/10.7554/elife.94168.4 }, author = {Yichun Shuai and Megan Sammons and Gabriella Sterne and Karen Hibbard and He Yang and Ching-Po Yang and Claire Managan and Igor Siwanowicz and Tzumin Lee and Gerald M. Rubin and Glenn Turner and Yoshinori Aso} } @article {68790, title = {Flexible specificity of memory in Drosophila depends on a comparison between choices}, journal = {eLife}, year = {2023}, month = {2023 Jun 15}, abstract = {

Memory guides behavior across widely varying environments and must therefore be both sufficiently specific and general. A memory too specific will be useless in even a slightly different environment, while an overly general memory may lead to suboptimal choices. Animals successfully learn to both distinguish between very similar stimuli and generalize across cues. Rather than forming memories that strike a balance between specificity and generality,\ Drosophila\ can flexibly categorize a given stimulus into different groups depending on the options available. We asked how this flexibility manifests itself in the well-characterized learning and memory pathways of the fruit fly. We show that flexible categorization in neuronal activity as well as behavior depends on the order and identity of the perceived stimuli. Our results identify the neural correlates of flexible stimulus-categorization in the fruit fly.

}, doi = {10.7554/eLife.80923}, url = {https://doi.org/10.7554/eLife.80923}, author = {Mehrab N Modi and Adithya Rajagopalan and Herv{\'e} Rouault and Yoshinori Aso and Glenn C Turner} } @article {68008, title = {Hierarchical architecture of dopaminergic circuits enables second-order conditioning in Drosophila}, journal = {eLife}, year = {2023}, month = {2023 Jan 24}, abstract = {

Dopaminergic neurons with distinct projection patterns and physiological properties compose memory subsystems in a brain. However, it is poorly understood whether or how they interact during complex learning. Here, we identify a feedforward circuit formed between dopamine subsystems and show that it is essential for second-order conditioning, an ethologically important form of higher-order associative learning. The\ Drosophila\ mushroom body comprises a series of dopaminergic compartments, each of which exhibits distinct memory dynamics. We find that a slow and stable memory compartment can serve as an effective {\textquotedblleft}teacher{\textquotedblright} by instructing other faster and transient memory compartments via a single key interneuron, which we identify by connectome analysis and neurotransmitter prediction. This excitatory interneuron acquires enhanced response to reward-predicting odor after first-order conditioning and, upon activation, evokes dopamine release in the {\textquotedblleft}student{\textquotedblright} compartments. These hierarchical connections between dopamine subsystems explain distinct properties of first- and second-order memory long known by behavioral psychologists.

}, doi = {10.7554/eLife.79042}, url = {https://doi.org/10.7554/eLife.79042}, author = {Daichi Yamada and Daniel Bushey and Li Feng and Karen Hibbard and Megan Sammons and Jan Funke and Ashok Litwin-Kumar and Toshihide Hige and Yoshinori Aso} } @article {68571, title = {Neural circuit mechanisms for transforming learned olfactory valences into wind-oriented movement}, journal = {eLife}, year = {2023}, month = {2023 Sep 18}, abstract = {

How memories are used by the brain to guide future action is poorly understood. In olfactory associative learning in Drosophila, multiple compartments of the mushroom body act in parallel to assign valence to a stimulus. Here, we show that appetitive memories stored in different compartments induce different levels of upwind locomotion. Using a photoactivation screen of a new collection of split-GAL4 drivers and EM connectomics, we identified a cluster of neurons postsynaptic to the mushroom body output neurons (MBONs) that can trigger robust upwind steering. These UpWind Neurons (UpWiNs) integrate inhibitory and excitatory synaptic inputs from MBONs of appetitive and aversive memory compartments, respectively. After training, disinhibition from the appetitive-memory MBONs enhances the response of UpWiNs to reward-predicting odors. Blocking UpWiNs impaired appetitive memory and reduced upwind locomotion during retrieval. Photoactivation of UpWiNs also increased the chance of returning to a location where activation was initiated, suggesting an additional role in olfactory navigation. Thus, our results provide insight into how learned abstract valences are gradually transformed into concrete memory-driven actions through divergent and convergent networks, a neuronal architecture that is commonly found in the vertebrate and invertebrate brains.

}, doi = {10.7554/eLife.85756}, url = {https://doi.org/10.7554/eLife.85756}, author = {Yoshinori Aso and Daichi Yamada and Daniel Bushey and Karen Hibbard and Megan Sammons and Hideo Otsuna and Yichun Shuai and Toshihide Hige} } @article {68113, title = {One engram two readouts: stimulus dynamics switch a learned behavior in Drosophila}, journal = {bioRxiv}, year = {2022}, month = {2022 May 26}, abstract = {

Memory guides the choices an animal makes across widely varying conditions in dynamic environments. Consequently, the most adaptive choice depends on the options available. How can a single memory support optimal behavior across different sets of choice options? We address this using olfactory learning in Drosophila. Even when we restrict an odor-punishment association to a single set of synapses using optogenetics, we find that flies still show choice behavior that depends on the options it encounters. Here we show that how the odor choices are presented to the animal influences memory recall itself. Presenting two similar odors in sequence enabled flies to not only discriminate them behaviorally but also at the level of neural activity. However, when the same odors were encountered as solitary stimuli, no such differences were detectable. These results show that memory recall is not simply a comparison to a static learned template, but can be adaptively modulated by stimulus dynamics.

}, doi = {10.1101/2022.05.24.492551}, url = {https://doi.org/10.1101/2022.05.24.492551}, author = {Mehrab N Modi and Adithya Rajagopalan and Herv{\'e} Rouault and Yoshinori Aso and Glenn C Turner} } @article {64997, title = {Neural circuit basis of aversive odour processing in drosophila from sensory input to descending output.}, journal = {bioRxiv}, year = {2018}, month = {2018 Aug 26}, abstract = {

Evolution has tuned the nervous system of most animals to produce stereotyped behavioural responses to ethologically relevant stimuli. For example, female\ Drosophila\ avoid laying eggs in the presence of geosmin, an odorant produced by toxic moulds. Using this system, we now identify third order olfactory neurons that are essential for an innate aversive behaviour. Connectomics data place these neurons in the context of a complete synaptic circuit from sensory input to descending output. We find multiple levels of valence-specific convergence, including a novel form of axo-axonic input onto second order neurons conveying another danger signal, the pheromone of parasitoid wasps. However we also observe a massive divergence as geosmin-responsive second order olfactory neurons connect with a diverse array of \~{}75 cell types. Our data suggest a transition from a labelled line organisation in the periphery to one in which olfactory information is mapped onto many different higher order populations with distinct behavioural significance.

}, doi = {10.1101/394403}, author = {Paavo Huoviala and Michael-John Dolan and Fiona M. Love and Shahar Frechter and Ruair{\'\i} J.V. Roberts and Zane Mitrevica and Philipp Schlegel and Alexander Shakeel Bates and Yoshinori Aso and Tiago Rodrigues and Hannah Cornwall and Marcus Stensmyr and Davi Bock and Gerald M. Rubin and Marta Costa and Gregory S.X.E. Jefferis} }