@article {69722, title = {Days-old zebrafish rapidly learn to recognize threatening agents through noradrenergic and forebrain circuits.}, journal = {Curr Biol}, volume = {35}, year = {2025}, month = {12/2024}, pages = {163-176.e4}, abstract = {

Animals need to rapidly learn to recognize and avoid predators. This ability may be especially important for young animals due to their increased vulnerability. It is unknown whether, and how, nascent vertebrates are capable of such rapid learning. Here, we used a robotic predator-prey interaction assay to show that 1\ week after fertilization-a developmental stage where they have approximately 1\% the number of neurons of adults-zebrafish larvae rapidly and robustly learn to recognize a stationary object as a threat after the object pursues the fish for \~{}1\ min. Larvae continue to avoid the threatening object after it stops moving and can learn to distinguish threatening from non-threatening objects of a different color. Whole-brain functional imaging revealed the multi-timescale activity of noradrenergic neurons and forebrain circuits that encoded the threat. Chemogenetic ablation of those populations prevented the learning. Thus, a noradrenergic and forebrain multiregional network underlies the ability of young vertebrates to rapidly learn to recognize potential predators within their first week of life.

}, keywords = {Adrenergic Neurons, Animals, Larva, Learning, Predatory Behavior, Prosencephalon, Zebrafish}, issn = {1879-0445}, doi = {10.1016/j.cub.2024.11.057}, author = {Zocchi, Dhruv and Nguyen, Millen and Marquez-Legorreta, Emmanuel and Siwanowicz, Igor and Singh, Chanpreet and Prober, David A and Hillman, Elizabeth M C and Ahrens, Misha B} } @article {70084, title = {Imaging cellular activity simultaneously across all organs of a vertebrate reveals body-wide circuits}, journal = {bioRxiv}, year = {2025}, month = {2025 August 22}, abstract = {

All cells in an animal collectively ensure, moment-to-moment, the survival of the whole organism in the face of environmental stressors1,2. Physiology seeks to elucidate the intricate network of interactions that sustain life, which often span multiple organs, cell types, and timescales, but a major challenge lies in the inability to simultaneously record time-varying cellular activity throughout the entire body.We developed WHOLISTIC, a method to image second-timescale, time-varying intracellular dynamics across cell-types of the vertebrate body. By advancing and integrating volumetric fluorescence microscopy, machine learning, and pancellular transgenic expression of calcium sensors in transparent young Danio rerio (zebrafish) and adult Danionella, the method enables real-time recording of cellular dynamics across the organism. Calcium is a universal intracellular messenger, with a large array of cellular processes depending on changes in calcium concentration across varying time-scales, making it an ideal proxy of cellular activity3.Using this platform to screen the dynamics of all cells in the body, we discovered unexpected responses of specific cell types to stimuli, such as chondrocyte reactions to cold, meningeal responses to ketamine, and state-dependent activity, such as oscillatory ependymal-cell activity during periods of extended motor quiescence. At the organ scale, the method uncovered pulsating traveling waves along the kidney nephron. At the multi-organ scale, we uncovered muscle synergies and independencies, as well as muscle-organ interactions. Integration with optogenetics allowed us to all-optically determine the causal direction of brain-body interactions. At the whole-organism scale, the method captured the rapid brainstem-controlled redistribution of blood flow across the body.Finally, we advanced Whole-Body Expansion Microscopy4 to provide ground-truth molecular and ultrastructural anatomical context, explaining the spatiotemporal structure of activity captured by WHOLISTIC. Together, these innovations establish a new paradigm for systems biology, bridging cellular and organismal physiology, with broad implications for both fundamental research and drug discovery.

}, doi = {10.1101/2025.08.20.670374}, url = {https://www.biorxiv.org/content/early/2025/08/22/2025.08.20.670374.1}, author = {Ruetten, Virginia M. S. and Zheng, Wei and Siwanowicz, Igor and Mensh, Brett D. and Eddison, Mark and Hu, Amy and Chi, Yunfeng and Lemire, Andrew L. and Guo, Caiying and Kadobianskyi, Mykola and Renz, Marc and Lelek-Greskovic, Sara and He, Yisheng and Close, Kari and Ihrke, Gudrun and Dev, Aparna and Petruncio, Alyson and Wan, Yinan and Engert, Florian and Fishman, Mark C. and Judkewitz, Benjamin and Rubinov, Mikail and Keller, Philipp J. and Satou, Chie and Yu, Guoqiang and Tillberg, Paul W. and Sahani, Maneesh and Ahrens, Misha B.} } @article {69949, title = {Proprioceptive limit detectors mediate sensorimotor control of the Drosophila leg}, journal = {bioRxiv}, year = {2025}, month = {2025 May 15}, abstract = {

Many animals possess mechanosensory neurons that fire when a limb nears the limit of its physical range, but the function of these proprioceptive limit detectors remains poorly understood. Here, we investigate a class of proprioceptors on the Drosophila leg called hair plates. Using calcium imaging in behaving flies, we find that a hair plate on the fly coxa (CxHP8) detects the limits of anterior leg movement. Reconstructing CxHP8 axons in the connectome, we found that they are wired to excite posterior leg movement and inhibit anterior leg movement. Consistent with this connectivity, optogenetic activation of CxHP8 neurons elicited posterior postural reflexes, while silencing altered the swing-to-stance transition during walking. Finally, we use comprehensive reconstruction of peripheral morphology and downstream connectivity to predict the function of other hair plates distributed across the fly leg. Our results suggest that each hair plate is specialized to control specific sensorimotor reflexes that are matched to the joint limit it detects. They also illustrate the feasibility of predicting sensorimotor reflexes from a connectome with identified proprioceptive inputs and motor outputs.

}, doi = {10.1101/2025.05.15.654260}, url = {https://www.biorxiv.org/content/early/2025/05/19/2025.05.15.654260}, author = {Pratt, Brandon G. and Dallmann, Chris J. and Chou, Grant M. and Siwanowicz, Igor and Walling-Bell, Sarah and Cook, Andrew and Sustar, Anne and Azevedo, Anthony and Tuthill, John C.} } @article {70195, title = {Structural dynamics and neural representation of wing deformation.}, journal = {Proc Natl Acad Sci USA}, volume = {122}, year = {2025}, month = {2025 Nov 18}, pages = {e2518032122}, abstract = {

Locomotor control is facilitated by mechanosensory inputs that report how the body interacts with a physical medium. Effective representation of compliant wing deformations is particularly challenging due to the many degrees of freedom. Structural configurations can constrain the stimulus space, and strategic placement of sensors can simplify computation. Here, we measured and modeled wing displacement fields and characterized spatiotemporal encoding of the wing mechanosensors. Our data show how dragonfly wing architecture prescribes deformation modes consistent across models and measurements. We found that the wing{\textquoteright}s state under normal flapping conditions is detected by the spike timing of few sensors, with additional sensors recruited under perturbation. The functional integration of wing biomechanics and sensor placement enables a straightforward solution for information transfer.

}, keywords = {Animals, Biomechanical Phenomena, Flight, Animal, Models, Biological, Odonata, Wings, Animal}, issn = {1091-6490}, doi = {10.1073/pnas.2518032122}, url = {https://doi.org/10.1073/pnas.2518032122}, author = {Yarger, Alexandra M and Maeda, Masateru and Siwanowicz, Igor and Kajiyama, Haruhiro and Walker, Simon M and Bomphrey, Richard J and Lin, Huai-Ti} } @article {70030, title = {Transforming descending input into motor output: An analysis of the Drosophila Male Adult Nerve Cord connectome}, journal = {eLife}, year = {2025}, month = {2025 Jul 21}, abstract = {

In most animals, a relatively small number of descending neurons (DNs) connect higher brain centers in the animal{\textquoteright}s head to circuits and motor neurons (MNs) in the nerve cord of the animal{\textquoteright}s body that effect movement of the limbs. To understand how brain signals generate behavior, it is critical to understand how these descending pathways are organized onto the body MNs. In the fly,\ Drosophila melanogaster, MNs controlling muscles in the leg, wing, and other motor systems reside in a ventral nerve cord (VNC), analogous to the mammalian spinal cord. In companion papers, we introduced a densely-reconstructed connectome of the\ Drosophila\ Male Adult Nerve Cord (MANC, (Takemura et al., 2024)), including cell type and developmental lineage annotation (Marin et al., 2024), which provides complete VNC connectivity at synaptic resolution. Here, we present a first look at the organization of the VNC networks connecting DNs to MNs based on this new connectome information. We proofread and curated all DNs and MNs to ensure accuracy and reliability, then systematically matched DN axon terminals and MN dendrites with light microscopy data to link their VNC morphology with their brain inputs or muscle targets. We report both broad organizational patterns of the entire network and fine-scale analysis of selected circuits of interest. We discover that direct DN-MN connections are infrequent and identify communities of intrinsic neurons linked to control of different motor systems, including putative ventral circuits for walking, dorsal circuits for flight steering and power generation, and intermediate circuits in the lower tectulum for coordinated action of wings and legs. Our analysis generates hypotheses for future functional experiments and, together with the MANC connectome, empowers others to investigate these and other circuits of the\ Drosophila\ ventral nerve cord in richer mechanistic detail.

}, doi = {10.7554/elife.96084.2}, url = {http://dx.doi.org/10.7554/eLife.96084.2}, author = {Cheong, Han Sj and Eichler, Katharina and St{\"u}rner, Tomke and Asinof, Samuel K and Champion, Andrew S and Marin, Elizabeth C and Oram, Tess B and Sumathipala, Marissa and Venkatasubramanian, Lalanti and Namiki, Shigehiro and Siwanowicz, Igor and Costa, Marta and Berg, Stuart and Jefferis, Gregory Sxe and Card, Gwyneth M} } @article {69706, title = {Days-old zebrafish rapidly learn to recognize threatening agents through noradrenergic and forebrain circuits.}, journal = {Curr Biol}, year = {2024}, month = {2024 Dec 19}, abstract = {

Animals need to rapidly learn to recognize and avoid predators. This ability may be especially important for young animals due to their increased vulnerability. It is unknown whether, and how, nascent vertebrates are capable of such rapid learning. Here, we used a robotic predator-prey interaction assay to show that 1\ week after fertilization-a developmental stage where they have approximately 1\% the number of neurons of adults-zebrafish larvae rapidly and robustly learn to recognize a stationary object as a threat after the object pursues the fish for \~{}1\ min. Larvae continue to avoid the threatening object after it stops moving and can learn to distinguish threatening from non-threatening objects of a different color. Whole-brain functional imaging revealed the multi-timescale activity of noradrenergic neurons and forebrain circuits that encoded the threat. Chemogenetic ablation of those populations prevented the learning. Thus, a noradrenergic and forebrain multiregional network underlies the ability of young vertebrates to rapidly learn to recognize potential predators within their first week of life.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2024.11.057}, author = {Zocchi, Dhruv and Nguyen, Millen and Marquez-Legorreta, Emmanuel and Siwanowicz, Igor and Singh, Chanpreet and Prober, David A and Hillman, Elizabeth M C and Ahrens, Misha B} } @article {69401, title = {Machine learning reveals the control mechanics of an insect wing hinge}, journal = {Nature}, volume = {628}, year = {2024}, month = {2024 Apr 17}, pages = {795-803}, abstract = {

Insects constitute the most species-rich radiation of metazoa, a success that is due to the evolution of active flight. Unlike pterosaurs, birds and bats, the wings of insects did not evolve from legs, but are novel structures that are attached to the body via a biomechanically complex hinge that transforms tiny, high-frequency oscillations of specialized power muscles into the sweeping back-and-forth motion of the wings. The hinge consists of a system of tiny, hardened structures called sclerites that are interconnected to one another via flexible joints and regulated by the activity of specialized control muscles. Here we imaged the activity of these muscles in a fly using a genetically encoded calcium indicator, while simultaneously tracking the three-dimensional motion of the wings with high-speed cameras. Using machine learning, we created a convolutional neural network that accurately predicts wing motion from the activity of the steering muscles, and an encoder-decoder that predicts the role of the individual sclerites on wing motion. By replaying patterns of wing motion on a dynamically scaled robotic fly, we quantified the effects of steering muscle activity on aerodynamic forces. A physics-based simulation incorporating our hinge model generates flight manoeuvres that are remarkably similar to those of free-flying flies. This integrative, multi-disciplinary approach reveals the mechanical control logic of the insect wing hinge, arguably among the most sophisticated and evolutionarily important skeletal structures in the natural world.

}, keywords = {Animals, Biomechanical Phenomena, Calcium, Drosophila melanogaster, Female, Flight, Animal, Machine Learning, Movement, Muscles, Neural Networks, Computer, Robotics, Wings, Animal}, issn = {1476-4687}, doi = {10.1038/s41586-024-07293-4}, author = {Melis, Johan M and Siwanowicz, Igor and Dickinson, Michael H} } @article {69484, title = {Mechanosensory representation of wing deformations}, journal = {bioRxiv}, year = {2024}, month = {2024 Jun 02}, abstract = {

Efficient representation of structural deformations is crucial for monitoring the instantaneous state of biological structures. Insects{\textquoteright} ability to encode wing deformations during flight demonstrates a general morphological computing principle applicable across sensory systems in nature as well as engineered systems. To characterize how relevant features are encoded, we measured and modelled displacement and strain across dragonfly wing surfaces in tethered and free flight. Functional interpretations were supported by neuroanatomical maps, and ablation and perturbation experiments. We find that signal redundancy is reduced by non-random sensor distributions and that morphology limits the stimulus space such that sensory systems can monitor natural states with few sensors. Deviations from the natural states are detected by a flexible population of additional sensors with many distinguishable activation patterns.

}, doi = {10.1101/2024.06.02.596338}, url = {https://www.biorxiv.org/content/early/2024/06/02/2024.06.02.596338}, author = {Yarger, Alexandra M and Maeda, Masateru and Siwanowicz, Igor and Kajiyama, Haruhiro and Walker, Simon M and Bomphrey, Richard J and Lin, Huai-Ti} } @article {69301, title = {Motor neurons generate pose-targeted movements via proprioceptive sculpting.}, journal = {Nature}, year = {2024}, month = {2024 Mar 20}, abstract = {

Motor neurons are the final common pathway through which the brain controls movement of the body, forming the basic elements from which all movement is composed. Yet how a single motor neuron contributes to control during natural movement remains unclear. Here we anatomically and functionally characterize the individual roles of the motor neurons that control head movement in the fly, Drosophila melanogaster. Counterintuitively, we find that activity in a single motor neuron rotates the head in different directions, depending on the starting posture of the head, such that the head converges towards a pose determined by the identity of the stimulated motor neuron. A feedback model predicts that this convergent behaviour results from motor neuron drive interacting with proprioceptive feedback. We identify and genetically suppress a single class of proprioceptive neuron that changes the motor neuron-induced convergence as predicted by the feedback model. These data suggest a framework for how the brain controls movements: instead of directly generating movement in a given direction by activating a fixed set of motor neurons, the brain controls movements by adding bias to a continuing proprioceptive-motor loop.

}, issn = {1476-4687}, doi = {10.1038/s41586-024-07222-5}, author = {Gorko, Benjamin and Siwanowicz, Igor and Close, Kari and Christoforou, Christina and Hibbard, Karen L and Kabra, Mayank and Lee, Allen and Park, Jin-Yong and Li, Si Ying and Chen, Alex B and Namiki, Shigehiro and Chen, Chenghao and Tuthill, John C and Bock, Davi D and Rouault, Herv{\'e} and Branson, Kristin and Ihrke, Gudrun and Huston, Stephen J} } @article {69679, title = {Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning}, journal = {bioRxiv}, year = {2024}, month = {2024 Nov 22}, abstract = {

The body of an animal influences how the nervous system produces behavior. Therefore, detailed modeling of the neural control of sensorimotor behavior requires a detailed model of the body. Here we contribute an anatomically-detailed biomechanical whole-body model of the fruit fly Drosophila melanogaster in the MuJoCo physics engine. Our model is general-purpose, enabling the simulation of diverse fly behaviors, both on land and in the air. We demonstrate the generality of our model by simulating realistic locomotion, both flight and walking. To support these behaviors, we have extended MuJoCo with phenomenological models of fluid forces and adhesion forces. Through data-driven end-to-end reinforcement learning, we demonstrate that these advances enable the training of neural network controllers capable of realistic locomotion along complex trajectories based on high-level steering control signals. We demonstrate the use of visual sensors and the re-use of a pre-trained general-purpose flight controller by training the model to perform visually guided flight tasks. Our project is an open-source platform for modeling neural control of sensorimotor behavior in an embodied context.Competing Interest StatementThe authors have declared no competing interest.

}, doi = {10.1101/2024.03.11.584515}, url = {https://www.biorxiv.org/content/early/2024/11/22/2024.03.11.584515}, author = {Vaxenburg, Roman and Siwanowicz, Igor and Merel, Josh and Robie, Alice A. and Morrow, Carmen and Novati, Guido and Stefanidi, Zinovia and Both, Gert-Jan and Card, Gwyneth M. and Reiser, Michael B. and Botvinick, Matthew M. and Branson, Kristin M. and Tassa, Yuval and Turaga, Srinivas C.} } @article {69667, title = {Wireless recordings from dragonfly target detecting neurons during prey interception flight}, journal = {bioRxiv}, year = {2024}, month = {2024 Nov 13}, abstract = {

Target interception is a complex sensorimotor behavior which requires fine tuning of the sensory system and its strategic coordination with the motor system. Despite various theories about how interception is achieved, its neural implementation remains unknown. We have previously shown that hunting dragonflies employ a balance of reactive and predictive control to intercept prey, using sophisticated model driven predictions to account for expected prey and self-motion. Here we explore the neural substrate of this interception system by investigating a well-known class of target-selective descending neurons (TSDNs). These cells have long been speculated to underlie interception steering but have never been studied in a behaving dragonfly. We combined detailed neuroanatomy, high-precision kinematics data and state-of-the-art neural telemetry to measure TSDN activity during flight. We found that TSDNs are exquisitely tuned to prey angular size and speed at ethological distances, and that they synapse directly onto neck and wing motoneurons in an unusual manner. However, we found that TSDNs were only weakly active during flight and are thus unlikely to provide the primary steering signal. Instead, they appear to drive the foveating head movements that stabilize prey on the eye before and likely throughout the interception flight. We suggest the TSDN population implements the reactive portion of the interception steering control system, coordinating head and wing movements to compensate for unexpected prey motion.

}, doi = {10.1101/2024.11.12.622977}, url = {https://doi.org/10.1101/2024.11.12.622977}, author = {Lin, Huai-Ti and Siwanowicz, Igor and Leonardo, Anthony} } @article {68933, title = {Biomechanical origins of proprioceptor feature selectivity and topographic maps in the Drosophila leg.}, journal = {Neuron}, year = {2023}, month = {2023 Aug 04}, abstract = {

Our ability to sense and move our bodies relies on proprioceptors, sensory neurons that detect mechanical forces within the body. Different subtypes of proprioceptors detect different kinematic features, such as joint position, movement, and vibration, but the mechanisms that underlie proprioceptor feature selectivity remain poorly understood. Using single-nucleus RNA sequencing (RNA-seq), we found that proprioceptor subtypes in the Drosophila leg lack differential expression of mechanosensitive ion channels. However, anatomical reconstruction of the proprioceptors and connected tendons revealed major biomechanical differences between subtypes. We built a model of the proprioceptors and tendons that identified a biomechanical mechanism for joint angle selectivity and predicted the existence of a topographic map of joint angle, which we confirmed using calcium imaging. Our findings suggest that biomechanical specialization is a key determinant of proprioceptor feature selectivity in Drosophila. More broadly, the discovery of proprioceptive maps reveals common organizational principles between proprioception and other topographically organized sensory systems.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2023.07.009}, author = {Mamiya, Akira and Sustar, Anne and Siwanowicz, Igor and Qi, Yanyan and Lu, Tzu-Chiao and Gurung, Pralaksha and Chen, Chenghao and Phelps, Jasper S and Kuan, Aaron T and Pacureanu, Alexandra and Lee, Wei-Chung Allen and Li, Hongjie and Mhatre, Natasha and Tuthill, John C} } @article {68413, title = {Muscles that move the retina augment compound eye vision in Drosophila.}, journal = {Nature}, year = {2022}, month = {2022 Oct 26}, abstract = {

Most animals have compound eyes, with tens to thousands of lenses attached rigidly to the exoskeleton. A natural assumption is that all of these species must resort to moving either their head or\ their body to actively change their visual input. However, classic anatomy has revealed that flies have muscles poised to move their retinas under the stable lenses of each compound eye. Here we show that Drosophila use their retinal muscles to smoothly track visual motion, which helps to stabilize the retinal image, and also to perform small saccades when viewing a stationary scene. We show that when the retina moves, visual receptive fields shift accordingly, and that even the smallest retinal saccades activate visual neurons. Using a head-fixed behavioural paradigm, we find that Drosophila perform binocular, vergence movements of their retinas-which could enhance depth perception-when crossing gaps, and impairing the physiology of retinal motor neurons alters gap-crossing trajectories during free behaviour. That flies evolved an ability to actuate their retinas suggests that moving the eye independently of the head is broadly paramount for animals. The similarities of smooth and saccadic movements of the Drosophila retina and the vertebrate eye highlight a notable example of convergent evolution.

}, issn = {1476-4687}, doi = {10.1038/s41586-022-05317-5}, author = {Fenk, Lisa M and Avritzer, Sofia C and Weisman, Jazz L and Nair, Aditya and Randt, Lucas D and Mohren, Thomas L and Siwanowicz, Igor and Maimon, Gaby} } @article {68022, title = {Systematic characterization of wing mechanosensors that monitor airflow and wing deformations.}, journal = {iScience}, volume = {25}, year = {2022}, month = {2022 Apr 15}, pages = {104150}, abstract = {

Animal wings deform during flight in ways that can enhance lift, facilitate flight control, and mitigate damage. Monitoring the structural and aerodynamic state of the wing is challenging because deformations are passive, and the flow fields are unsteady; it requires distributed mechanosensors that respond to local airflow and strain on the wing. Without a complete map of the sensor arrays, it is impossible to model control strategies underpinned by them. Here, we present the first systematic characterization of mechanosensors on the dragonfly{\textquoteright}s wings: morphology, distribution, and wiring. By combining a cross-species survey of sensor distribution with quantitative neuroanatomy and a high-fidelity finite element analysis, we show that the mechanosensors are well placed to perceive features of the wing dynamics relevant to flight. This work describes the wing sensory apparatus in its entirety and advances our understanding of the sensorimotor loop that facilitates exquisite flight control in animals with highly deformable wings.

}, issn = {2589-0042}, doi = {10.1016/j.isci.2022.104150}, author = {Fabian, Joseph and Siwanowicz, Igor and Uhrhan, Myriam and Maeda, Masateru and Bomphrey, Richard J and Lin, Huai-Ti} } @article {67181, title = {A novel family of secreted insect proteins linked to plant gall development.}, journal = {Current Biology}, volume = {31}, year = {2021}, month = {2021 May 10}, pages = {1836}, abstract = {

In an elaborate form of inter-species exploitation, many insects hijack plant development to induce novel plant organs called galls that provide the insect with a source of nutrition and a temporary home. Galls result from dramatic reprogramming of plant cell biology driven by insect molecules, but the roles of specific insect molecules in gall development have not yet been determined. Here, we study the aphid Hormaphis cornu, which makes distinctive "cone" galls on leaves of witch hazel Hamamelis virginiana. We found that derived genetic variants in the aphid gene determinant of gall color (dgc) are associated with strong downregulation of dgc transcription in aphid salivary glands, upregulation in galls of seven genes involved in anthocyanin synthesis, and deposition of two red anthocyanins in galls. We hypothesize that aphids inject DGC protein into galls and that this results in differential expression of a small number of plant genes. dgc is a member of a large, diverse family of novel predicted secreted proteins characterized by a pair of widely spaced cysteine-tyrosine-cysteine (CYC) residues, which we named BICYCLE proteins. bicycle genes are most strongly expressed in the salivary glands specifically of galling aphid generations, suggesting that they may regulate many aspects of gall development. bicycle genes have experienced unusually frequent diversifying selection, consistent with their potential role controlling gall development in a molecular arms race between aphids and their host plants.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2021.01.104}, author = {Korgaonkar, Aishwarya and Han, Clair and Lemire, Andrew L and Siwanowicz, Igor and Bennouna, Djawed and Kopec, Rachel E and Andolfatto, Peter and Shigenobu, Shuji and Stern, David L} } @article {65642, title = {Controlling motor neurons of every muscle for fly proboscis reaching.}, journal = {eLife}, volume = {9}, year = {2020}, month = {2020 Jun 25}, abstract = {

We describe the anatomy of all the primary motor neurons in the fly proboscis and characterize their contributions to its diverse reaching movements. Pairing this behavior with the wealth of genetic tools offers the possibility to study motor control at single-neuron resolution, and soon throughout entire circuits. As an entry to these circuits, we provide detailed anatomy of proboscis motor neurons, muscles, and joints. We create a collection of fly strains to individually manipulate every proboscis muscle through control of its motor neurons, the first such collection for an appendage. We generate a model of the action of each proboscis joint, and find that only a small number of motor neurons are needed to produce proboscis reaching. Comprehensive control of each motor element in this numerically simple system paves the way for future study of both reflexive and flexible movements of this appendage.

}, issn = {2050-084X}, doi = {10.7554/eLife.54978}, author = {McKellar, Claire E and Siwanowicz, Igor and Dickson, Barry J and Simpson, Julie H} } @article {65852, title = {Multi-regional circuits underlying visually guided decision-making in Drosophila.}, journal = {Current Opinion in Neurobiology}, volume = {65}, year = {2020}, month = {2020 Nov 17}, pages = {77-87}, abstract = {

Visually guided decision-making requires integration of information from distributed brain areas, necessitating a brain-wide approach to examine its neural mechanisms. New tools in Drosophila melanogaster enable circuits spanning the brain to be charted with single cell-type resolution. Here, we highlight recent advances uncovering the computations and circuits that transform and integrate visual information across the brain to make behavioral choices. Visual information flows from the optic lobes to three primary central brain regions: a sensorimotor mapping area and two {\textquoteright}higher{\textquoteright} centers for memory or spatial orientation. Rapid decision-making during predator evasion emerges from the spike timing dynamics in parallel sensorimotor cascades. Goal-directed decisions may occur through memory, navigation and valence processing in the central complex and mushroom bodies.

}, issn = {1873-6882}, doi = {10.1016/j.conb.2020.10.010}, author = {Cheong, Han and Siwanowicz, Igor and Card, Gwyneth M} } @article {65907, title = {Multi-regional circuits underlying visually guided decision-making in Drosophila.}, journal = {Current Opinion in Neurobiology}, volume = {65}, year = {2020}, month = {2020 Dec 01}, pages = {77-87}, abstract = {

Visually guided decision-making requires integration of information from distributed brain areas, necessitating a brain-wide approach to examine its neural mechanisms. New tools in Drosophila melanogaster enable circuits spanning the brain to be charted with single cell-type resolution. Here, we highlight recent advances uncovering the computations and circuits that transform and integrate visual information across the brain to make behavioral choices. Visual information flows from the optic lobes to three primary central brain regions: a sensorimotor mapping area and two {\textquoteright}higher{\textquoteright} centers for memory or spatial orientation. Rapid decision-making during predator evasion emerges from the spike timing dynamics in parallel sensorimotor cascades. Goal-directed decisions may occur through memory, navigation and valence processing in the central complex and mushroom bodies.

}, issn = {1873-6882}, doi = {10.1016/j.conb.2020.10.010}, author = {Cheong, Han Sj and Siwanowicz, Igor and Card, Gwyneth M} } @article {64917, title = {A neural circuit encoding the experience of copulation in female Drosophila.}, journal = {Neuron}, volume = {102}, year = {2019}, month = {2019 Apr 26}, pages = {1025}, abstract = {

Female behavior changes profoundly after mating. In Drosophila, the mechanisms underlying the long-term changes led by seminal products have been extensively studied. However, the effect of the sensory component of copulation on the female{\textquoteright}s internal state and behavior remains elusive. We pursued this question by dissociating the effect of coital sensory inputs from those of male ejaculate. We found that the sensory inputs of copulation cause a reduction of post-coital receptivity in females, referred to as the "copulation effect." We identified three layers of a neural circuit underlying this phenomenon. Abdominal neurons expressing the mechanosensory channel Piezo convey the signal of copulation to female-specific ascending neurons, LSANs, in the ventral nerve cord. LSANs relay this information to neurons expressing myoinhibitory peptides in the brain. We hereby provide a\ neural mechanism by which the experience of\ copulation facilitates females encoding their mating\ status, thus adjusting behavior to optimize reproduction.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2019.04.009}, author = {Shao, Lisha and Chung, Phuong and Wong, Allan and Siwanowicz, Igor and Kent, Clement F and Long, Xi and Heberlein, Ulrike} } @article {65229, title = {The yellow gene influences Drosophila male mating success through sex comb melanization.}, journal = {eLife}, volume = {8}, year = {2019}, month = {2019 Oct 15}, abstract = {

males perform a series of courtship behaviors that, when successful, result in copulation with a female. For over a century, mutations in the gene, named for its effects on pigmentation, have been known to reduce male mating success. Prior work has suggested that influences mating behavior through effects on wing extension, song, and/or courtship vigor. Here, we rule out these explanations, as well as effects on the nervous system more generally, and find instead that the effects of on male mating success are mediated by its effects on pigmentation of male-specific leg structures called sex combs. Loss of expression in these modified bristles reduces their melanization, which changes their structure and causes difficulty grasping females prior to copulation. These data illustrate why the mechanical properties of anatomy, not just neural circuitry, must be considered to fully understand the development and evolution of behavior.

}, issn = {2050-084X}, doi = {10.7554/eLife.49388}, author = {Massey, Jonathan H and Chung, Daayun and Siwanowicz, Igor and Stern, David L and Wittkopp, Patricia J} } @article {48624, title = {Three dimensional reconstruction of energy stores for jumping in planthoppers and froghoppers from confocal laser scanning microscopy.}, journal = {eLife}, volume = {6}, year = {2017}, month = {2017 Jun 21}, abstract = {

Jumping in planthopper and froghopper insects is propelled by a catapult-like mechanism requiring mechanical storage of energy and its quick release to accelerate the hind legs rapidly. To understand the functional biomechanics involved in these challenging movements, the internal skeleton, tendons and muscles involved were reconstructed in 3-D from confocal scans in unprecedented detail. Energy to power jumping was generated by slow contractions of hind leg depressor muscles and then stored by bending specialised elements of the thoracic skeleton that are composites of the rubbery protein resilin sandwiched between layers of harder cuticle with air-filled tunnels reducing mass. The images showed that the lever arm of the power-producing muscle changed in magnitude during jumping, but at all joint angles would cause depression, suggesting a mechanism by which the stored energy is released. This methodological approach illuminates how miniaturized components interact and function in complex and rapid movements of small animals.

}, issn = {2050-084X}, doi = {10.7554/eLife.23824}, author = {Siwanowicz, Igor and Burrows, Malcolm} } @article {47726, title = {The genome of the crustacean Parhyale hawaiensis: a model for animal development, regeneration, immunity and lignocellulose digestion.}, journal = {eLife}, volume = {5}, year = {2016}, month = {2016 Nov 16}, pages = {e20062}, abstract = {

Parhyale hawaiensis is a blossoming model system for studies of developmental mechanisms and more recently adult regeneration. We have sequenced the genome allowing annotation of all key signaling pathways, small non-coding RNAs and transcription factors that will enhance ongoing functional studies. Parhayle is a member of the Malacostraca, which includes crustacean food crop species. We analysed the immunity related genes of Parhyale as an important comparative system for these species, where immunity related aquaculture problems have increased as farming has intensified. We also find that Parhyale and other species within Multicrustacea contain the enzyme sets necessary to perform lignocellulose digestion (wood eating), suggesting this ability may predate the diversification of this lineage. Our data provide an essential resource for further development of the Parhyale model. The first Malacostracan genome sequence will underpin ongoing comparative work in important food crop species and research investigating lignocellulose as energy source.

Publication first appeared in BioRxiv on August 2, 2016.\ http://dx.doi.org/10.1101/065789

}, doi = {10.7554/eLife.20062}, author = {Kao, Damian and Lai, Alvina G and Stamataki, Evangelia and Rosic, Silvana and Konstantinides, Nikolaos and Jarvis, Erin and Di Donfrancesco, Alessia and Pouchkina-Stantcheva, Natalia and Semon, Marie and Grillo, Marco and Bruce, Heather and Kumar, Suyash and Siwanowicz, Igor and Le, Andy and Lemire, Andrew and Extavour, Cassandra and Browne, William and Wolff, Carsten and Averof, Michalis and et al} } @article {6300, title = {A subset of dopamine neurons signals reward for odour memory in Drosophila.}, journal = {Nature}, volume = {488}, year = {2012}, month = {2012 Aug 23}, pages = {512-6}, abstract = {

Animals approach stimuli that predict a pleasant outcome. After the paired presentation of an odour and a reward, Drosophila melanogaster can develop a conditioned approach towards that odour. Despite recent advances in understanding the neural circuits for associative memory and appetitive motivation, the cellular mechanisms for reward processing in the fly brain are unknown. Here we show that a group of dopamine neurons in the protocerebral anterior medial (PAM) cluster signals sugar reward by transient activation and inactivation of target neurons in intact behaving flies. These dopamine neurons are selectively required for the reinforcing property of, but not a reflexive response to, the sugar stimulus. In vivo calcium imaging revealed that these neurons are activated by sugar ingestion and the activation is increased on starvation. The output sites of the PAM neurons are mainly localized to the medial lobes of the mushroom bodies (MBs), where appetitive olfactory associative memory is formed. We therefore propose that the PAM cluster neurons endow a positive predictive value to the odour in the MBs. Dopamine in insects is known to mediate aversive reinforcement signals. Our results highlight the cellular specificity underlying the various roles of dopamine and the importance of spatially segregated local circuits within the MBs.

}, keywords = {Animals, Appetitive Behavior, Calcium Signaling, Dendrites, Dopamine, Dopaminergic Neurons, Drosophila melanogaster, Memory, Mushroom Bodies, Odors, Reward, Smell}, issn = {1476-4687}, doi = {10.1038/nature11304}, author = {Liu, Chang and Placais, Pierre-Yves and Yamagata, Nobuhiro and Pfeiffer, Barret D and Aso, Yoshinori and Friedrich, Anja B and Siwanowicz, Igor and Rubin, Gerald M and Preat, Thomas and Tanimoto, Hiromu} } @article {5291, title = {Mushroom body efferent neurons responsible for aversive olfactory memory retrieval in Drosophila.}, journal = {Nature Neuroscience}, volume = {14}, year = {2011}, month = {2011 Jun 19}, pages = {903-10}, abstract = {

Aversive olfactory memory is formed in the mushroom bodies in Drosophila melanogaster. Memory retrieval requires mushroom body output, but the manner in which a memory trace in the mushroom body drives conditioned avoidance of a learned odor remains unknown. To identify neurons that are involved in olfactory memory retrieval, we performed an anatomical and functional screen of defined sets of mushroom body output neurons. We found that MB-V2 neurons were essential for retrieval of both short- and long-lasting memory, but not for memory formation or memory consolidation. MB-V2 neurons are cholinergic efferent neurons that project from the mushroom body vertical lobes to the middle superiormedial protocerebrum and the lateral horn. Notably, the odor response of MB-V2 neurons was modified after conditioning. As the lateral horn has been implicated in innate responses to repellent odorants, we propose that MB-V2 neurons recruit the olfactory pathway involved in innate odor avoidance during memory retrieval.

}, keywords = {Afferent Pathways, Analysis of Variance, Animal, Animals, Antigens, Avoidance Learning, Behavior, Biological, CD8, Choline O-Acetyltransferase, Conditioning (Psychology), Drosophila melanogaster, Drosophila Proteins, Gene Expression Regulation, Genetically Modified, Glutamate Decarboxylase, Green Fluorescent Proteins, Memory, Mental Recall, Models, Motor Neurons, Mushroom Bodies, Odors, Olfactory Pathways, Short-Term, Smell, Synapsins, Temperature, Time Factors, Transcription Factors, Tyrosine 3-Monooxygenase, Vesicular Glutamate Transport Proteins}, issn = {1546-1726}, doi = {10.1038/nn.2846}, author = {S{\'e}journ{\'e}, Julien and Placais, Pierre-Yves and Aso, Yoshinori and Siwanowicz, Igor and Trannoy, S{\'e}verine and Thoma, Vladimiros and Tedjakumala, Stevanus R and Rubin, Gerald M and Tch{\'e}nio, Paul and Ito, Kei and Isabel, Guillaume and Tanimoto, Hiromu and Preat, Thomas} }