A deep understanding of how the brain controls behaviour requires mapping neural circuits down to the muscles that they control. Here, we apply automated tools to segment neurons and identify synapses in an electron microscopy dataset of an adult female Drosophila melanogaster ventral nerve cord (VNC)1, which functions like the vertebrate spinal cord to sense and control the body. We find that the fly VNC contains roughly 45 million synapses and 14,600 neuronal cell bodies. To interpret the output of the connectome, we mapped the muscle targets of leg and wing motor neurons using genetic driver lines2 and X-ray holographic nanotomography3. With this motor neuron atlas, we identified neural circuits that coordinate leg and wing movements during take-off. We provide the reconstruction of VNC circuits, the motor neuron atlas and tools for programmatic and interactive access as resources to support experimental and theoretical studies of how the nervous system controls behaviour.
\nThe evolution of flight in an early winged insect ancestral lineage is recognized as a key adaptation explaining the unparalleled success and diversification of insects. Subsequent transitions and modifications to flight machinery, including secondary reductions and losses, also play a central role in shaping the impacts of insects on broadscale geographic and ecological processes and patterns in the present and future. Given the importance of insect flight, there has been a centuries-long history of research and debate on the evolutionary origins and biological mechanisms of flight. Here, we revisit this history from an interdisciplinary perspective, discussing recent discoveries regarding the developmental origins, physiology, biomechanics, and neurobiology and sensory control of flight in a diverse set of insect models. We also identify major outstanding questions yet to be addressed and provide recommendations for overcoming current methodological challenges faced when studying insect flight, which will allow the field to continue to move forward in new and exciting directions. By integrating mechanistic work into ecological and evolutionary contexts, we hope that this synthesis promotes and stimulates new interdisciplinary research efforts necessary to close the many existing gaps about the causes and consequences of insect flight evolution.
", "doi": "10.1093/icb/icae106", "issn": "1540-7063", "publisher": "Oxford University Press", "publication": "Integrative And Comparative Biology", "publication_date": "2024-07-09", "pages": "icae106" }, { "id": "authors:ekf21-21m53", "collection": "authors", "collection_id": "ekf21-21m53", "cite_using_url": "https://authors.library.caltech.edu/records/ekf21-21m53", "type": "article", "title": "Synaptic architecture of leg and wing premotor control networks in Drosophila", "author": [ { "family_name": "Lesser", "given_name": "Ellen", "orcid": "0000-0001-7929-0503", "clpid": "Lesser-Ellen" }, { "family_name": "Azevedo", "given_name": "Anthony W.", "orcid": "0000-0001-8318-9678" }, { "family_name": "Phelps", "given_name": "Jasper S.", "orcid": "0000-0001-8033-258X" }, { "family_name": "Elabbady", "given_name": "Leila", "orcid": "0000-0002-1452-1603" }, { "family_name": "Cook", "given_name": "Andrew", "orcid": "0000-0002-4349-2880" }, { "family_name": "Syed", "given_name": "Durafshan Sakeena", "orcid": "0000-0002-5729-0382" }, { "family_name": "Mark", "given_name": "Brandon", "orcid": "0000-0002-2607-3756" }, { "family_name": "Kuroda", "given_name": "Sumiya", "orcid": "0000-0001-9633-087X" }, { "family_name": "Sustar", "given_name": "Anne", "orcid": "0000-0002-9821-7456" }, { "family_name": "Moussa", "given_name": "Anthony", "orcid": "0000-0002-9538-2747" }, { "family_name": "Dallmann", "given_name": "Chris J.", "orcid": "0000-0002-4944-920X" }, { "family_name": "Agrawal", "given_name": "Sweta", "orcid": "0000-0003-0547-4099" }, { "family_name": "Lee", "given_name": "Su-Yee J.", "orcid": "0000-0001-5160-9930" }, { "family_name": "Pratt", "given_name": "Brandon", "orcid": "0000-0001-7620-9423" }, { "family_name": "Skutt-Kakaria", "given_name": "Kyobi", "orcid": "0000-0002-7826-6736", "clpid": "Skutt-Kakaria-Kyobi" }, { "family_name": "Gerhard", "given_name": "Stephan", "orcid": "0000-0003-4454-6171" }, { "family_name": "Lu", "given_name": "Ran" }, { "family_name": "Kemnitz", "given_name": "Nico", "orcid": "0000-0002-0260-4532" }, { "family_name": "Lee", "given_name": "Kisuk", "orcid": "0000-0001-5707-0155" }, { "family_name": "Halageri", "given_name": "Akhilesh", "orcid": "0000-0001-7367-4776" }, { "family_name": "Castro", "given_name": "Manuel" }, { "family_name": "Ih", "given_name": "Dodam", "orcid": "0000-0002-0295-8445" }, { "family_name": "Gager", "given_name": "Jay", "orcid": "0000-0002-8001-2604" }, { "family_name": "Tammam", "given_name": "Marwan", "orcid": "0000-0002-5550-4164" }, { "family_name": "Dorkenwald", "given_name": "Sven", "orcid": "0000-0003-2352-319X" }, { "family_name": "Collman", "given_name": "Forrest", "orcid": "0000-0002-0280-7022" }, { "family_name": "Schneider-Mizell", "given_name": "Casey", "orcid": "0000-0001-9477-3853" }, { "family_name": "Brittain", "given_name": "Derrick", "orcid": "0000-0003-3890-2862" }, { "family_name": "Jordan", "given_name": "Chris S." }, { "family_name": "Macrina", "given_name": "Thomas", "orcid": "0000-0002-0622-9338" }, { "family_name": "Dickinson", "given_name": "Michael", "orcid": "0000-0002-8587-9936", "clpid": "Dickinson-M-H" }, { "family_name": "Lee", "given_name": "Wei-Chung Allen", "orcid": "0000-0002-4618-295X" }, { "family_name": "Tuthill", "given_name": "John C.", "orcid": "0000-0002-5689-5806" } ], "abstract": "Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles1. MN activity is coordinated by complex premotor networks that facilitate the contribution of individual muscles to many different behaviours2,3,4,5,6. Here we use connectomics7 to analyse the wiring logic of premotor circuits controlling the Drosophila leg and wing. We find that both premotor networks cluster into modules that link MNs innervating muscles with related functions. Within most leg motor modules, the synaptic weights of each premotor neuron are proportional to the size of their target MNs, establishing a circuit basis for hierarchical MN recruitment. By contrast, wing premotor networks lack proportional synaptic connectivity, which may enable more flexible recruitment of wing steering muscles. Through comparison of the architecture of distinct motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.
\nInsects 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 legs1, 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 wings2. 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 network3 that accurately predicts wing motion from the activity of the steering muscles, and an encoder–decoder4 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.
\nThe clumped distribution of resources in the world has influenced the pattern of foraging behavior since the origins of locomotion, selecting for a common search motif in which straight movements through resource-poor regions alternate with zig-zag exploration in resource-rich domains. For example, during local search, flying flies spontaneously execute rapid flight turns, called body saccades, but suppress these maneuvers during long-distance dispersal or when surging upstream toward an attractive odor. Here, we describe the key cellular components of a neural network in flies that generate spontaneous turns as well as a specialized pair of neurons that inhibits the network and suppresses turning. Using 2-photon imaging, optogenetic activation, and genetic ablation, we show that only four descending neurons appear sufficient to generate the descending commands to execute flight saccades. The network is organized into two functional units—one for right turns and one for left—with each unit consisting of an excitatory (DNae014) and an inhibitory (DNb01) neuron that project to the flight motor neuropil within the ventral nerve cord. Using resources from recently published connectomes of the fly, we identified a pair of large, distinct interneurons (VES041) that form inhibitory connections to all four saccade command neurons and created specific genetic driver lines for this cell. As predicted by its connectivity, activation of VES041 strongly suppresses saccades, suggesting that it promotes straight flight to regulate the transition between local search and long-distance dispersal. These results thus identify the key elements of a network that may play a crucial role in foraging ecology.
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