Explore the vast range of titles available.

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 lines 2 and X-ray holographic nanotomography 3 . 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. Automated reconstruction of dense neural networks in the ventral nerve cord of the fruit fly provides a resource for investigating the neural control of movement.

Abrams et al. report that a simple dietary supplement is sufficient to induce appendage regeneration in jellyfish, fruit flies, and mice (Abrams et al., 2021). This conclusion is surprising because it was previously thought that flies and mice lack the capacity for regeneration after injury. We replicated the Drosophila experiments of Abrams et al. but did not observe any instances of leg regeneration. We also conclude that the \"white blob\" observed at the amputation site by Abrams et al. consists of bacteria and is not regenerated tissue.

Somatosensory neurons provide the nervous system with information about mechanical forces originating inside and outside the body. Here, we use connectomics from electron microscopy to reconstruct and analyze neural circuits downstream of the largest somatosensory organ in the Drosophila leg, the femoral chordotonal organ (FeCO). The FeCO has been proposed to support both proprioceptive sensing of the fly’s femur-tibia joint and exteroceptive sensing of substrate vibrations, but it was unknown which sensory neurons and central circuits contribute to each of these functions. We found that different subtypes of FeCO sensory neurons feed into distinct proprioceptive and exteroceptive pathways. Position- and movement-encoding FeCO neurons connect to local leg motor control circuits in the ventral nerve cord (VNC), indicating a proprioceptive function. In contrast, signals from the vibration-encoding FeCO neurons are integrated across legs and transmitted to mechanosensory regions in the brain, indicating an exteroceptive function. Overall, our analyses reveal the structure of specialized circuits for processing proprioceptive and exteroceptive signals from the fly leg. These findings are consistent with a growing body of work in invertebrate and vertebrate species demonstrating the existence of specialized limb mechanosensory pathways for sensing external vibrations. Determining whether somatosensory neurons are involved in internal or external sensing remains a challenge. Here, the authors show that analyzing connectivity is a powerful approach to identify putative neural functions of somatosensory neurons in the fly.

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. By reconstructing CxHP8 axons in an electron microscopy dataset, 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. The physiology and behavioral function of proprioceptors that detect joint limits are not fully understood. In this study, the authors used calcium imaging, optogenetics, behavioral genetics, and the connectome to demonstrate that hair plate proprioceptors on the fly leg detect joint limits and engage circuits to drive the leg away from those limits.

Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles 1 . MN activity is coordinated by complex premotor networks that facilitate the contribution of individual muscles to many different behaviours 2 – 6 . Here we use connectomics 7 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. We use connectomics to compare the wiring logic of premotor circuits controlling the Drosophila leg and wing, finding that both premotor networks cluster into modules that link motor neurons innervating muscles with related functions.

A new approach tracks animal movements in 3D from multiple camera views using volumetric triangulation, reconciling occlusions and ambiguities present in any one camera view.

Proprioception, the sense of self-movement and position, is mediated by mechanosensory neurons that detect diverse features of body kinematics. Although proprioceptive feedback is crucial for accurate motor control, little is known about how downstream circuits transform limb sensory information to guide motor output. Here we investigate neural circuits in Drosophila that process proprioceptive information from the fly leg. We identify three cell types from distinct developmental lineages that are positioned to receive input from proprioceptor subtypes encoding tibia position, movement, and vibration. 13Bα neurons encode femur-tibia joint angle and mediate postural changes in tibia position. 9Aα neurons also drive changes in leg posture, but encode a combination of directional movement, high frequency vibration, and joint angle. Activating 10Bα neurons, which encode tibia vibration at specific joint angles, elicits pausing in walking flies. Altogether, our results reveal that central circuits integrate information across proprioceptor subtypes to construct complex sensorimotor representations that mediate diverse behaviors, including reflexive control of limb posture and detection of leg vibration.

To move the body, the brain must precisely coordinate patterns of activity among diverse populations of motor neurons. Here, we use in vivo calcium imaging, electrophysiology, and behavior to understand how genetically-identified motor neurons control flexion of the fruit fly tibia. We find that leg motor neurons exhibit a coordinated gradient of anatomical, physiological, and functional properties. Large, fast motor neurons control high force, ballistic movements while small, slow motor neurons control low force, postural movements. Intermediate neurons fall between these two extremes. This hierarchical organization resembles the size principle, first proposed as a mechanism for establishing recruitment order among vertebrate motor neurons. Recordings in behaving flies confirmed that motor neurons are typically recruited in order from slow to fast. However, we also find that fast, intermediate, and slow motor neurons receive distinct proprioceptive feedback signals, suggesting that the size principle is not the only mechanism that dictates motor neuron recruitment. Overall, this work reveals the functional organization of the fly leg motor system and establishes Drosophila as a tractable system for investigating neural mechanisms of limb motor control. In the body, spindly nerve cells called motor neurons connect the brain to the muscles. Their role is to control movement, as they translate the electrical signals from the brain into instructions to the muscles. In humans, it takes over 150,000 motor neurons to control the movement of one leg; in contrast, fruit flies only need 50 neurons to operate a leg, despite also executing a variety of movements. Fruit flies are commonly used in laboratories to study an array of biological processes, yet little is known about how their motor neurons direct movements. In particular, it was unclear whether the same principles that control how muscles contract in mammals also applied in the tiny fruit fly. To begin investigating, Azevedo et al. mapped out the arrangement of motor neurons that control muscles in the fruit fly leg. As the leg moved, the activity of both the neurons and the muscles they controlled was recorded, as well as the force that had been generated. The experiments showed that each motor neuron controls a certain range of leg force and speed: some produced small, slow motion important for posture and dexterity, while others created large, fast movements essential to running or escape. In addition, the neurons activate in a particular order – cells that control slow movements fire first, and those that direct fast maneuvers later. These processes are also found in other organisms, but the difference is that flies have so few neurons, allowing scientists to reliably identify each motor neuron. Future experiments will therefore be able to test how flies recruit the right neurons to create specific movement sequences. Fruit flies are often used to research human illnesses that affect movement, such as motor neuron disease. A better understanding of the way their neural circuits coordinate the body could help reveal how these conditions emerge.

Walking animals must maintain stability in the presence of external perturbations, despite significant temporal delays in neural signaling and muscle actuation. Here, we develop a 3D kinematic model with a layered control architecture to investigate how sensorimotor delays constrain the robustness of walking behavior in the fruit fly, Drosophila . Motivated by the anatomical architecture of insect locomotor control circuits, our model consists of three component layers: a neural network that generates realistic 3D joint kinematics for each leg, an optimal controller that executes the joint kinematics while accounting for delays, and an inter-leg coordinator. The model generates realistic simulated walking that resembles real fly walking kinematics and sustains walking even when subjected to unexpected perturbations, generalizing beyond its training data. However, we found that the model’s robustness to perturbations deteriorates when sensorimotor delay parameters exceed the physiological range. These results suggest that fly sensorimotor control circuits operate close to the temporal limit at which they can detect and respond to external perturbations. More broadly, we show how a modular, layered model architecture can be used to investigate physiological constraints on animal behavior.

Conspecific pollen precedence (CPP) is a major component of reproductive isolation between many flowering plant taxa and may reveal mechanisms of gametophytic evolution within species, but little is known about the genetic basis and evolutionary history of CPP. We systematically investigated the genetic architecture of CPP using patterns of transmission ratio distortion (TRD) in F2 and backcross hybrids between closely related species of Mimulus (Phrymaceae) with divergent mating systems. We found that CPP in Mimulus hybrids was polygenic and was the majority source of interspecific TRD genome-wide, with at least eight genomic regions contributing to the transmission advantage of M. guttatus pollen grains on M. guttatus styles. In aggregate, these male-specific transmission ratio distorting loci (TRDLs) were more than sufficient to account for the 100% precedence of pure M. guttatus pollen over M. nasutus pollen in mixed pollinations of M. guttatus. All but one of these pollen TRDLs were style-dependent; that is, we observed pollen TRD in F1 and/or M. guttatus styles, but not in M. nasutus styles. These findings suggest that species-specific differences in pollen tube performance accumulate gradually and may have been driven by coevolution between pollen and style in the predominantly outcrossing M. guttatus.