Igor Siwanowicz

Protein aggregate formation can cause cell damage and contribute to tissue pathologies including neurodegeneration. Protein aggregates are normally eliminated by cell-intrinsic mechanisms of lysosomal (autophagic) degradation. However, degradative capacity varies between cell types, raising the possibility that high-capacity cell types, such as macrophages, may contribute to aggregate disposal from the low-capacity cells. Here, we show that cellular protein aggregates are removed by macrophages via trogocytosis — a partial engulfment of the target cells. Lysosomal stress upon protein aggregate accumulation promotes cell surface expression of the transmembrane isoform of the macrophage growth factor CSF1 (tmCSF1), which is detected by macrophages through the CSF1 receptor to drive trogocytosis. Insufficient or impaired degradation of protein aggregates in lysosomes results in their transport along microtubules to the plasma membrane where they physically associate with tmCSF1. This enables macrophages to specifically trogocytose protein aggregates. These findings reveal a protein homeostasis system that eliminates protein aggregates in a non-cell-autonomous manner.

Animals actively sense their surroundings to acquire behaviorally relevant environmental cues and stimuli. This dynamic acquisition of sensory information is enabled by active positioning of sensors and helps guide behavioral responses in dynamic environments. Yet how these active movements are controlled during behavior and coordinated with ongoing sensory acquisition is not fully understood. In the fruit fly Drosophila melanogaster , the antennae are crucial sensors for extracting important information from the environment including mechanosensory, olfactory, and auditory signals. Just four distinct muscles command movement of the antennae, providing a tractable model system for understanding efferent control of sensation. This work characterizes motor neurons used by Drosophila to actively position the antennae. We first identify antennal motor neurons in the central brain, and map each one from a comprehensive connectomic dataset to its peripheral muscle target. Our analysis of presynaptic inputs to the entire antennal motor system reveals a diverse array of premotor neurons for antennal motor control. We then provide genetic access to each motor unit by building a library of genetic lines with expression in antennal motor neurons. Using this library of antennal motor neuron lines, we next characterize motor unit function with quantitative behavior and optogenetics, revealing that the antennal motor system produces two primary movements in the dorsal-ventral and medial-lateral axes. Together, this work provides a comprehensive framework for understanding the motor control of an active sensor.

In most animals, a small number of descending neurons (DNs) connect the brain to circuits and motor neurons (MNs) in the nerve cord. To understand how brain signals generate behavior, it is critical to understand the organization of the neural pathways linking DNs to MNs. In companion papers, we introduced a densely reconstructed connectome of the Drosophila Male Adult Nerve Cord (MANC; Takemura et al., 2024), including cell types and developmental lineages (Marin et al., 2024), which provides complete connectivity of the ventral nerve cord (VNC) at synaptic resolution. Here, we present a first look at the organization of the networks connecting DNs to MNs. We first proofread and curated all DNs and MNs, then systematically matched their morphology to light microscopy data. 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 neuron communities putatively linked to control of different motor systems, including walking, flight steering and power generation, and coordinated action of wings and legs. Our analyses generate hypotheses for future functional experiments and empowers others to investigate these and other circuits of the VNC in richer mechanistic detail.

In most animals, a relatively small number of descending neurons (DNs) connect higher brain centers in the animal’s head to circuits and motor neurons (MNs) in the nerve cord of the animal’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.

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.

To perform most behaviors, animals must send commands from higher-order processing centers in the brain to premotor circuits that reside in ganglia distinct from the brain, such as the mammalian spinal cord or insect ventral nerve cord. How these circuits are functionally organized to generate the great diversity of animal behavior remains unclear. An important first step in unraveling the organization of premotor circuits is to identify their constituent cell types and create tools to monitor and manipulate these with high specificity to assess their functions. This is possible in the tractable ventral nerve cord of the fly. To generate such a toolkit, we used a combinatorial genetic technique (split-GAL4) to create 195 sparse transgenic driver lines targeting 196 individual cell types in the ventral nerve cord. These included wing and haltere motoneurons, modulatory neurons, and interneurons. Using a combination of behavioral, developmental, and anatomical analyses, we systematically characterized the cell types targeted in our collection. In addition, we identified correspondences between the cells in this collection and a recent connectomic data set of the ventral nerve cord. Taken together, the resources and results presented here form a powerful toolkit for future investigations of neuronal circuits and connectivity of premotor circuits while linking them to behavioral outputs.

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, investigation of many cell types upstream and downstream of the MB has been hindered due to lack of specific 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 a 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.

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, investigation of many cell types upstream and downstream of the MB has been hindered due to lack of specific 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 a 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.

SUMMARY: 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.

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 modelled 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’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. Significance Statement: Many systems in nature precisely control highly deformable structures, yet monitoring structural deformations has posed a significant challenge for biologists and engineers. By measuring and modelling the intricate structure of compliant dragonfly wings, we demonstrate an elegant solution for mechanosensory representation. We show that the functional integration of natural wing biomechanics and sensor placement provides a straightforward solution for information transfer. Wing morphology passively constrains the range of natural deformations, producing strain patterns that align with sensor locations, allowing them to monitor the wing using a simple timing-dependent encoding strategy. The way insects monitor aerodynamic and inertial forces via wing deformations during flight could apply to many sensory systems in nature and inspire artificial neural networks for controlling diverse dynamic systems.