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1. Presynaptic Gαo (GOA-1) signals to depress command neuron excitability and allow stretch-dependent modulation of egg laying in Caenorhabditis elegans

   https://doi.org/10.1093/genetics/iyab080  

   Ravi, Bhavya ; Zhao, Jian ; Chaudhry, Sana I ; Signorelli, Rossana ; Bartole, Mattingly ; Kopchock, Richard J ; Guijarro, Christian ; Kaplan, Joshua M ; Kang, Lijun ; Collins, Kevin M ( August 2021 , Genetics)

   Bülow, H (Ed.)

   Abstract Egg laying in the nematode worm Caenorhabditis elegans is a two-state behavior modulated by internal and external sensory input. We have previously shown that homeostatic feedback of embryo accumulation in the uterus regulates bursting activity of the serotonergic HSN command neurons that sustains the egg-laying active state. How sensory feedback of egg release signals to terminate the egg-laying active state is less understood. We find that Gαo, a conserved Pertussis Toxin-sensitive G protein, signals within HSN to inhibit egg-laying circuit activity and prevent entry into the active state. Gαo signaling hyperpolarizes HSN, reducing HSN Ca2+ activity and input onto the postsynaptic vulval muscles. Loss of inhibitory Gαo signaling uncouples presynaptic HSN activity from a postsynaptic, stretch-dependent homeostat, causing precocious entry into the egg-laying active state when only a few eggs are present in the uterus. Feedback of vulval opening and egg release activates the uv1 neuroendocrine cells which release NLP-7 neuropeptides which signal to inhibit egg laying through Gαo-independent mechanisms in the HSNs and Gαo-dependent mechanisms in cells other than the HSNs. Thus, neuropeptide and inhibitory Gαo signaling maintain a bi-stable state of electrical excitability that dynamically controls circuit activity in response to both external and internal sensory input to drive a two-state behavior output. 

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2. Serotonin signals through postsynaptic Gαq, Trio RhoGEF, and diacylglycerol to promote Caenorhabditis elegans egg-laying circuit activity and behavior

   https://doi.org/10.1093/genetics/iyac084  

   Dhakal, Pravat ; Chaudhry, Sana I. ; Signorelli, Rossana ; Collins, Kevin M. ; Pocock, ed., R. ( May 2022 , Genetics)

   Activated Gαq signals through phospholipase-Cβ and Trio, a Rho GTPase exchange factor (RhoGEF), but how these distinct effector pathways promote cellular responses to neurotransmitters like serotonin remains poorly understood. We used the egg-laying behavior circuit of Caenorhabditis elegans to determine whether phospholipase-Cβ and Trio mediate serotonin and Gαq signaling through independent or related biochemical pathways. Our genetic rescue experiments suggest that phospholipase-Cβ functions in neurons while Trio Rho GTPase exchange factor functions in both neurons and the postsynaptic vulval muscles. While Gαq, phospholipase-Cβ, and Trio Rho GTPase exchange factor mutants fail to lay eggs in response to serotonin, optogenetic stimulation of the serotonin-releasing HSN neurons restores egg laying only in phospholipase-Cβ mutants. Phospholipase-Cβ mutants showed vulval muscle Ca2+ transients while strong Gαq and Trio Rho GTPase exchange factor mutants had little or no vulval muscle Ca2+ activity. Treatment with phorbol 12-myristate 13-acetate that mimics 1,2-diacylglycerol, a product of PIP2 hydrolysis, rescued egg-laying circuit activity and behavior defects of Gαq signaling mutants, suggesting both phospholipase-C and Rho signaling promote synaptic transmission and egg laying via modulation of 1,2-diacylglycerol levels. 1,2-Diacylglycerol activates effectors including UNC-13; however, we find that phorbol esters, but not serotonin, stimulate egg laying in unc-13 and phospholipase-Cβ mutants. These results support a model where serotonin signaling through Gαq, phospholipase-Cβ, and UNC-13 promotes neurotransmitter release, and that serotonin also signals through Gαq, Trio Rho GTPase exchange factor, and an unidentified, phorbol 12-myristate 13-acetate-responsive effector to promote postsynaptic muscle excitability. Thus, the same neuromodulator serotonin can signal in distinct cells and effector pathways to coordinate activation of a motor behavior circuit.

    

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3. Whole-organism behavioral profiling reveals a role for dopamine in state-dependent motor program coupling in C. elegans

   https://doi.org/10.7554/eLife.57093  

   Cermak, Nathan ; Yu, Stephanie K ; Clark, Rebekah ; Huang, Yung-Chi ; Baskoylu, Saba N ; Flavell, Steven W ( June 2020 , eLife)

   null (Ed.)

   Animals generate many different motor programs (such as moving, feeding and grooming) that they can alter in response to internal needs and environmental cues. These motor programs are controlled by dedicated brain circuits that act on specific muscle groups. However, little is known about how organisms coordinate these different motor programs to ensure that their resulting behavior is coherent and appropriate to the situation. This is difficult to investigate in large organisms with complex nervous systems, but with 302 brain cells that control 143 muscle cells, the small worm Caenorhabditis elegans provides a good system to examine this question. Here, Cermak, Yu, Clark et al. devised imaging methods to record each type of motor program in C. elegans worms over long time periods, while also dissecting the underlying neural mechanisms that coordinate these motor programs. This constitutes one of the first efforts to capture and quantify all the behavioral outputs of an entire organism at once. The experiments also showed that dopamine – a messenger molecule in the brain – links the neural circuits that control two motor programs: movement and egg-laying. A specific type of high-speed movement activates brain cells that release dopamine, which then transmits this information to the egg-laying circuit. This means that worms lay most of their eggs whilst traveling at high speed through a food source, so that their progeny can be distributed across a nutritive environment. This work opens up the possibility to study how behaviors are coordinated at the level of the whole organism – a departure from the traditional way of focusing on how specific neural circuits generate specific behaviors. Ultimately, it will also be interesting to look at the role of dopamine in behavior coordination in a wide range of animals. 

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4. Timing precision in fly flight control: integrating mechanosensory input with muscle physiology

   https://doi.org/10.1098/rspb.2020.1774  

   Dickerson, Bradley H. ( December 2020 , Proceedings of the Royal Society B: Biological Sciences)

   null (Ed.)

   Animals rapidly collect and act on incoming information to navigate complex environments, making the precise timing of sensory feedback critical in the context of neural circuit function. Moreover, the timing of sensory input determines the biomechanical properties of muscles that undergo cyclic length changes, as during locomotion. Both of these issues come to a head in the case of flying insects, as these animals execute steering manoeuvres at timescales approaching the upper limits of performance for neuromechanical systems. Among insects, flies stand out as especially adept given their ability to execute manoeuvres that require sub-millisecond control of steering muscles. Although vision is critical, here I review the role of rapid, wingbeat-synchronous mechanosensory feedback from the wings and structures unique to flies, the halteres. The visual system and descending interneurons of the brain employ a spike rate coding scheme to relay commands to the wing steering system. By contrast, mechanosensory feedback operates at faster timescales and in the language of motor neurons, i.e. spike timing, allowing wing and haltere input to dynamically structure the output of the wing steering system. Although the halteres have been long known to provide essential input to the wing steering system as gyroscopic sensors, recent evidence suggests that the feedback from these vestigial hindwings is under active control. Thus, flies may accomplish manoeuvres through a conserved hindwing circuit, regulating the firing phase—and thus, the mechanical power output—of the wing steering muscles. 

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5. The connectome of an insect brain

   https://doi.org/10.1126/science.add9330  

   Winding, Michael ; Pedigo, Benjamin D. ; Barnes, Christopher L. ; Patsolic, Heather G. ; Park, Youngser ; Kazimiers, Tom ; Fushiki, Akira ; Andrade, Ingrid V. ; Khandelwal, Avinash ; Valdes-Aleman, Javier ; et al ( March 2023 , Science)

   INTRODUCTION A brainwide, synaptic-resolution connectivity map—a connectome—is essential for understanding how the brain generates behavior. However because of technological constraints imaging entire brains with electron microscopy (EM) and reconstructing circuits from such datasets has been challenging. To date, complete connectomes have been mapped for only three organisms, each with several hundred brain neurons: the nematode C. elegans , the larva of the sea squirt Ciona intestinalis , and of the marine annelid Platynereis dumerilii . Synapse-resolution circuit diagrams of larger brains, such as insects, fish, and mammals, have been approached by considering select subregions in isolation. However, neural computations span spatially dispersed but interconnected brain regions, and understanding any one computation requires the complete brain connectome with all its inputs and outputs. RATIONALE We therefore generated a connectome of an entire brain of a small insect, the larva of the fruit fly, Drosophila melanogaster. This animal displays a rich behavioral repertoire, including learning, value computation, and action selection, and shares homologous brain structures with adult Drosophila and larger insects. Powerful genetic tools are available for selective manipulation or recording of individual neuron types. In this tractable model system, hypotheses about the functional roles of specific neurons and circuit motifs revealed by the connectome can therefore be readily tested. RESULTS The complete synaptic-resolution connectome of the Drosophila larval brain comprises 3016 neurons and 548,000 synapses. We performed a detailed analysis of the brain circuit architecture, including connection and neuron types, network hubs, and circuit motifs. Most of the brain’s in-out hubs (73%) were postsynaptic to the learning center or presynaptic to the dopaminergic neurons that drive learning. We used graph spectral embedding to hierarchically cluster neurons based on synaptic connectivity into 93 neuron types, which were internally consistent based on other features, such as morphology and function. We developed an algorithm to track brainwide signal propagation across polysynaptic pathways and analyzed feedforward (from sensory to output) and feedback pathways, multisensory integration, and cross-hemisphere interactions. We found extensive multisensory integration throughout the brain and multiple interconnected pathways of varying depths from sensory neurons to output neurons forming a distributed processing network. The brain had a highly recurrent architecture, with 41% of neurons receiving long-range recurrent input. However, recurrence was not evenly distributed and was especially high in areas implicated in learning and action selection. Dopaminergic neurons that drive learning are amongst the most recurrent neurons in the brain. Many contralateral neurons, which projected across brain hemispheres, were in-out hubs and synapsed onto each other, facilitating extensive interhemispheric communication. We also analyzed interactions between the brain and nerve cord. We found that descending neurons targeted a small fraction of premotor elements that could play important roles in switching between locomotor states. A subset of descending neurons targeted low-order post-sensory interneurons likely modulating sensory processing. CONCLUSION The complete brain connectome of the Drosophila larva will be a lasting reference study, providing a basis for a multitude of theoretical and experimental studies of brain function. The approach and computational tools generated in this study will facilitate the analysis of future connectomes. Although the details of brain organization differ across the animal kingdom, many circuit architectures are conserved. As more brain connectomes of other organisms are mapped in the future, comparisons between them will reveal both common and therefore potentially optimal circuit architectures, as well as the idiosyncratic ones that underlie behavioral differences between organisms. Some of the architectural features observed in the Drosophila larval brain, including multilayer shortcuts and prominent nested recurrent loops, are found in state-of-the-art artificial neural networks, where they can compensate for a lack of network depth and support arbitrary, task-dependent computations. Such features could therefore increase the brain’s computational capacity, overcoming physiological constraints on the number of neurons. Future analysis of similarities and differences between brains and artificial neural networks may help in understanding brain computational principles and perhaps inspire new machine learning architectures. The connectome of the Drosophila larval brain. The morphologies of all brain neurons, reconstructed from a synapse-resolution EM volume, and the synaptic connectivity matrix of an entire brain. This connectivity information was used to hierarchically cluster all brains into 93 cell types, which were internally consistent based on morphology and known function. 

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