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Nociception - the detection of harmful stimuli by the nervous system - contributes to both rapid escape and long-term avoidance behaviors. larvae detect damaging heat, mechanical, and chemical stimuli with specialized multidendritic (md) neurons, and these cells are among the only sensory neurons that survive metamorphosis. However, it remains unknown which somatosensory neurons contribute to nociception in adult flies. In an optogenetic screen, we found that abdominal md neurons were the only somatosensory class to induce rapid escape and sustained place avoidance. Calcium imaging from abdominal md axons revealed that they are activated by thermal nociceptive stimuli (>40°C). Connectomic reconstruction showed that md axons form their strongest synaptic connections with ascending interneurons that project to the brain. Among these, we identified two classes of ascending neurons that mediate rapid escape responses and a third that supports sustained avoidance. Our findings reveal that adult meet several core criteria commonly used to define pain: dedicated nociceptors, ascending pathways connecting peripheral sensors to integrative brain centers, and a capacity for sustained avoidance of noxious stimuli.

Just as genomes revolutionized molecular genetics, connectomes (maps of neurons and synapses) are transforming neuroscience. To date, the only species with complete connectomes are worms and sea squirts (10 -10 synapses). By contrast, the fruit fly is more complex (10 synaptic connections), with a brain that supports learning and spatial memory and an intricate ventral nerve cord analogous to the vertebrate spinal cord . Here we report the first adult fly connectome that unites the brain and ventral nerve cord, and we leverage this resource to investigate principles of neural control. We show that effector cells (motor neurons, endocrine cells and efferent neurons targeting the viscera) are primarily influenced by local sensory cells in the same body part, forming local feedback loops. These local loops are linked by long-range circuits involving ascending and descending neurons organized into behavior-centric modules. Single ascending and descending neurons are often positioned to influence the voluntary movements of multiple body parts, together with endocrine cells or visceral organs that support those movements. Brain regions involved in learning and navigation supervise these circuits. These results reveal an architecture that is distributed, parallelized and embodied (tightly connected to effectors), reminiscent of distributed control architectures in engineered systems .

Natural behaviors are a coordinated symphony of motor acts which drive self-induced or reafferent sensory activation. Single sensors only signal presence and magnitude of a sensory cue; they cannot disambiguate exafferent (externally-induced) from reafferent sources. Nevertheless, animals readily differentiate between these sources of sensory signals to make appropriate decisions and initiate adaptive behavioral outcomes. This is mediated by predictive motor signaling mechanisms, which emanate from motor control pathways to sensory processing pathways, but how predictive motor signaling circuits function at the cellular and synaptic level is poorly understood. We use a variety of techniques, including connectomics from both male and female electron microscopy volumes, transcriptomics, neuroanatomical, physiological and behavioral approaches to resolve the network architecture of two pairs of ascending histaminergic neurons (AHNs), which putatively provide predictive motor signals to several sensory and motor neuropil. Both AHN pairs receive input primarily from an overlapping population of descending neurons, many of which drive wing motor output. The two AHN pairs target almost exclusively non-overlapping downstream neural networks including those that process visual, auditory and mechanosensory information as well as networks coordinating wing, haltere, and leg motor output. These results support the conclusion that the AHN pairs multi-task, integrating a large amount of common input, then tile their output in the brain, providing predictive motor signals to non-overlapping sensory networks affecting motor control both directly and indirectly.

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 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.

Animals continuously monitor their body surfaces to detect and remove debris or parasites. Effective grooming requires that tactile inputs from specific body regions be converted into precisely targeted motor actions, but the neural circuits that support this sensorimotor transformation remain poorly understood. Here, we combine genetic tools and connectomics to elucidate a central somatotopic map of the Drosophila leg. We show that the axonal projections of leg touch receptors within the fly's ventral nerve cord (VNC) are organized along the same cardinal axes as the developing leg. Somatotopically organized bristle axons target a specific class of developmentally-related local interneurons, which imbricate the leg map with overlapping receptive fields of different shapes and sizes. These second-order interneurons target distinct pools of premotor interneurons, which in turn synapse directly onto motor neurons that control leg muscles. Optogenetic activation of second-order interneurons elicits spatially targeted grooming of specific leg regions, consistent with our spatial receptive field predictions based on the connectome. Together, our results suggest that this four-layer circuit processes spatial information from a somatotopic map of the fly leg to guide targeted grooming behavior.Competing Interest StatementThe authors have declared no competing interest.

Dengue-suppressing Wolbachia strains are promising tools for arbovirus control, particularly as they have the potential to self-spread following local introductions. To test this, we followed the frequency of the transinfected Wolbachia strain wMel through Ae. aegypti in Cairns, Australia, following releases at 3 nonisolated locations within the city in early 2013. Spatial spread was analysed graphically using interpolation and by fitting a statistical model describing the position and width of the wave. For the larger 2 of the 3 releases (covering 0.97 km2 and 0.52 km2), we observed slow but steady spatial spread, at about 100-200 m per year, roughly consistent with theoretical predictions. In contrast, the smallest release (0.11 km2) produced erratic temporal and spatial dynamics, with little evidence of spread after 2 years. This is consistent with the prediction concerning fitness-decreasing Wolbachia transinfections that a minimum release area is needed to achieve stable local establishment and spread in continuous habitats. Our graphical and likelihood analyses produced broadly consistent estimates of wave speed and wave width. Spread at all sites was spatially heterogeneous, suggesting that environmental heterogeneity will affect large-scale Wolbachia transformations of urban mosquito populations. The persistence and spread of Wolbachia in release areas meeting minimum area requirements indicates the promise of successful large-scale population transformation.

Fire activity in Australia is strongly affected by high inter-annual climate variability and extremes. Through changes in the climate, anthropogenic climate change has the potential to alter fire dynamics. Here we compile satellite (19 and 32 years) and ground-based (90 years) burned area datasets, climate and weather observations, and simulated fuel loads for Australian forests. Burned area in Australia’s forests shows a linear positive annual trend but an exponential increase during autumn and winter. The mean number of years since the last fire has decreased consecutively in each of the past four decades, while the frequency of forest megafire years (>1 Mha burned) has markedly increased since 2000. The increase in forest burned area is consistent with increasingly more dangerous fire weather conditions, increased risk factors associated with pyroconvection, including fire-generated thunderstorms, and increased ignitions from dry lightning, all associated to varying degrees with anthropogenic climate change. The degree to which wildfire activity in Australia is affected by climate change is not well quantified. Here, the authors show that the frequency of forest fires and the area burned have increased significantly over recent decades, mainly due to an increase in dangerous fire weather conditions through warmer temperature and circulation changes.

Pain is the major symptom of osteoarthritis (OA) and is an important factor in strategies to manage this disease. However, the current standard of care does not provide satisfactory pain relief for many patients. The pathophysiology of OA is complex, and its presentation as a clinical syndrome is associated with pathologies of multiple joint tissues. Inflammation is associated with both OA pain and disease outcome and is therefore a major treatment target for OA and OA pain. Unlike TNF inhibitors and IL-1 inhibitors, established drugs such as glucocorticoids and methotrexate can reduce OA pain. Although central nociceptive pathways contribute to OA pain, crosstalk between the immune system and nociceptive neurons is central to inflammatory pain; therefore, new therapies might target this crosstalk. Newly identified drug targets, including neurotrophins and the granulocyte–macrophage colony-stimulating factor (GM-CSF)–CC-chemokine ligand 17 (CCL17) chemokine axis, offer the hope of better results but require clinical validation.Pain management is presently the focus of osteoarthritis (OA) therapy, but traditional pain-relieving drugs such as NSAIDs and glucocorticoids have limited utility. In this Review, the next generation of OA analgesics and their potential mechanisms of action are discussed.

Key Points Colony-stimulating factors (CSFs) are pleiotropic, but each has its own unique biological role. All CSFs control myeloid cell numbers, but each has a level of specificity in regard to its target cells and effects. Preclinical and/or clinical data suggest that targeting granulocyte–macrophage CSF (GM-CSF), CSF1, granulocyte CSF (G-CSF) or IL-3 could be useful in the treatment of numerous autoimmune and/or inflammatory pathologies, including rheumatoid arthritis and multiple sclerosis. Further investigation of the potential of targeting CSFs in other pathologies is warranted. CSF targeting, as well as associated patient stratification, should be based on the specific CSF biology. There is not necessarily an inverse relationship between the effects of CSF blockade and administration of the protein itself on a particular pathology. The colony-stimulating factors (CSFs) have roles in inflammation and immunity, and are potential targets for diseases caused by aberrant immune activation, including rheumatoid arthritis and multiple sclerosis. Hamilton et al . describe how distinguishing attributes could be used to target individual CSFs for therapeutic use in immune and inflammatory conditions and the progress that has been made towards that goal. They also clarify misconceptions about targeting this class of molecules. Granulocyte–macrophage colony-stimulating factor (GM-CSF), macrophage colony-stimulating factor (M-CSF; also known as CSF1), granulocyte colony-stimulating factor (G-CSF) and interleukin-3 (IL-3) can each play a part in the host response to injury and infection, and there is burgeoning interest in targeting these CSFs in inflammatory and autoimmune disorders, as well as in cancer. For success in clinical medicine, therapeutic targeting will need to be delineated from current strategies. The individual CSFs have unique biological roles, suggesting that they could be used to target specific conditions. This Review compares the CSFs, with a focus on how they could be targeted, discusses the relevant clinical trial data and summarizes the potential clinical applications of targeting each CSF. Importantly, we discuss the novelty of CSF biology and attempt to clarify some of the surrounding misconceptions and issues that can affect therapeutic decisions.

Mark Daly and colleagues present a statistical framework to evaluate the role of de novo mutations in human disease by calibrating a model of de novo mutation rates at the individual gene level. The mutation probabilities defined by their model and list of constrained genes can be used to help identify genetic variants that have a significant role in disease. Spontaneously arising ( de novo ) mutations have an important role in medical genetics. For diseases with extensive locus heterogeneity, such as autism spectrum disorders (ASDs), the signal from de novo mutations is distributed across many genes, making it difficult to distinguish disease-relevant mutations from background variation. Here we provide a statistical framework for the analysis of excesses in de novo mutation per gene and gene set by calibrating a model of de novo mutation. We applied this framework to de novo mutations collected from 1,078 ASD family trios, and, whereas we affirmed a significant role for loss-of-function mutations, we found no excess of de novo loss-of-function mutations in cases with IQ above 100, suggesting that the role of de novo mutations in ASDs might reside in fundamental neurodevelopmental processes. We also used our model to identify ∼1,000 genes that are significantly lacking in functional coding variation in non-ASD samples and are enriched for de novo loss-of-function mutations identified in ASD cases.