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Summmary Quantitative behavior analyses of spider movements — large and small — reveal repeated action sequences that define stages of web building.

The way genes contribute to behavior is complicated. Although there are some single genes with large contributions, most behavioral differences are due to small effects from many interacting genes. This makes it hard to identify the genes that cause behavioral differences. Mutagenesis screens in model organisms, selective breeding experiments in animals, comparisons between related populations with different behaviors, and genome-wide association studies in humans are promising and complementary approaches to understanding the heritable aspects of complex behaviors. To connect genes to behaviors requires measuring behavioral differences, locating correlated genetic changes, determining when, where, and how these candidate genes act, and designing causative confirmatory experiments. This area of research has implications from basic discovery science to human mental health.

In many behaviors such walking and swimming, animals need to coordinate their left and right limbs. In

The brain coordinates the movements that constitute behavior, but how descending neurons convey the myriad of commands required to activate the motor neurons of the limbs in the right order and combinations to produce those movements is not well understood. For anterior grooming behavior in the fly, we show that its component head sweeps and leg rubs can be initiated separately, or as a set, by different descending neurons. Head sweeps and leg rubs are mutually exclusive movements of the front legs that normally alternate, and we show that circuits in the ventral nerve cord as well as in the brain can resolve competing commands. Finally, the left and right legs must work together to remove debris. The coordination for leg rubs can be achieved by unilateral activation of a single descending neuron, while a similar manipulation of a different descending neuron decouples the legs to produce single-sided head sweeps. Taken together, these results demonstrate that distinct descending neurons orchestrate the complex alternation between the movements that make up anterior grooming.

Behavioral differences can be observed between species or populations (variation) or between individuals in a genetically similar population (variability). Here, we investigate genetic differences as a possible source of variation and variability in Drosophila grooming. Grooming confers survival and social benefits. Grooming features of five Drosophila species exposed to a dust irritant were analyzed. Aspects of grooming behavior, such as anterior to posterior progression, were conserved between and within species. However, significant differences in activity levels, proportion of time spent in different cleaning movements, and grooming syntax were identified between species. All species tested showed individual variability in the order and duration of action sequences. Genetic diversity was not found to correlate with grooming variability within a species: melanogaster flies bred to increase or decrease genetic heterogeneity exhibited similar variability in grooming syntax. Individual flies observed on consecutive days also showed grooming sequence variability. Standardization of sensory input using optogenetics reduced but did not eliminate this variability. In aggregate, these data suggest that sequence variability may be a conserved feature of grooming behavior itself. These results also demonstrate that large genetic differences result in distinguishable grooming phenotypes (variation), but that genetic heterogeneity within a population does not necessarily correspond to an increase in the range of grooming behavior (variability).

Behavioral differences can be observed between species or populations (variation) or between individuals in a genetically similar population (variability). Here, we investigate genetic differences as a possible source of variation and variability in Drosophila grooming. Drosophila grooming behavior confers survival and social benefits. Although the leg movements that constitute the grooming sequence are stereotyped, their order is not fixed. Grooming features of five drosophilid species exposed to a dust irritant were analyzed. Components of grooming behavior were conserved between and within species. However, significant differences in grooming syntax were identified, corresponding both to anterior and posterior grooming actions. Genetic heterogeneity was not found to be related to grooming variability, as melanogaster flies bred to increase genetic het-erogeneity did not exhibit increased variability in grooming syntax. Likewise, no relationship between decreased heterogeneity and variability was identified. Finally, individual melanogaster flies were observed on consecutive days to determine the degree of variability of grooming behavior within an individual over time. Individual flies were not found to possess strong, stable grooming traits over several recordings. Additionally, standardization of sensory input using optogenetics did not eliminate grooming variability. In aggregate, these data suggest the importance of sensory inputs and other factors such as life history in grooming variability. Significance Statement Broadly speaking, genes influence behavior, but genes also play a role in determining the natural range of behavioral variability. Here, we show that Drosophila species exhibit differences in grooming behavior both between and within species. In particular, we demonstrate that transitions between grooming actions differ significantly between drosophilid species and common melanogaster stock lines, suggesting that these actions are under partial genetic control. Within melanogaster, however, genotype had no observable effect on the range of grooming behavior. This work establishes similarities in grooming behavior between drosophilids while also highlighting important differences, providing targets for future explorations of genetic, sensory, and developmental contributions to behavior.

We describe the anatomy of all the primary motor neurons in the fly proboscis and characterize their contributions to its diverse reaching movements. Pairing this behavior with the wealth of Drosophila’s genetic tools offers the possibility to study motor control at single-neuron resolution, and soon throughout entire circuits. As an entry to these circuits, we provide detailed anatomy of proboscis motor neurons, muscles, and joints. We create a collection of fly strains to individually manipulate every proboscis muscle through control of its motor neurons, the first such collection for an appendage. We generate a model of the action of each proboscis joint, and find that only a small number of motor neurons are needed to produce proboscis reaching. Comprehensive control of each motor element in this numerically simple system paves the way for future study of both reflexive and flexible movements of this appendage.

SUMMARY Animals integrate information from different sensory modalities, body parts, and time points to inform behavioral choice, but the relevant sensory comparisons and the underlying neural circuits are still largely unknown. We use the grooming behavior of Drosophila melanogaster as a model to investigate the sensory comparisons that govern a motor sequence. Flies perform grooming movements spontaneously, but when covered with dust, they clean their bodies following an anterior-to-posterior sequence. After investigating different sensory modalities that could detect dust, we focus on mechanosensory bristle neurons, whose optogenetic activation induces a similar sequence. Computational modeling predicts that higher sensory input strength to the head will cause anterior grooming to occur first. We test this prediction using an optogenetic competition assay whereby two targeted light beams independently activate mechanosensory bristle neurons on different body parts. We find that the initial choice of grooming movement is determined by the ratio of sensory inputs to different body parts. In dust-covered flies, sensory inputs change as a result of successful cleaning movements. Simulations from our model suggest that this change results in sequence progression. One possibility is that flies perform frequent comparisons between anterior and posterior sensory inputs, and the changing ratios drive different behavior choices. Alternatively, flies may track the temporal change in sensory input to a given body part to measure cleaning effectiveness. The first hypothesis is supported by our optogenetic competition experiments: iterative spatial comparisons of sensory inputs between body parts is essential for organizing grooming movements in sequence., In Brief Zhang et al. find that Drosophila covered with dust compare sensory inputs from mechanosensory bristles on different body parts during grooming. The ratio of anterior:posterior sensory input and its dynamics, rather than the rate of dust removal from the anterior, drives the anterior-to-posterior grooming sequence.

Drosophila melanogaster is an established model for neuroscience research with relevance in biology and medicine. Until recently, research on the Drosophila brain was hindered by the lack of a complete and uniform nomenclature. Recognizing this, Ito et al. (2014) produced an authoritative nomenclature for the adult insect brain, using Drosophila as the reference. Here, we extend this nomenclature to the adult thoracic and abdominal neuromeres, the ventral nerve cord (VNC), to provide an anatomical description of this major component of the Drosophila nervous system. The VNC is the locus for the reception and integration of sensory information and involved in generating most of the locomotor actions that underlie fly behaviors. The aim is to create a nomenclature, definitions, and spatial boundaries for the Drosophila VNC that are consistent with other insects. The work establishes an anatomical framework that provides a powerful tool for analyzing the functional organization of the VNC.

The fruit fly, Drosophila melanogaster, is an established and powerful model system for neuroscience research with wide relevance in biology and medicine. Until recently, research on the Drosophila brain was hindered by the lack of a complete and uniform nomenclature. Recognising this problem, the Insect Brain Name Working Group produced an authoritative hierarchical nomenclature system for the adult insect brain, using Drosophila melanogaster as the reference framework, with other taxa considered to ensure greater consistency and expandability (Ito et al., 2014). Here, we extend this nomenclature system to the sub-gnathal regions of the adult Drosophila nervous system, thus providing a systematic anatomical description of the ventral nervous system (VNS). This portion of the nervous system includes the thoracic and abdominal neuromeres that were not included in the original work and contains the motor circuits that play essential roles in most fly behaviours.

The ability to image and manipulate specific cell populations in Drosophila enables the investigation of how neural circuits develop and coordinate appropriate motor behaviors. Gal4 lines give genetic access to many types of neurons, but the expression patterns of these reagents are often complex. Here, we present the generation and expression patterns of LexA lines based on the vesicular neurotransmitter transporters and Hox transcription factors. Intersections between these LexA lines and existing Gal4 collections provide a strategy for rationally subdividing complex expression patterns based on neurotransmitter or segmental identity.

Goal-directed animal behaviors are typically composed of sequences of motor actions whose order and timing are critical for a successful outcome. Although numerous theoretical models for sequential action generation have been proposed, few have been supported by the identification of control neurons sufficient to elicit a sequence. Here, we identify a pair of descending neurons that coordinate a stereotyped sequence of engagement actions during Drosophila melanogaster male courtship behavior. These actions are initiated sequentially but persist cumulatively, a feature not explained by existing models of sequential behaviors. We find evidence consistent with a ramp-to-threshold mechanism, in which increasing neuronal activity elicits each action independently at successively higher activity thresholds.

In most animals, the brain controls the body via a set of descending neurons (DNs) that traverse the neck. DN activity activates, maintains or modulates locomotion and other behaviors. Individual DNs have been well-studied in species from insects to primates, but little is known about overall connectivity patterns across the DN population. We systematically investigated DN anatomy in Drosophila melanogaster and created over 100 transgenic lines targeting individual cell types. We identified roughly half of all Drosophila DNs and comprehensively map connectivity between sensory and motor neuropils in the brain and nerve cord, respectively. We find the nerve cord is a layered system of neuropils reflecting the fly’s capability for two largely independent means of locomotion -- walking and flight -- using distinct sets of appendages. Our results reveal the basic functional map of descending pathways in flies and provide tools for systematic interrogation of neural circuits.

Understanding how activity patterns in specific neural circuits coordinate an animal’s behavior remains a key area of neuroscience research. Genetic tools and a brain of tractable complexity make Drosophila a premier model organism for these studies. Here, we review the wealth of reagents available to map and manipulate neuronal activity with light.

This special issue of the Journal of Neurogenetics honors the career of Professor Barry Ganetzky on the occasion of his retirement from the Laboratory of Genetics at the University of Wisconsin–Mad...

Understanding how activity patterns in specific neural circuits coordinate an animal’s behavior remains a key area of neuroscience research. Genetic tools and a brain of tractable complexity make Drosophila a premier model organism for these studies. Here, we review the wealth of reagents available to map and manipulate neuronal activity with light.

Assigning behavioral functions to neural structures has long been a central goal in neuroscience and is a necessary first step toward a circuit-level understanding of how the brain generates behavior. Here, we map the neural substrates of locomotion and social behaviors for Drosophila melanogaster using automated machine-vision and machine-learning techniques. From videos of 400,000 flies, we quantified the behavioral effects of activating 2,204 genetically targeted populations of neurons. We combined a novel quantification of anatomy with our behavioral analysis to create brain-behavior correlation maps, which are shared as browsable web pages and interactive software. Based on these maps, we generated hypotheses of regions of the brain causally related to sensory processing, locomotor control, courtship, aggression, and sleep. Our maps directly specify genetic tools to target these regions, which we used to identify a small population of neurons with a role in the control of walking.

Animals can perform complex and purposeful behaviors by executing simpler movements in flexible sequences. It is particularly challenging to analyze behavior sequences when they are highly variable, as is the case in language production, certain types of birdsong and, as in our experiments, flies grooming. High sequence variability necessitates rigorous quantification of large amounts of data to identify organizational principles and temporal structure of such behavior. To cope with large amounts of data, and minimize human effort and subjective bias, researchers often use automatic behavior recognition software. Our standard grooming assay involves coating flies in dust and videotaping them as they groom to remove it. The flies move freely and so perform the same movements in various orientations. As the dust is removed, their appearance changes. These conditions make it difficult to rely on precise body alignment and anatomical landmarks such as eyes or legs and thus present challenges to existing behavior classification software. Human observers use speed, location, and shape of the movements as the diagnostic features of particular grooming actions. We applied this intuition to design a new automatic behavior recognition system (ABRS) based on spatiotemporal features in the video data, heavily weighted for temporal dynamics and invariant to the animal's position and orientation in the scene. We use these spatiotemporal features in two steps of supervised classification that reflect two time-scales at which the behavior is structured. As a proof of principle, we show results from quantification and analysis of a large data set of stimulus-induced fly grooming behaviors that would have been difficult to assess in a smaller dataset of human-annotated ethograms. While we developed and validated this approach to analyze fly grooming behavior, we propose that the strategy of combining alignment-invariant features and multi-timescale analysis may be generally useful for movement-based classification of behavior from video data.

Research in the fruit fly Drosophila melanogaster has led to insights in neural development, axon guidance, ion channel function, synaptic transmission, learning and memory, diurnal rhythmicity, and neural disease that have had broad implications for neuroscience. Drosophila is currently the eukaryotic model organism that permits the most sophisticated in vivo manipulations to address the function of neurons and neuronally expressed genes. Here, we summarize many of the techniques that help assess the role of specific neurons by labeling, removing, or altering their activity. We also survey genetic manipulations to identify and characterize neural genes by mutation, overexpression, and protein labeling. Here, we attempt to acquaint the reader with available options and contexts to apply these methods.

Analyzing Drosophila melanogaster neural expression patterns in thousands of three-dimensional image stacks of individual brains requires registering them into a canonical framework based on a fiducial reference of neuropil morphology. Given a target brain labeled with predefined landmarks, the BrainAligner program automatically finds the corresponding landmarks in a subject brain and maps it to the coordinate system of the target brain via a deformable warp. Using a neuropil marker (the antibody nc82) as a reference of the brain morphology and a target brain that is itself a statistical average of data for 295 brains, we achieved a registration accuracy of 2 μm on average, permitting assessment of stereotypy, potential connectivity and functional mapping of the adult fruit fly brain. We used BrainAligner to generate an image pattern atlas of 2954 registered brains containing 470 different expression patterns that cover all the major compartments of the fly brain.

We developed a multicolor neuron labeling technique in Drosophila melanogaster that combines the power to specifically target different neural populations with the label diversity provided by stochastic color choice. This adaptation of vertebrate Brainbow uses recombination to select one of three epitope-tagged proteins detectable by immunofluorescence. Two copies of this construct yield six bright, separable colors. We used Drosophila Brainbow to study the innervation patterns of multiple antennal lobe projection neuron lineages in the same preparation and to observe the relative trajectories of individual aminergic neurons. Nerve bundles, and even individual neurites hundreds of micrometers long, can be followed with definitive color labeling. We traced motor neurons in the subesophageal ganglion and correlated them to neuromuscular junctions to identify their specific proboscis muscle targets. The ability to independently visualize multiple lineage or neuron projections in the same preparation greatly advances the goal of mapping how neurons connect into circuits.

The V3D system provides three-dimensional (3D) visualization of gigabyte-sized microscopy image stacks in real time on current laptops and desktops. V3D streamlines the online analysis, measurement and proofreading of complicated image patterns by combining ergonomic functions for selecting a location in an image directly in 3D space and for displaying biological measurements, such as from fluorescent probes, using the overlaid surface objects. V3D runs on all major computer platforms and can be enhanced by software plug-ins to address specific biological problems. To demonstrate this extensibility, we built a V3D-based application, V3D-Neuron, to reconstruct complex 3D neuronal structures from high-resolution brain images. V3D-Neuron can precisely digitize the morphology of a single neuron in a fruitfly brain in minutes, with about a 17-fold improvement in reliability and tenfold savings in time compared with other neuron reconstruction tools. Using V3D-Neuron, we demonstrate the feasibility of building a 3D digital atlas of neurite tracts in the fruitfly brain.

Automatic alignment (registration) of 3D images of adult fruit fly brains is often influenced by the significant displacement of the relative locations of the two optic lobes (OLs) and the center brain (CB). In one of our ongoing efforts to produce a better image alignment pipeline of adult fruit fly brains, we consider separating CB and OLs and align them independently. This paper reports our automatic method to segregate CB and OLs, in particular under conditions where the signal to noise ratio (SNR) is low, the variation of the image intensity is big, and the relative displacement of OLs and CB is substantial. We design an algorithm to find a minimum-cost 3D surface in a 3D image stack to best separate an OL (of one side, either left or right) from CB. This surface is defined as an aggregation of the respective minimum-cost curves detected in each individual 2D image slice. Each curve is defined by a list of control points that best segregate OL and CB. To obtain the locations of these control points, we derive an energy function that includes an image energy term defined by local pixel intensities and two internal energy terms that constrain the curve's smoothness and length. Gradient descent method is used to optimize this energy function. To improve both the speed and robustness of the method, for each stack, the locations of optimized control points in a slice are taken as the initialization prior for the next slice. We have tested this approach on simulated and real 3D fly brain image stacks and demonstrated that this method can reasonably segregate OLs from CBs despite the aforementioned difficulties.

Nat. Methods 8, 253–259 (2011); published online 6 February 2011; corrected online 16 February 2011; corrected after print 30 August 2012; corrected after print 3 August 2015 In the version of this article initially published, the sequence reported for the dBrainbow construct was incorrect. The bluefluorescent protein was reported as EBFP2; it is mTFP1.

© 2015 Elsevier Ltd. Hunger is a complex motivational state that drives multiple behaviors. The sensation of hunger is caused by an imbalance between energy intake and expenditure. One immediate response to hunger is increased food consumption. Hunger also modulates behaviors related to food seeking such as increased locomotion and enhanced sensory sensitivity in both insects [1-5] and vertebrates [6, 7]. In addition, hunger can promote the expression of food-associated memory [8, 9]. Although progress is being made [10], how hunger is represented in the brain and how it coordinates these behavioral responses is not fully understood in any system. Here, we use Drosophila melanogaster to identify neurons encoding hunger. We found a small group of neurons that, when activated, induced a fed fly to eat as though it were starved, suggesting that these neurons are downstream of the metabolic regulation of hunger. Artificially activating these neurons also promotes appetitive memory performance in sated flies, indicating that these neurons are not simply feeding command neurons but likely play a more general role in encoding hunger. We determined that the neurons relevant for the feeding effect are serotonergic and project broadly within the brain, suggesting a possible mechanism for how various responses to hunger are coordinated. These findings extend ourunderstanding of the neural circuitry that drives feeding and enable future exploration of how state influences neural activity within this circuit. Albin etal. have identified a small set of neurons that can induce sated flies to feed as though starved, as well as provide the hunger signal required for appetitive memory performance. The serotonergic subset of these neurons is responsible for conveying the sensation of hunger.

Animals perform many stereotyped movements, but how nervous systems are organized for controlling specific movements remains unclear. Here we use anatomical, optogenetic, behavioral, and physiological techniques to identify a circuit in Drosophila melanogaster that can elicit stereotyped leg movements that groom the antennae. Mechanosensory chordotonal neurons detect displacements of the antennae and excite three different classes of functionally connected interneurons, which include two classes of brain interneurons and different parallel descending neurons. This multilayered circuit is organized such that neurons within each layer are sufficient to specifically elicit antennal grooming. However, we find differences in the durations of antennal grooming elicited by neurons in the different layers, suggesting that the circuit is organized to both command antennal grooming and control its duration. As similar features underlie stimulus-induced movements in other animals, we infer the possibility of a common circuit organization for movement control that can be dissected in Drosophila. DOI: http://dx.doi.org/10.7554/eLife.08758.001, eLife digest Many movements that animals perform regularly—including walking and grooming—consist of stereotyped sequences of muscle contractions. For example, a dog may scratch its side in response to a fleabite or because it is itchy. But how does the nervous system trigger such specific movements from among the repertoire of different movements that the animal could perform? It also remains unclear how such movements can be produced in a reliable, yet flexible manner. Hampel et al. have now described the neural circuit that triggers and controls the stereotyped leg movements that the fruit fly Drosophila uses to groom its antennae. Such grooming movements are stereotyped yet have certain degrees of flexibility, which makes them ideal to study the neural circuits that underlie specific movements. Grooming further lends itself to this kind of investigation because it can be triggered by irritating the surface of the fly's body. There is also an extensive genetic toolkit that can be used to manipulate and observe the fruit fly's nervous system in detail. Hampel et al. first identified sensory neurons in the flies' antennae that were needed to elicit grooming in response to irritating displacements of the antennae. Once these neurons were found, techniques—including those that allow specific neurons in the fly's brain to be precisely controlled—were then used to find other neurons that participate in the grooming process. This approach highlighted three groups of interneurons: two in the brain and one in the fly's equivalent of the spinal cord. Together these layers of sensory neurons and interneurons formed a circuit that triggered grooming whenever the antennae were disturbed. Notably, activating different sensory or interneurons triggered bouts of antennal grooming of differing durations. This shows that the same neural circuit can both produce highly specific movements and modify the movements to provide flexibility. Neural circuits with similar features have been observed previously to induce other animal behaviors, for example, swimming in leeches. This suggests that this organization may be common in circuits that elicit movements. Additional experiments are now needed to validate whether similar circuits underlie other stereotyped movements in fruit flies and other animals. DOI: http://dx.doi.org/10.7554/eLife.08758.002

Natural events present multiple types of sensory cues, each detected by a specialized sensory modality. Combining information from several modalities is essential for the selection of appropriate actions. Key to understanding multimodal computations is determining the structural patterns of multimodal convergence and how these patterns contribute to behaviour. Modalities could converge early, late or at multiple levels in the sensory processing hierarchy. Here we show that combining mechanosensory and nociceptive cues synergistically enhances the selection of the fastest mode of escape locomotion in Drosophila larvae. In an electron microscopy volume that spans the entire insect nervous system, we reconstructed the multisensory circuit supporting the synergy, spanning multiple levels of the sensory processing hierarchy. The wiring diagram revealed a complex multilevel multimodal convergence architecture. Using behavioural and physiological studies, we identified functionally connected circuit nodes that trigger the fastest locomotor mode, and others that facilitate it, and we provide evidence that multiple levels of multimodal integration contribute to escape mode selection. We propose that the multilevel multimodal convergence architecture may be a general feature of multisensory circuits enabling complex input-output functions and selective tuning to ecologically relevant combinations of cues.

Transgenesis in numerous eukaryotes has been facilitated by the use of site-specific integrases to stably insert transgenes at predefined genomic positions (landing sites). However, the utility of integrase-mediated transgenesis in any system is constrained by the limited number and variable expression properties of available landing sites. By exploiting the nonstandard recombination activity exhibited by a phiC31 integrase mutant, we developed a rapid and inexpensive method for isolating landing sites that exhibit desired expression properties. Additionally, we devised a simple technique for constructing arrays of transgenes at a single landing site, thereby extending the utility of previously characterized landing sites. Using the fruit fly Drosophila melanogaster, we demonstrate the feasibility of these approaches by isolating new landing sites optimized to express transgenes in the nervous system and by building fluorescent reporter arrays at several landing sites. Because these strategies require the activity of only a single exogenous protein, we anticipate that they will be portable to species such as nonmodel organisms, in which genetic manipulation is more challenging, expediting the development of genetic resources in these systems.

WOS: 000345484000033 PubMed ID: 25362474 Synaptic connectivity and molecular composition provide a blueprint for information processing in neural circuits. Detailed structural analysis of neural circuits requires nanometer resolution, which can be obtained with serial-section electron microscopy. However, this technique remains challenging for reconstructing molecularly defined synapses. We used a genetically encoded synaptic marker for electron microscopy (GESEM) based on intra-vesicular generation of electron-dense labeling in axonal boutons. This approach allowed the identification of synapses from Cre recombinase expressing or GAL4-expressing neurons in the mouse and fly with excellent preservation of ultrastructure. We applied this tool to visualize long-range connectivity of AGRP and POMC neurons in the mouse, two molecularly defined hypothalamic populations that are important for feeding behavior. Combining selective ultrastructural reconstruction of neuropil with functional and viral circuit mapping, we characterized some basic features of circuit organization for axon projections of these cell types. Our findings demonstrate that GESEM labeling enables long-range connectomics with molecularly defined cell types. Howard Hughes Medical Institute We thank A. Wardlaw for mouse breeding and genotyping, K. Morris for HSV stereotaxic injections, A. Hu and M. Copeland for histology, and L. Lo, D. Anderson and R. Gong with advice on HSV129 anterograde tracing. This research was funded by the Howard Hughes Medical Institute. The HSV129 Delta TK-TT anterograde trans-synaptic tracer virus was provided by the Center for Neuroanatomy with Neurotropic Viruses (P40RR018604).

Motor sequences are formed through the serial execution of different movements, but how nervous systems implement this process remains largely unknown. We determined the organizational principles governing how dirty fruit flies groom their bodies with sequential movements. Using genetically targeted activation of neural subsets, we drove distinct motor programs that clean individual body parts. This enabled competition experiments revealing that the motor programs are organized into a suppression hierarchy; motor programs that occur first suppress those that occur later. Cleaning one body part reduces the sensory drive to its motor program, which relieves suppression of the next movement, allowing the grooming sequence to progress down the hierarchy. A model featuring independently evoked cleaning movements activated in parallel, but selected serially through hierarchical suppression, was successful in reproducing the grooming sequence. This provides the first example of an innate motor sequence implemented by the prevailing model for generating human action sequences. DOI: http://dx.doi.org/10.7554/eLife.02951.001, eLife digest Anyone who has ever lived with a cat is familiar with its grooming behavior. This innate behavior follows a particular sequence as the cat methodically cleans its body parts one-by-one. Many animals also have grooming habits, even insects such as fruit flies. The fact that grooming sequences are seen across such different species suggests that this behavior is important for survival. Nevertheless, how the brain organizes grooming sequences, or other behaviors that involve a sequence of tasks, is not well understood. Fruit flies make a good model for studying grooming behavior for a couple of reasons. First, they are fastidious cleaners. When coated with dust they will faithfully carry out a series of cleaning tasks to clean each body part. Second, there are many genetic tools and techniques that researchers can use to manipulate the fruit flies' behaviors. One technique allows specific brain cells to be targeted and activated to trigger particular behaviors. Seeds et al. used these sophisticated techniques, computer modeling, and behavioral observations to uncover how the brains of fruit flies orchestrate a grooming sequence. Dust-covered flies follow a predictable sequence of cleaning tasks: beginning by using their front legs to clean their eyes, they then clean their antennae and head. This likely helps to protect their sensory organs. Next, they move on to the abdomen, possibly to ensure that dust doesn't interfere with their ability to breathe. Wings and thorax follow last. Periodically, the flies stop to rub their legs together to remove any accumulated dust before resuming the cleaning sequence. Seeds et al. activated different sets of brain cells one-by-one to see if they could trigger a particular grooming task and found that individual cleaning tasks could be triggered, in the absence of dust, by stimulating a specific group of brain cells. This suggests each cleaning task is a discrete behavior controlled by a subset of cells. Then Seeds et al. tried to stimulate more than one cleaning behavior at a time; they discovered that wing-cleaning suppressed thorax-cleaning, abdomen-cleaning suppressed both of these, and head-cleaning suppressed all the others. This suggests that a ‘hierarchy’ exists in the brain that exactly matches the sequence that flies normally follow as they clean their body parts. By learning more about how the brain coordinates grooming sequences, the findings of Seeds et al. may also provide insights into other behaviors that involve a sequence of tasks, such as nest building in animals or typing in humans. Following on from this work, one of the next challenges will be to see if such behaviors also use a ‘suppression hierarchy’ to ensure that individual tasks are carried out in the right order. DOI: http://dx.doi.org/10.7554/eLife.02951.002

Slit is secreted by midline glia in Drosophila and functions as a short-range repellent to control midline crossing. Although most Slit stays near the midline, some diffuses laterally, functioning as a long-range chemorepellent. Here we show that a combinatorial code of Robo receptors controls lateral position in the CNS by responding to this presumptive Slit gradient. Medial axons express only Robo, intermediate axons express Robo3 and Robo, while lateral axons express Robo2, Robo3, and Robo. Removal of robo2 or robo3 causes lateral axons to extend medially; ectopic expression of Robo2 or Robo3 on medial axons drives them laterally. Precise topography of longitudinal pathways appears to be controlled by a combination of long-range guidance (the Robo code determining region) and short-range guidance (discrete local cues determining specific location within a region).

We established a collection of 7,000 transgenic lines of Drosophila melanogaster. Expression of GAL4 ineach line is controlled by a different, defined fragment of genomic DNA that serves as a transcriptional enhancer. We used confocal microscopy ofdissected nervous systems to determine the expression patterns driven by each fragment in the adult brain and ventral nerve cord. We present image data on 6,650 lines. Using both manual and machine-assisted annotation, we describe the expression patterns in the most useful lines. We illustrate the utility of these data for identifying novel neuronal cell types, revealing brain asymmetry, and describing the nature and extent of neuronal shape stereotypy. The GAL4 lines allow expression of exogenous genes in distinct, small subsets of the adult nervous system. The set of DNA fragments, each driving a documented expression pattern, will facilitate the generation of additional constructs for manipulating neuronal function.