Predicting visual function by interpreting a neuronal wiring diagram.

As connectomics advances, it will become commonplace to know far more about the structure of a nervous system than about its function. The starting point for many investigations will become neuronal wiring diagrams, which will be interpreted to make theoretical predictions about function. Here I demonstrate this emerging approach with the Drosophila optic lobe, analysing its structure to predict that three Dm3 (refs. 1-4) and three TmY (refs. 2,4) cell types are part of a circuit that serves the function of form vision. Receptive fields are predicted from connectivity, and suggest that the cell types encode the local orientation of a visual stimulus. Extraclassical5,6 receptive fields are also predicted, with implications for robust orientation tuning7, position invariance8,9 and completion of noisy or illusory contours10,11. The TmY types synapse onto neurons that project from the optic lobe to the central brain12,13, which are conjectured to compute conjunctions and disjunctions of oriented features. My predictions can be tested through neurophysiology, which would constrain the parameters and biophysical mechanisms in neural network models of fly vision14.

Connectomic reconstruction predicts visual features used for navigation.

Many animals use visual information to navigate1-4, but how such information is encoded and integrated by the navigation system remains incompletely understood. In Drosophila melanogaster, EPG neurons in the central complex compute the heading direction5 by integrating visual input from ER neurons6-12, which are part of the anterior visual pathway (AVP)10,13-16. Here we densely reconstruct all neurons in the AVP using electron-microscopy data17. The AVP comprises four neuropils, sequentially linked by three major classes of neurons: MeTu neurons10,14,15, which connect the medulla in the optic lobe to the small unit of the anterior optic tubercle (AOTUsu) in the central brain; TuBu neurons9,16, which connect the AOTUsu to the bulb neuropil; and ER neurons6-12, which connect the bulb to the EPG neurons. On the basis of morphologies, connectivity between neural classes and the locations of synapses, we identify distinct information channels that originate from four types of MeTu neurons, and we further divide these into ten subtypes according to the presynaptic connections in the medulla and the postsynaptic connections in the AOTUsu. Using the connectivity of the entire AVP and the dendritic fields of the MeTu neurons in the optic lobes, we infer potential visual features and the visual area from which any ER neuron receives input. We confirm some of these predictions physiologically. These results provide a strong foundation for understanding how distinct sensory features can be extracted and transformed across multiple processing stages to construct higher-order cognitive representations.

The fly connectome reveals a path to the effectome.

A goal of neuroscience is to obtain a causal model of the nervous system. The recently reported whole-brain fly connectome1-3 specifies the synaptic paths by which neurons can affect each other, but not how strongly they do affect each other in vivo. To overcome this limitation, we introduce a combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the 'effectome'. Specifically, we propose an estimator for a linear dynamical model of the fly brain that uses stochastic optogenetic perturbation data to estimate causal effects and the connectome as a prior to greatly improve estimation efficiency. We validate our estimator in connectome-based linear simulations and show that it recovers a linear approximation to the nonlinear dynamics of more biophysically realistic simulations. We then analyse the connectome to propose circuits that dominate the dynamics of the fly nervous system. We discover that the dominant circuits involve only relatively small populations of neurons-thus, neuron-level imaging, stimulation and identification are feasible. This approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, we provide evidence that fly whole-brain dynamics are generated by a large collection of small circuits that operate largely independently of each other. This implies that a causal model of a brain can be feasibly obtained in the fly.

Network statistics of the whole-brain connectome of Drosophila.

Brains comprise complex networks of neurons and connections, similar to the nodes and edges of artificial networks. Network analysis applied to the wiring diagrams of brains can offer insights into how they support computations and regulate the flow of information underlying perception and behaviour. The completion of the first whole-brain connectome of an adult fly, containing over 130,000 neurons and millions of synaptic connections1-3, offers an opportunity to analyse the statistical properties and topological features of a complete brain. Here we computed the prevalence of two- and three-node motifs, examined their strengths, related this information to both neurotransmitter composition and cell type annotations4,5, and compared these metrics with wiring diagrams of other animals. We found that the network of the fly brain displays rich-club organization, with a large population (30% of the connectome) of highly connected neurons. We identified subsets of rich-club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex ( https://codex.flywire.ai ) and should serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

Neuronal parts list and wiring diagram for a visual system.

A catalogue of neuronal cell types has often been called a 'parts list' of the brain1, and regarded as a prerequisite for understanding brain function2,3. In the optic lobe of Drosophila, rules of connectivity between cell types have already proven to be essential for understanding fly vision4,5. Here we analyse the fly connectome to complete the list of cell types intrinsic to the optic lobe, as well as the rules governing their connectivity. Most new cell types contain 10 to 100 cells, and integrate information over medium distances in the visual field. Some existing type families (Tm, Li, and LPi)6-10 at least double in number of types. A new serpentine medulla (Sm) interneuron family contains more types than any other. Three families of cross-neuropil types are revealed. The consistency of types is demonstrated by analysing the distances in high-dimensional feature space, and is further validated by algorithms that select small subsets of discriminative features. We use connectivity to hypothesize about the functional roles of cell types in motion, object and colour vision. Connectivity with 'boundary types' that straddle the optic lobe and central brain is also quantified. We showcase the advantages of connectomic cell typing: complete and unbiased sampling, a rich array of features based on connectivity and reduction of the connectome to a substantially simpler wiring diagram of cell types, with immediate relevance for brain function and development.

Digest: Evaluating sexual conflict in the genome.

Audet et al. (2024) investigate the genomic basis of sexual conflict in response to sexually discordant size selection in Drosophila melanogaster. They report interesting morphological changes in sexual dimorphism and multivariate allometry. Although they do not find any genetic variants that individually show a strong effect on these traits, as expected for a polygenic trait such as body size, they do find a region on chromosome 3L showing signs of sexually discordant selection (i.e., conflict between males and females). This study highlights potential genomic regions involved in sexual conflict, offering insights into sex-specific adaptations.

Changes in the wing shape and size in fruit flies exposed to micro and nanoplastics.

Geometric morphometrics analysis (GMA) is a well-known technique to identify minute changes in Drosophila wings. This study aimed to determine potential changes in Drosophila wings shape and size after exposure to polystyrene nanoplastics (NPs) (50 nm) and microplastics (MPs) (1 µm). Flies were exposed from eggs to pupal eclosion and analyzed using GMA. Results revealed a difference in shape and size between male and female wings, as expected, due to sexual dimorphism. Therefore, wings were analyzed by sex. Wings of MPs and NPs treated females were elongated compared to controls and had a constriction of the wing joint. Additionally, MPs treated female flies had the most dissimilar shape compared to controls. In male flies, NPs flies had smaller wings compared to MPs and control flies. Compared to control, NPs wings of males were shrunken at the joint and in the entire proximal region of the wing. However, male MPs wings had a narrower anal region and were slightly elongated. These results reveal that wing shape and size can change in a different way based on the sex of the flies and size of plastic particles that larvae interacted with. All the changes in the wings occurred only within the normally allowed wing variation and treatment with NPs/MPs did not cause development of the aberrant phenotypes. Results can pave the way for further understanding of how MPs and NPs can alter phenotypes of flies.

Drosophila immune cells transport oxygen through PPO2 protein phase transition.

Insect respiration has long been thought to be solely dependent on an elaborate tracheal system without assistance from the circulatory system or immune cells1,2. Here we describe that Drosophila crystal cells-myeloid-like immune cells called haemocytes-control respiration by oxygenating Prophenoloxidase 2 (PPO2) proteins. Crystal cells direct the movement of haemocytes between the trachea of the larval body wall and the circulation to collect oxygen. Aided by copper and a neutral pH, oxygen is trapped in the crystalline structures of PPO2 in crystal cells. Conversely, PPO2 crystals can be dissolved when carbonic anhydrase lowers the intracellular pH and then reassembled into crystals in cellulo by adhering to the trachea. Physiologically, larvae lacking crystal cells or PPO2, or those expressing a copper-binding mutant of PPO2, display hypoxic responses under normoxic conditions and are susceptible to hypoxia. These hypoxic phenotypes can be rescued by hyperoxia, expression of arthropod haemocyanin or prevention of larval burrowing activity to expose their respiratory organs. Thus, we propose that insect immune cells collaborate with the tracheal system to reserve and transport oxygen through the phase transition of PPO2 crystals, facilitating internal oxygen homeostasis in a process that is comparable to vertebrate respiration.

Connectomic reconstruction of a female Drosophila ventral nerve cord.

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 lines2 and X-ray holographic nanotomography3. 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.

Synaptic architecture of leg and wing premotor control networks in Drosophila.

Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles1. MN activity is coordinated by complex premotor networks that facilitate the contribution of individual muscles to many different behaviours2-6. Here we use connectomics7 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.

Evolution of connectivity architecture in the Drosophila mushroom body.

Brain evolution has primarily been studied at the macroscopic level by comparing the relative size of homologous brain centers between species. How neuronal circuits change at the cellular level over evolutionary time remains largely unanswered. Here, using a phylogenetically informed framework, we compare the olfactory circuits of three closely related Drosophila species that differ in their chemical ecology: the generalists Drosophila melanogaster and Drosophila simulans and Drosophila sechellia that specializes on ripe noni fruit. We examine a central part of the olfactory circuit that, to our knowledge, has not been investigated in these species-the connections between projection neurons and the Kenyon cells of the mushroom body-and identify species-specific connectivity patterns. We found that neurons encoding food odors connect more frequently with Kenyon cells, giving rise to species-specific biases in connectivity. These species-specific connectivity differences reflect two distinct neuronal phenotypes: in the number of projection neurons or in the number of presynaptic boutons formed by individual projection neurons. Finally, behavioral analyses suggest that such increased connectivity enhances learning performance in an associative task. Our study shows how fine-grained aspects of connectivity architecture in an associative brain center can change during evolution to reflect the chemical ecology of a species.

Sexually discordant selection is associated with trait-specific morphological changes and a complex genomic response.

Sexes often have differing fitness optima, potentially generating intra-locus sexual conflict, as each sex bears a genetic "load" of alleles beneficial to the other sex. One strategy to evaluate conflict in the genome is to artificially select populations discordantly against established sexual dimorphism (SD), reintroducing attenuated conflict. We investigate a long-term artificial selection experiment reversing sexual size dimorphism in Drosophila melanogaster during ~350 generations of sexually discordant selection. We explore morphological and genomic changes to identify loci under selection between the sexes in discordantly and concordantly size-selected treatments. Despite substantial changes to overall size, concordant selection maintained ancestral SD. However, discordant selection altered size dimorphism in a trait-specific manner. We observe multiple possible soft selective sweeps in the genome, with size-related genes showing signs of selection. Patterns of genomic differentiation between the sexes within lineages identified potential sites maintained by sexual conflict. One discordant selected lineage shows a pattern of elevated genomic differentiation between males and females on chromosome 3L, consistent with the maintenance of sexual conflict. Our results suggest visible signs of conflict and differentially segregating alleles between the sexes due to discordant selection.

The sex of organ geometry.

Organs have a distinctive yet often overlooked spatial arrangement in the body1-5. We propose that there is a logic to the shape of an organ and its proximity to its neighbours. Here, by using volumetric scans of many Drosophila melanogaster flies, we develop methods to quantify three-dimensional features of organ shape, position and interindividual variability. We find that both the shapes of organs and their relative arrangement are consistent yet differ between the sexes, and identify unexpected interorgan adjacencies and left-right organ asymmetries. Focusing on the intestine, which traverses the entire body, we investigate how sex differences in three-dimensional organ geometry arise. The configuration of the adult intestine is only partially determined by physical constraints imposed by adjacent organs; its sex-specific shape is actively maintained by mechanochemical crosstalk between gut muscles and vascular-like trachea. Indeed, sex-biased expression of a muscle-derived fibroblast growth factor-like ligand renders trachea sexually dimorphic. In turn, tracheal branches hold gut loops together into a male or female shape, with physiological consequences. Interorgan geometry represents a previously unrecognized level of biological complexity which might enable or confine communication across organs and could help explain sex or species differences in organ function.

Machine learning reveals the control mechanics of an insect wing hinge.

Insects constitute the most species-rich radiation of metazoa, a success that is due to the evolution of active flight. Unlike pterosaurs, birds and bats, the wings of insects did not evolve from legs1, but are novel structures that are attached to the body via a biomechanically complex hinge that transforms tiny, high-frequency oscillations of specialized power muscles into the sweeping back-and-forth motion of the wings2. The hinge consists of a system of tiny, hardened structures called sclerites that are interconnected to one another via flexible joints and regulated by the activity of specialized control muscles. Here we imaged the activity of these muscles in a fly using a genetically encoded calcium indicator, while simultaneously tracking the three-dimensional motion of the wings with high-speed cameras. Using machine learning, we created a convolutional neural network3 that accurately predicts wing motion from the activity of the steering muscles, and an encoder-decoder4 that predicts the role of the individual sclerites on wing motion. By replaying patterns of wing motion on a dynamically scaled robotic fly, we quantified the effects of steering muscle activity on aerodynamic forces. A physics-based simulation incorporating our hinge model generates flight manoeuvres that are remarkably similar to those of free-flying flies. This integrative, multi-disciplinary approach reveals the mechanical control logic of the insect wing hinge, arguably among the most sophisticated and evolutionarily important skeletal structures in the natural world.

Motor neurons generate pose-targeted movements via proprioceptive sculpting.

Motor neurons are the final common pathway1 through which the brain controls movement of the body, forming the basic elements from which all movement is composed. Yet how a single motor neuron contributes to control during natural movement remains unclear. Here we anatomically and functionally characterize the individual roles of the motor neurons that control head movement in the fly, Drosophila melanogaster. Counterintuitively, we find that activity in a single motor neuron rotates the head in different directions, depending on the starting posture of the head, such that the head converges towards a pose determined by the identity of the stimulated motor neuron. A feedback model predicts that this convergent behaviour results from motor neuron drive interacting with proprioceptive feedback. We identify and genetically2 suppress a single class of proprioceptive neuron3 that changes the motor neuron-induced convergence as predicted by the feedback model. These data suggest a framework for how the brain controls movements: instead of directly generating movement in a given direction by activating a fixed set of motor neurons, the brain controls movements by adding bias to a continuing proprioceptive-motor loop.

Colorimetric Synchronization of Drosophila Larvae.

The rapid succession of events during development poses an inherent challenge to achieve precise synchronization required for rigorous, quantitative phenotypic and genotypic analyses in multicellular model organisms. Drosophila melanogaster is an indispensable model for studying the development and function of higher order organisms due to extensive genome homology, tractability, and its relatively short lifespan. Presently, nine Nobel prizes serve as a testament to the utility of this elegant model system. Ongoing advancements in genetic and molecular tools allow for the underlying mechanisms of human disease to be investigated in Drosophila. However, the absence of a method to precisely age-match tissues during larval development prevents further capitalization of this powerful model organism. Drosophila spends nearly half of its life cycle progressing through three morphologically distinct larval instar stages, during which the imaginal discs, precursors of mature adult external structures (e.g., eyes, legs, wings), grow and develop distinct cell fates. Other tissues, such as the central nervous system, undergo massive morphological changes during larval development. While these three larval stages and subsequent pupal stages have historically been identified based on the number of hours post egg-laying under standard laboratory conditions, a reproducible, efficient, and inexpensive method is required to accurately age-match larvae within the third instar. The third instar stage is of particular interest, as this developmental stage spans a 48-hr window during which larval tissues switch from proliferative to differentiation programs. Moreover, some genetic manipulations can lead to developmental delays, further compounding the need for precise age-matching between control and experimental samples. This article provides a protocol optimized for synchronous staging of Drosophila third instar larvae by colorimetric characterization and is useful for age-matching a variety of tissues for numerous downstream applications. We also provide a brief discussion of the technical challenges associated with successful application of this protocol. © 2023 Wiley Periodicals LLC. Basic Protocol: Synchronization of third instar Drosophila larvae.

A rise-to-threshold process for a relative-value decision.

Whereas progress has been made in the identification of neural signals related to rapid, cued decisions1-3, less is known about how brains guide and terminate more ethologically relevant decisions in which an animal's own behaviour governs the options experienced over minutes4-6. Drosophila search for many seconds to minutes for egg-laying sites with high relative value7,8 and have neurons, called oviDNs, whose activity fulfills necessity and sufficiency criteria for initiating the egg-deposition motor programme9. Here we show that oviDNs express a calcium signal that (1) dips when an egg is internally prepared (ovulated), (2) drifts up and down over seconds to minutes-in a manner influenced by the relative value of substrates-as a fly determines whether to lay an egg and (3) reaches a consistent peak level just before the abdomen bend for egg deposition. This signal is apparent in the cell bodies of oviDNs in the brain and it probably reflects a behaviourally relevant rise-to-threshold process in the ventral nerve cord, where the synaptic terminals of oviDNs are located and where their output can influence behaviour. We provide perturbational evidence that the egg-deposition motor programme is initiated once this process hits a threshold and that subthreshold variation in this process regulates the time spent considering options and, ultimately, the choice taken. Finally, we identify a small recurrent circuit that feeds into oviDNs and show that activity in each of its constituent cell types is required for laying an egg. These results argue that a rise-to-threshold process regulates a relative-value, self-paced decision and provide initial insight into the underlying circuit mechanism for building this process.

Drosophila class-I myosins that can impact left-right asymmetry have distinct ATPase kinetics.

Myosin-1D (myo1D) is important for Drosophila left-right asymmetry, and its effects are modulated by myosin-1C (myo1C). De novo expression of these myosins in nonchiral Drosophila tissues promotes cell and tissue chirality, with handedness depending on the paralog expressed. Remarkably, the identity of the motor domain determines the direction of organ chirality, rather than the regulatory or tail domains. Myo1D, but not myo1C, propels actin filaments in leftward circles in in vitro experiments, but it is not known if this property contributes to establishing cell and organ chirality. To further explore if there are differences in the mechanochemistry of these motors, we determined the ATPase mechanisms of myo1C and myo1D. We found that myo1D has a 12.5-fold higher actin-activated steady-state ATPase rate, and transient kinetic experiments revealed myo1D has an 8-fold higher MgADP release rate compared to myo1C. Actin-activated phosphate release is rate limiting for myo1C, whereas MgADP release is the rate-limiting step for myo1D. Notably, both myosins have among the tightest MgADP affinities measured for any myosin. Consistent with ATPase kinetics, myo1D propels actin filaments at higher speeds compared to myo1C in in vitro gliding assays. Finally, we tested the ability of both paralogs to transport 50 nm unilamellar vesicles along immobilized actin filaments and found robust transport by myo1D and actin binding but no transport by myo1C. Our findings support a model where myo1C is a slow transporter with long-lived actin attachments, whereas myo1D has kinetic properties associated with a transport motor.

A phosphate-sensing organelle regulates phosphate and tissue homeostasis.

Inorganic phosphate (Pi) is one of the essential molecules for life. However, little is known about intracellular Pi metabolism and signalling in animal tissues1. Following the observation that chronic Pi starvation causes hyperproliferation in the digestive epithelium of Drosophila melanogaster, we determined that Pi starvation triggers the downregulation of the Pi transporter PXo. In line with Pi starvation, PXo deficiency caused midgut hyperproliferation. Interestingly, immunostaining and ultrastructural analyses showed that PXo specifically marks non-canonical multilamellar organelles (PXo bodies). Further, by Pi imaging with a Förster resonance energy transfer (FRET)-based Pi sensor2, we found that PXo restricts cytosolic Pi levels. PXo bodies require PXo for biogenesis and undergo degradation following Pi starvation. Proteomic and lipidomic characterization of PXo bodies unveiled their distinct feature as an intracellular Pi reserve. Therefore, Pi starvation triggers PXo downregulation and PXo body degradation as a compensatory mechanism to increase cytosolic Pi. Finally, we identified connector of kinase to AP-1 (Cka), a component of the STRIPAK complex and JNK signalling3, as the mediator of PXo knockdown- or Pi starvation-induced hyperproliferation. Altogether, our study uncovers PXo bodies as a critical regulator of cytosolic Pi levels and identifies a Pi-dependent PXo-Cka-JNK signalling cascade controlling tissue homeostasis.