Two invasive hemipteran adelgids cause widespread damage to North American conifers. Adelges tsugae (the hemlock woolly adelgid) has decimated Tsuga canadensis and Tsuga caroliniana (the Eastern and Carolina hemlocks, respectively). A. tsugae was introduced from East Asia and reproduces parthenogenetically in North America, where it can kill trees rapidly. A. abietis, introduced from Europe, makes pineapple galls on several North American spruce species, and weakens trees, increasing their susceptibility to other stresses. Broad-spectrum insecticides that are often used to control adelgid populations can have off-target impacts on beneficial insects and the development of more selective chemical treatments could improve control methods and minimize ecological damage. Whole genome sequencing was performed on both species to aid in development of targeted pest control solutions and improve species conservation. The assembled A. tsugae and A. abietis genomes are 231.71 Mbp and 290.39 Mbp, respectively, each consisting of nine chromosomes and both genomes are over 96% complete based on BUSCO assessment. Genome annotation identified 11,424 and 14,118 protein-coding genes in A. tsugae and A. abietis, respectively. Comparative analysis across 29 Hemipteran species and 14 arthropod outgroups identified 31,666 putative gene families. Gene family expansions in A. abietis included ABC transporters and carboxypeptidases involved in carbohydrate metabolism, while both species showed contractions in core histone families and oxidoreductase pathways. Gene family expansions in A. tsugae highlighted families associated with the regulation of cell differentiation and development (survival motor protein, SMN; juvenile hormone acid methyltransferase JHAMT) as well as those that may be involved in the suppression of plant immunity (clip domain serine protease-D, CLIPD; Endoplasmic reticulum aminopeptidase 1, ERAP1). Among the analyzed gene families, Nicotinic acetylcholine receptors (nAChRs) maintained consistent copy numbers and structural features across species, a finding particularly relevant given their role as targets for current forestry management insecticides. Detailed phylogenetic analysis of nAChR subunits across adelgids and other ecologically important insects revealed remarkable conservation in both sequence composition and predicted structural features, providing crucial insights for the development of more selective pest control strategies.

Protein synthesis is central to life and requires the ribosome, which catalyzes the stepwise addition of amino acids to a polypeptide chain by undergoing a sequence of structural transformations. Here, we employed high-resolution template matching (HRTM) on cryoelectron microscopy (cryo-EM) images of directly cryofixed living cells to obtain a set of ribosomal configurations covering the entire elongation cycle, with each configuration occurring at its native abundance. HRTM's position and orientation precision and ability to detect small targets (∼300 kDa) made it possible to order these configurations along the reaction coordinate and to reconstruct molecular features of any configuration along the elongation cycle. Visualizing the cycle's structural dynamics by combining a sequence of >40 reconstructions into a 3D movie readily revealed component and ligand movements, some of them surprising, such as spring-like intramolecular motion, providing clues about the molecular mechanisms involved in some still mysterious steps during chain elongation.

Connectomics is a subfield of neuroscience that aims to map the brain’s intricate wiring diagram. Accurate neuron segmentation from microscopy volumes is essential for automating connectome reconstruction. However, current state-of-the-art algorithms use image-based convolutional neural networks that are limited to local neuron shape context. Thus, we introduce a new framework that reasons over global neuron shape with a novel point affinity transformer. Our framework embeds a (multi-)neuron point cloud into a fixed-length feature set from which we can decode any point pair affinities, enabling clustering neuron point clouds for automatic proofreading. We also show that the learned feature set can easily be mapped to a contrastive embedding space that enables neuron type classification using a simple KNN classifier. Our approach excels in two demanding connectomics tasks: proofreading segmentation errors and classifying neuron types. Evaluated on three benchmark datasets derived from state-of-the-art connectomes, our method outperforms point transformers, graph neural networks, and unsupervised clustering baselines.

The body of an animal influences how the nervous system produces behavior. Therefore, detailed modeling of the neural control of sensorimotor behavior requires a detailed model of the body. Here we contribute an anatomically-detailed biomechanical whole-body model of the fruit fly Drosophila melanogaster in the MuJoCo physics engine. Our model is general-purpose, enabling the simulation of diverse fly behaviors, both on land and in the air. We demonstrate the generality of our model by simulating realistic locomotion, both flight and walking. To support these behaviors, we have extended MuJoCo with phenomenological models of fluid forces and adhesion forces. Through data-driven end-to-end reinforcement learning, we demonstrate that these advances enable the training of neural network controllers capable of realistic locomotion along complex trajectories based on high-level steering control signals. We demonstrate the use of visual sensors and the re-use of a pre-trained general-purpose flight controller by training the model to perform visually guided flight tasks. Our project is an open-source platform for modeling neural control of sensorimotor behavior in an embodied context.Competing Interest StatementThe authors have declared no competing interest.

Animals are often bombarded with visual information and must prioritize specific visual features based on their current needs. The neuronal circuits that detect and relay visual features have been well studied. Much less is known about how an animal adjusts its visual attention as its goals or environmental conditions change. During social behaviours, flies need to focus on nearby flies. Here we study how the flow of visual information is altered when female Drosophila enter an aggressive state. From the connectome, we identify three state-dependent circuit motifs poised to modify the response of an aggressive female to fly-sized visual objects: convergence of excitatory inputs from neurons conveying select visual features and internal state; dendritic disinhibition of select visual feature detectors; and a switch that toggles between two visual feature detectors. Using cell-type-specific genetic tools, together with behavioural and neurophysiological analyses, we show that each of these circuit motifs is used during female aggression. We reveal that features of this same switch operate in male Drosophila during courtship pursuit, suggesting that disparate social behaviours may share circuit mechanisms. Our study provides a compelling example of using the connectome to infer circuit mechanisms that underlie dynamic processing of sensory signals.

Summary HYlight is a genetically encoded fluorescent biosensor that ratiometrically monitors fructose 1,6-bisphosphate (FBP), a key glycolytic metabolite. Given the role of glucose in liver cancer metabolism, we expressed HYlight in human liver cancer cells and primary mouse hepatocytes. Through in vitro, in silico, and in cellulo experiments, we showed HYlight’s ability to monitor FBP changes linked to glycolysis, not gluconeogenesis. HYlight’s affinity for FBP was ∼1 μM and stable within physiological pH range. HYlight demonstrated weak binding to dihydroxyacetone phosphate, and its ratiometric response was influenced by both ionic strength and phosphate. Therefore, simulating cytosolic conditions in vitro was necessary to establish a reliable correlation between HYlight’s cellular responses and FBP concentrations. FBP concentrations were found to be in the lower micromolar range, far lower than previous millimolar estimates. Altogether, this biosensor approach offers real-time monitoring of FBP concentrations at single-cell resolution, making it an invaluable tool for the understanding of cancer metabolism.

Significant technical challenges exist when measuring synaptic connections between neurons in living brain tissue. The patch clamping technique, when used to probe for synaptic connections, is manually laborious and time-consuming. To improve its efficiency, we pursued another approach: instead of retracting all patch clamping electrodes after each recording attempt, we cleaned just one of them and reused it to obtain another recording while maintaining the others. With one new patch clamp recording attempt, many new connections can be probed. By placing one pipette in front of the others in this way, one can 'walk' across the mouse brain slice, termed 'patch-walking.' We performed 136 patch clamp attempts for two pipettes, achieving 71 successful whole cell recordings (52.2%). Of these, we probed 29 pairs (i.e. 58 bidirectional probed connections) averaging 91 μm intersomatic distance, finding three connections. Patch-walking yields 80-92% more probed connections, for experiments with 10-100 cells than the traditional synaptic connection searching method.

Genetically encoded voltage indicators (GEVIs) allow optical recording of membrane potential from targeted cells in vivo. However, red GEVIs that are compatible with two-photon microscopy and that can be multiplexed in vivo with green reporters like GCaMP, are currently lacking. To address this gap, we explored diverse rhodopsin proteins as GEVIs and engineered a novel GEVI, 2Photron, based on a rhodopsin from the green algae Klebsormidium nitens. 2Photron, combined with two photon ultrafast local volume excitation (ULoVE), enabled multiplexed readout of spiking and subthreshold voltage simultaneously with GCaMP calcium signals in visual cortical neurons of awake, behaving mice. These recordings revealed the cell-specific relationship of spiking and subthreshold voltage dynamics with GCaMP responses, highlighting the challenges of extracting underlying spike trains from calcium imaging.

Target interception is a complex sensorimotor behavior which requires fine tuning of the sensory system and its strategic coordination with the motor system. Despite various theories about how interception is achieved, its neural implementation remains unknown. We have previously shown that hunting dragonflies employ a balance of reactive and predictive control to intercept prey, using sophisticated model driven predictions to account for expected prey and self-motion. Here we explore the neural substrate of this interception system by investigating a well-known class of target-selective descending neurons (TSDNs). These cells have long been speculated to underlie interception steering but have never been studied in a behaving dragonfly. We combined detailed neuroanatomy, high-precision kinematics data and state-of-the-art neural telemetry to measure TSDN activity during flight. We found that TSDNs are exquisitely tuned to prey angular size and speed at ethological distances, and that they synapse directly onto neck and wing motoneurons in an unusual manner. However, we found that TSDNs were only weakly active during flight and are thus unlikely to provide the primary steering signal. Instead, they appear to drive the foveating head movements that stabilize prey on the eye before and likely throughout the interception flight. We suggest the TSDN population implements the reactive portion of the interception steering control system, coordinating head and wing movements to compensate for unexpected prey motion.

Behavioral neuroscience faces two conflicting demands: long-duration recordings from large neural populations and unimpeded animal behavior. To meet this challenge we developed ONIX, an open-source data acquisition system with high data throughput (2 GB s) and low closed-loop latencies (<1 ms) that uses a 0.3-mm thin tether to minimize behavioral impact. Head position and rotation are tracked in three dimensions and used to drive active commutation without torque measurements. ONIX can acquire data from combinations of passive electrodes, Neuropixels probes, head-mounted microscopes, cameras, three-dimensional trackers and other data sources. We performed uninterrupted, long (~7 h) neural recordings in mice as they traversed complex three-dimensional terrain, and multiday sleep-tracking recordings (~55 h). ONIX enabled exploration with similar mobility as nonimplanted animals, in contrast to conventional tethered systems, which have restricted movement. By combining long recordings with full mobility, our technology will enable progress on questions that require high-quality neural recordings during ethologically grounded behaviors.