Huntington's disease (HD) is a neurodegenerative disorder caused by a CAG trinucleotide repeat expansion in the first exon of the huntingtin gene. The huntingtin protein (Htt) is ubiquitously expressed and localized in several organelles, including endosomes, where it plays an essential role in intracellular trafficking. Presymptomatic HD is associated with a failure in energy metabolism and oxidative stress. Ascorbic acid is a potent antioxidant that plays a key role in modulating neuronal metabolism and is highly concentrated in the brain. During synaptic activity, neurons take up ascorbic acid released by glial cells; however, this process is disrupted in HD. In this study, we aim to elucidate the molecular and cellular mechanisms underlying this dysfunction. Using an electrophysiological approach in presymptomatic YAC128 HD slices, we observed decreased ascorbic acid flux from astrocytes to neurons, which altered neuronal metabolic substrate preferences. Ascorbic acid efflux and recycling were also decreased in cultured astrocytes from YAC128 HD mice. We confirmed our findings using GFAP-HD160Q, an HD mice model expressing mutant N-terminal Htt mainly in astrocytes. For the first time, we demonstrated that ascorbic acid is released from astrocytes via extracellular vesicles (EVs). Decreased number of particles and exosomal markers were observed in EV fractions from cultured YAC128 HD astrocytes and Htt-KD cells. We observed reduced number of multivesicular bodies (MVBs) in YAC128 HD striatum via electron microscopy, suggesting mutant Htt alters MVB biogenesis. EVs containing ascorbic acid effectively reduced reactive oxygen species, whereas "free" ascorbic acid played a role in modulating neuronal metabolic substrate preferences. These findings suggest that the early redox imbalance observed in HD arises from a reduced release of ascorbic acid-containing EVs by astrocytes. Meanwhile, a decrease in "free" ascorbic acid likely contributes to presymptomatic metabolic impairment.

Cellular heterogeneity within complex tissues and organs is essential to coordinate biological processes across biological scales. The effect of local cues and tissue microenvironments on cell heterogeneity has been mainly studied at the transcriptional level. However, it is within the subcellular scale - the organelles - that lays the machinery to conduct most metabolic reactions and maintain cells alive, ensuring proper tissue function. How changes in subcellular organization under different microenvironments define the functional diversity of cells within organs remains largely unexplored. Here we determine how organelles adapt to different microenvironments using the mouse liver as model system, in combination with computational approaches and machine-learning. To understand organelle adaptation in response to changing nutritional conditions, we analyzed 3D fluorescent microscopy volumes of liver samples labeled to simultaneously visualize mitochondria, peroxisomes, and lipid droplets from mice subjected to different diets: a control diet, a high-fat diet, and a control diet plus fasting. A Cellpose based pipeline was implemented for cell and organelle segmentation, which allowed us to measure 100 different organelle metrics and helped us define subcellular architectures in liver samples at the single cell level. Our results showed that hepatocytes display distinct subcellular architectures within different regions of the liver-close to the central vein, in the middle region, and near the portal vein- and across the various diet groups, thus reflecting their adaptation to specific nutritional inputs. Principal component analysis and clustering of hepatocytes based on organelle signatures revealed 12 different hepatocyte categories within the different experimental groups, highlighting a reduction in hepatocyte heterogeneity under nutritional perturbations. Finally, using single cell organelle signatures exclusively, we generated machine learning models that were able to predict with high accuracy different hepatocyte categories, diet groups, and the stages of MASLD. Our results demonstrate how organelle signatures can be used as hallmarks to define hepatocyte heterogeneity and their adaptation to different nutritional conditions. In the future, our strategy, which combines subcellular resolution imaging of liver volumes and machine learning, could help establish protocols to better define and predict liver disease progression.

We take up the challenge of developing an international network with capacity to survey the world's scientists on an ongoing basis, providing rich datasets regarding the opinions of scientists and scientific sub-communities, both at a time and also over time. The novel methodology employed sees local coordinators, at each institution in the network, sending survey invitation emails internally to scientists at their home institution. The emails link to a '10 second survey', where the participant is presented with a single statement to consider, and a standard five-point Likert scale. In June 2023, a group of 30 philosophers and social scientists invited 20,085 scientists across 30 institutions in 12 countries to participate, gathering 6,807 responses to the statement Science has put it beyond reasonable doubt that COVID-19 is caused by a virus. The study demonstrates that it is possible to establish a global network to quickly ascertain scientific opinion on a large international scale, with high response rate, low opt-out rate, and in a way that allows for significant (perhaps indefinite) repeatability. Measuring scientific opinion in this new way would be a valuable complement to currently available approaches, potentially informing policy decisions and public understanding across diverse fields.

Psychological stress and its sequelae pose a major challenge to public health. Immune activation is conventionally thought to aggravate stress-related mental diseases such as anxiety disorders and depression. Here, we sought to identify potentially beneficial consequences of immune activation in response to stress. We showed that stress led to increased interleukin (IL)-22 production in the intestine as a result of stress-induced gut leakage. IL-22 was both necessary and sufficient to attenuate stress-induced anxiety behaviors in mice. More specifically, IL-22 gained access to the septal area of the brain and directly suppressed neuron activation. Furthermore, human patients with clinical depression displayed reduced IL-22 levels, and exogenous IL-22 treatment ameliorated depressive-like behavior elicited by chronic stress in mice. Our study thus identifies a gut-brain axis in response to stress, whereby IL-22 reduces neuronal activation and concomitant anxiety behavior, suggesting that early immune activation can provide protection against psychological stress.

We address the problem of explaining the decision process of deep neural network classifiers on images, which is of particular importance in biomedical datasets where class-relevant differences are not always obvious to a human observer. Our proposed solution, termed quantitative attribution with counterfactuals (QuAC), generates visual explanations that highlight class-relevant differences by attributing the classifier decision to changes of visual features in small parts of an image. To that end, we train a separate network to generate counterfactual images (i.e., to translate images between different classes). We then find the most important differences using novel discriminative attribution methods. Crucially, QuAC allows scoring of the attribution and thus provides a measure to quantify and compare the fidelity of a visual explanation. We demonstrate the suitability and limitations of QuAC on two datasets: (1) a synthetic dataset with known class differences, representing different levels of protein aggregation in cells and (2) an electron microscopy dataset of D. melanogaster synapses with different neurotransmitters, where QuAC reveals so far unknown visual differences. We further discuss how QuAC can be used to interrogate mispredictions to shed light on unexpected inter-class similarities and intra-class differences.

Phase separation is an important mechanism to generate certain biomolecular condensates and organize the cell interior. Condensate formation and function remain incompletely understood due to difficulties in visualizing the condensate interior at high resolution. Here we analyzed the structure of biochemically reconstituted chromatin condensates through cryo-electron tomography. We found that traditional blotting methods of sample preparation were inadequate, and high-pressure freezing plus focused ion beam milling was essential to maintain condensate integrity. To identify densely packed molecules within the condensate, we integrated deep learning-based segmentation with novel context-aware template matching. Our approaches were developed on chromatin condensates, and were also effective on condensed regions of in situ native chromatin. Using these methods, we determined the average structure of nucleosomes to 6.1 and 12 Å resolution in reconstituted and native systems, respectively, and found that nucleosomes have a nearly random orientation distribution in both cases. Our methods should be applicable to diverse biochemically reconstituted biomolecular condensates and to some condensates in cells.

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.