Intracellular lipid binding proteins (iLBPs) play crucial roles in lipid transport and cellular metabolism across the animal kingdom. Recently, a fat-to-neuron axis was described in Caenorhabditis elegans, in which lysosomal activity in the fat liberates polyunsaturated fatty acids (PUFAs) that signal to neurons and extend lifespan with durable fecundity. In this study, we investigate the structure and binding mechanisms of a lifespan-extending lipid chaperone, lipid binding protein-3 (LBP-3), which shuttles dihomo-γ-linolenic (DGLA) acid from intestinal fat to neurons. We present the first high-resolution crystal structure of LBP-3, which reveals a classic iLBP fold with an unexpected and unique homodimeric arrangement via interstrand interactions that is incompatible with ligand binding. We identify key ionic interactions that mediate DGLA binding within the lipid binding pocket. Molecular dynamics simulations further elucidate LBP-3's preferential binding to DGLA due to its rotational freedom and access to favorable binding conformations compared to other 20-carbon PUFAs. We also propose that LBP-3 dimerization may be a unique regulatory mechanism for lipid chaperones.

The scaffolding function of receptor interacting protein kinase 1 (RIPK1) confers intrinsic and extrinsic resistance to immune checkpoint blockades (ICBs) and emerges as a promising target for improving cancer immunotherapies. To address the challenge posed by a poorly defined binding pocket within the intermediate domain of RIPK1, here we harness proteolysis targeting chimera (PROTAC) technology to develop a RIPK1 degrader, LD4172. LD4172 exhibits potent and selective RIPK1 degradation both in vitro and in vivo. Degradation of RIPK1 by LD4172 triggers immunogenic cell death, enhances tumor-infiltrating lymphocyte responses, and sensitizes tumors to anti-PD1 therapy in female C57BL/6J mice. This work reports a RIPK1 degrader that serves as a chemical probe for investigating the scaffolding functions of RIPK1 and as a potential therapeutic agent to enhance tumor responses to ICBs therapy.

Transient exposure to ketamine can trigger lasting changes in behavior and mood. We found that brief ketamine exposure causes long-term suppression of futility-induced passivity in larval zebrafish, reversing the "giving-up" response that normally occurs when swimming fails to cause forward movement. Whole-brain imaging revealed that ketamine hyperactivates the norepinephrine-astroglia circuit responsible for passivity. After ketamine washout, this circuit exhibits hyposensitivity to futility, leading to long-term increased perseverance. Pharmacological, chemogenetic, and optogenetic manipulations show that norepinephrine and astrocytes are necessary and sufficient for ketamine's long-term perseverance-enhancing aftereffects. In vivo calcium imaging revealed that astrocytes in adult mouse cortex are similarly activated during futility in the tail suspension test and that acute ketamine exposure also induces astrocyte hyperactivation. The cross-species conservation of ketamine's modulation of noradrenergic-astroglial circuits and evidence that plasticity in this pathway can alter the behavioral response to futility hold promise for identifying new strategies to treat affective disorders.

Cells counter accumulation of misfolded secretory proteins in the endoplasmic reticulum (ER) through activation of the Unfolded Protein Response (UPR). Small molecules termed chemical chaperones can promote protein folding to alleviate ER stress. The bile acid tauroursodeoxycholic acid (TUDCA), has been described as a chemical chaperone. While promising in models of protein folding diseases, TUDCA's mechanism of action remains unclear. Here, we found TUDCA can rescue growth of yeast treated with the ER stressor tunicamycin (Tm), even in the absence of a functional UPR. In contrast, TUDCA failed to rescue growth on other ER stressors. Nor could TUDCA attenuate chronic UPR associated with specific gene deletions or over-expression of a misfolded mutant secretory protein. Neither pretreatment with or delayed addition of TUDCA conferred protection against Tm. Importantly, attenuation of Tm-induced toxicity required TUDCA's critical micelle forming concentration, suggesting a mechanism where TUDCA directly sequesters drugs. Indeed, in several assays, TUDCA treated cells closely resembled cells treated with lower doses of Tm. In addition, we found TUDCA can inhibit dyes from labeling intracellular compartments. Thus, our study challenges the model of TUDCA as a chemical chaperone and suggests that TUDCA decreases drug bioavailability, allowing cells to adapt to ER stress.

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.