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One of the many goals of neuroscience is to understand how the brain encodes and transforms sensory information into behavior. These animal behaviors can be studied at the level of multi-limb poses or through the focused analysis of individual body parts. Techniques for tracking animal pose, such as DeepLabCut and SLEAP, enable detailed studies of large-scale multi-limb behaviors but show reduced accuracy when used for single-keypoint tracking, where insufficient spatial context leads to increased drift and instability in tracking (Arent I, Schmidt FP, Botsch M et al. Marker-less motion capture of insect locomotion with deep neural networks pre-trained on synthetic videos. Frontiers in Behavioral Neuroscience. Vol. 15. 2021. Tang G, Han Y, Sun X, et al. Anti-drift pose tracker (ADPT), a transformer-based network for robust animal pose estimation cross-species. eLife. Vol. 13. 2025). More general techniques, such as Faster Region-based Convolutional Neural Network (Faster R-CNN) and You Only Look Once (YOLO), have also been used to track location-based behaviors such as center-of-mass position and velocity. However, behaviors localized to a single body structure, such as the pharyngeal pumping (i.e., feeding) in the microscopic roundworm Caenorhabditis elegans (C. elegans), are particularly sensitive to noise from moving non-target body parts. This limitation cannot be resolved by simply adding more training data, as doing so often leads to overfitting rather than improved robustness, and instead requires additional processing beyond existing object tracking packages. To address these challenges, we present a fast, automated method that reliably measures pumping in freely moving C. elegans by combining a state-of-the-art object detector (Faster R-CNN) with a tunable noise filter in a technique we call PumpKin. To validate its performance, we demonstrate both its speed (average of 0.4 seconds/frame) and its robust estimation capabilities through application to eight different experimental conditions that encompass both satiety and genetically-driven changes to feeding. PumpKin accurately estimates average pumping rates under eight different experimental conditions, which are positively correlated with the estimates of two expert annotators. Furthermore, PumpKin provides reliable estimates of the instantaneous pumping rate dynamics, achieving an average overlap that exceeds the human-human agreement measured via leave-one-out analysis. Applying PumpKin to conditions differing in satiety revealed a shared basal pumping rate of 0.5 Hz across all worm groups recorded off food, regardless of genetic background or satiety state. Together, these findings highlight PumpKin's ability to accurately isolate and estimate the motion of a single body part during locomotion. Although we present results specific to C. elegans, we anticipate that PumpKin will generalize to behaviors localized to a single body structure in other systems.

Enteropathogenic Escherichia coli (EPEC) is a major bacterial enteropathogen causing infectious diarrhea among children in developing countries. Here, we found that EPEC induced isolated Ca2+ responses in epithelial cells, triggered by extracellular ATP (eATP). These responses were dependent on type III secretion (T3S) and down-regulated by the bacterial secreted protease EspC, consistent with eATP released by the T3S translocon pore-forming activity in host membranes. By performing high-speed Ca2+ imaging, we uncovered that at the onset of infection, low eATP levels triggered Ca2+-responses involving the whole cell but showing small amplitude and fast kinetics usually associated with local Ca2+ responses. The findings, supported by theoretical modeling, evoke a conceptual shift whereby low amounts of inositol 1, 4, 5-trisphosphate (IP3) induced by low eATP levels and subsequent moderate Ca2+ release enable the fast coordination of IP3 receptor cluster activation throughout the cell. Importantly, these yet undescribed coordinated fast responses occurred over prolonged time periods and defined a cell state with dampened activation of the pro-inflammatory transcriptional activator NF-kB associated with a decrease in its Ca2+-dependent O-linked β-N-acetylglucosamine modification.

Transcription is a fundamental cellular process and the first step of gene expression. In human cells, it depends on the binding to chromatin of various proteins, including RNA polymerases and numerous transcription factors (TFs). Observations indicate that these proteins tend to form macromolecular clusters, known as transcription factories, whose morphology and composition are still debated. While some microscopy experiments have revealed the presence of specialised factories, composed of similar TFs transcribing families of related genes, sequencing experiments suggest instead that mixed clusters may be prevalent, as a panoply of different TFs binds promiscuously to the same chromatin region. The mechanisms underlying the formation of specialised or mixed factories remain elusive. With the aim of finding such mechanisms, here we develop a chromatin polymer model mimicking the chromatin binding-unbinding dynamics of different types of complexes of TFs. Surprisingly, both specialised (i.e. demixed) and mixed clusters spontaneously emerge, and which of the two types forms depends mainly on cluster size. The mechanism promoting mixing is the presence of non-specific interactions between chromatin and proteins, which become increasingly important as clusters become larger. This result, that we observe both in simple polymer models and more realistic ones for human chromosomes, reconciles the apparently contrasting experimental results obtained. Additionally, we show how the introduction of different types of TFs strongly affects the emergence of transcriptional networks, providing a pathway to investigate transcriptional changes following gene editing or naturally occurring mutations.

This study investigates which visual information enables humans to recognize facial identity across different viewpoints, a key unresolved question in vision science. Participants completed an identity recognition task using faces rotated across a range of yaw angles and filtered to retain specific orientation ranges of visual information. Regardless of viewpoint, human performance consistently relied on horizontal facial information. To understand why, we used model observers to assess the identity information physically available in the images. A view-selective model, which matched identities within the same viewpoint, indicated that diagnostic identity cues shift from predominantly horizontal in frontal views to more vertical in profile views. In contrast, a view-tolerant model, which matched identities across different viewpoints, revealed that horizontal information provides the most stable and reliable identity cues across views. Furthermore, horizontal facial information best predicted the average appearance of a face across viewpoints, supporting its role in forming stable identity representations. These findings suggest that view-tolerant face representations are acquired through exposure to the stable statistical properties of faces primarily conveyed by horizontal information. By specifying the spatial information underlying recognition across viewpoints, the study offers valuable empirical constraints for the development of theoretical and computational models of face recognition.

Effective decision making in dynamic environments requires flexible evidence accumulation. Although models often express this flexibility as a property of the accumulator, its implementation in the brain may involve adaptive mechanisms operating at other stages of the decision process. We examined two such mechanisms: (1) stimulus-specific sensory adaptation at the level of evidence encoding, and (2) arousal-related neuromodulation, which could, in principle, affect both evidence encoding and accumulation. We measured single-unit activity in the middle temporal (MT) area and pupil-linked arousal while monkeys performed a modified random-dot motion direction-discrimination task in which an adapting stimulus with varied temporal stability preceded a behaviorally relevant test stimulus. The monkeys' decisions reflected adaptive evidence accumulation that depended on temporal-context stability and corresponded to context-dependent changes in both stimulus-specific sensory adaptation in MT and task-evoked pupil responses. However, adaptation and pupil adjustments were not related to each other. Together, these findings suggest that multiple mechanisms contribute to flexible, context-dependent evidence accumulation, including changes in sensory adaptation that shape evidence encoding and changes in arousal that may shape the accumulation process itself.

A new diffusion MRI approach offers a glimpse of the anomalies of cellular architecture underlying basal ganglia degeneration in Huntington's disease.

The quantitative analysis of tissue deformation at cellular resolution remains an important challenge in mammalian organogenesis. Here, we developed a new computational workflow to extract regional and temporal patterns of tissue deformation, and applied it to a collection of live microscopy datasets from mouse cardiogenesis. We devised a method to track tissue deformation directly from time-lapse raw images and experimentally validated the method by comparison with actual cell tracks. We then used a machine-learning approach to temporally and spatially align different specimens and reconstruct a single statistical model of tissue motion, deducing maps of strain, anisotropy, and tissue growth. We also implemented a virtual fate mapping tool that allows tracking any initial position in the cardiac primordium onto the linear heart tube (HT). Our study reveals predominant local cellular coherence during the deformation of the cardiac tissue, whereas strong compartmentalization of tissue deformation patterns transforms the bilateral cardiac primordium into a 3D longitudinal HT. At the future outer curvature of the primitive tube, the ventricular chamber forms by expansion of the tissue in a hemi-barrel shape with two harnessing belts: one that constrains tissue expansion at the arterial pole and one that constrains the expansion at the venous pole. Our study provides a new approach to understanding heart morphogenesis and proposes a new model of primitive HT formation.

In most animals, a small number of descending neurons (DNs) connect the brain to circuits and motor neurons (MNs) in the nerve cord. To understand how brain signals generate behavior, it is critical to understand the organization of the neural pathways linking DNs to MNs. In companion papers, we introduced a densely reconstructed connectome of the Drosophila Male Adult Nerve Cord (MANC; Takemura et al., 2024), including cell types and developmental lineages (Marin et al., 2024), which provides complete connectivity of the ventral nerve cord (VNC) at synaptic resolution. Here, we present a first look at the organization of the networks connecting DNs to MNs. We first proofread and curated all DNs and MNs, then systematically matched their morphology to light microscopy data. We report both broad organizational patterns of the entire network and fine-scale analysis of selected circuits of interest. We discover that direct DN-MN connections are infrequent and identify neuron communities putatively linked to control of different motor systems, including walking, flight steering and power generation, and coordinated action of wings and legs. Our analyses generate hypotheses for future functional experiments and empowers others to investigate these and other circuits of the VNC in richer mechanistic detail.

Snakebite globally claims more than 100,000 lives per year and results in morbidity for 400,000 survivors. Current treatment uses antibody-based antivenoms which are constrained by their efficacy, safety, and cost. In this study we evaluated the efficacy of previously described repurposed drugs against viperid snakes of the medically important Bothrops genus. Despite variable toxin representation and bioactivity across this central and south American genus, we found that the lead inhibitors targeting metalloproteinases (marimastat and DMPS) and phospholipases (varespladib) demonstrated pan-species neutralisation in enzymatic assays, whilst nafamostat (serine protease inhibitor) had variable activity. The metalloproteinase inhibitors protected against the procoagulant and haemorrhagic effects of several venoms in phenotypic assays. Collectively these findings demonstrate that repurposed drugs may be of great value as early interventions for the treatment of bothropic envenoming in the Neotropics and thus provide a strong rationale for their progression into future preclinical and clinical evaluation for snakebite indication. Snakebites are a major health problem, particularly in rural parts of tropical countries. Every year, millions of people are bitten by venomous snakes, causing more than 100,000 deaths and leaving many others with permanent disabilities. The World Health Organization classifies snakebite as a neglected tropical disease because, despite its devastating impact, it has received relatively little attention and investment. The only approved treatment for snakebite is antivenom. Antivenom is made by stimulating animals to produce antibodies against snake venom and must be given intravenously in a hospital. While it can be lifesaving, it has important limitations: it is often only effective against certain snake species, can cause serious side effects, and is frequently unavailable where snakebites occur. In remote regions, such as parts of the Amazon, people may have to travel five hours or more before reaching medical care. Earlier treatment could greatly improve survival and reduce permanent injuries. Researchers are therefore investigating new treatments that could be given soon after a bite, before a patient reaches hospital. One promising approach is to repurpose existing small molecule drugs that block the harmful toxins found in snake venom. Three drugs – varespladib, marimastat and DMPS – have already shown promise because they can block two major groups of venom toxins responsible for much of the damage caused by snakebites. Clare et al. wanted to further evaluate the suitability of these drugs, together with another drug called nafamostat, against venom from seven species of Bothrops snakes. Bothrops snakes are responsible for most serious snakebites in Latin America, and their venoms contain a mixture of toxins that damage tissues, disrupt blood clotting and cause severe haemorrhage. The researchers carried out a series of laboratory experiments to measure how well the drugs blocked different venom effects, including toxin activity, blood clotting problems and haemorrhage. Although the venoms varied between snake species, the drugs targeting two major toxin groups consistently worked across all seven species in laboratory tests. In contrast, nafamostat showed more variable effects. The experiments also showed that marimastat and DMPS, which target enzymes responsible for tissue damage and haemorrhage, provided the strongest overall protection. The drugs targeting the other toxin groups also showed promise but require further development. Overall, the findings of Clare et al. suggest that repurposed drugs could become valuable early treatments for Bothrops snakebites in Latin America. Because these medicines can be taken by mouth, they could potentially be given in the community soon after a bite, buying valuable time before patients reach a hospital for antivenom treatment. The study also highlights the need to develop additional drugs that target other important venom toxins, helping pave the way for more effective treatments for snakebite in the future.

Diffuse large B-cell lymphoma (DLBCL) is a common aggressive form of non-Hodgkin lymphoma. Tetraspanin CD37 is highly expressed on mature B cells and being studied as a therapeutic target for NHL, including DLBCL. DuoHexaBody-CD37 is a biparatopic antibody with an E430G hexamerization-enhancing mutation targeting two non-overlapping CD37 epitopes shown to promote complement-dependent cytotoxicity. However, the impact of DuoHexaBody-CD37 on direct cytotoxic signaling has not yet been studied. Here, we demonstrate that DuoHexaBody-CD37 induces direct cytotoxicity in DLBCL-derived tumor cell lines independent of the subtype. DuoHexaBody-CD37 induced significant CD37 clustering and was retained at the cell surface in contrast to rituximab, which was internalized. Unbiased screening identified the modulation of 26 (phospho)proteins upon DuoHexaBody-CD37 treatment of primary B cells or DLBCL cells. Whereas DLBCL cells predominantly upregulated p-SHP1(Y564) upon DuoHexaBody-CD37 treatment, primary B cells showed significantly increased p-AKT(S473) and MAPK signaling which is linked to cell survival. Studies using CD37-mutants identified the N-terminus to be involved in DuoHexaBody-CD37-induced signaling. Finally, DuoHexaBody-CD37 treatment inhibited cytokine pro-survival signaling in DLBCL cells. These findings provide novel insights into the signaling functions of CD37 upon DuoHexaBody-CD37 treatment, and open up opportunities for developing CD37-targeted immunotherapy in combination with small molecule inhibitors to maximize tumor cell death.

Color discrimination thresholds-the smallest detectable color differences-provide a benchmark for models of color vision, enable quantitative evaluation of eye diseases, and inform the design of display technologies. Despite their importance, a comprehensive characterization of these thresholds has long been considered intractable due to the psychophysical curse of dimensionality. Here, we address this challenge using a novel semiparametric Wishart process psychophysical model (WPPM), which leverages the feature that the internal noise limiting color discrimination varies smoothly across stimulus space. The model was fit to data collected with a nonparametric adaptive trial-placement procedure, enabling efficient stimulus selection. Together, through the combination of adaptive trial placement and post hoc WPPM fitting, we achieved a comprehensive characterization of color discrimination in the isoluminant plane with only ∼6000 trials per participant (N = 8). Once fit, the WPPM allows readouts of discrimination performance for any stimulus pair. We validated these readouts against 25 probe psychometric functions, measured with an additional 6000 trials per participant held out from model fitting. In conclusion, our study provides a foundational dataset for color vision, and our approach generalizes beyond color to any domain in which the internal noise limiting performance varies smoothly across stimulus space, offering a powerful and efficient method for comprehensively characterizing various perceptual discrimination thresholds.

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Nucleic acid aptamers hold promise for clinical applications, yet understanding their molecular binding mechanisms to target proteins, and efficiently optimizing their binding affinities, remain challenging. Here, we present CAAMO (Computer-Aided Aptamer Modeling and Optimization), which integrates in silico aptamer design with experimental validation to accelerate the development of aptamer-based RNA therapeutics. Starting from the sequence information of a reported RNA aptamer, Ta, for the SARS-CoV-2 spike protein, our CAAMO method first determines its binding mode with the spike protein's receptor binding domain (RBD) through a multi-strategy computational approach. We then optimize its binding affinity via structure-based rational design. Among the six designed candidates, five were experimentally verified and exhibited enhanced binding affinities compared to the original Ta sequence. Furthermore, we directly compared the binding properties of the RNA aptamers to neutralizing antibodies and found that the designed aptamer TaG34C demonstrated a comparable binding affinity to the RBD compared to the representative neutralizing antibodies analyzed in this study. This highlights its potential as an alternative to existing COVID-19 antibodies. Our work provides a robust approach for the efficient design of a relatively large number of high-affinity aptamers with complicated topologies. This approach paves the way for the development of aptamer-based RNA diagnostics and therapeutics.

Amyloid-forming peptides are increasingly recognized as dynamic regulators at the host-pathogen interface, yet how environmental factors control their assembly and activity remains poorly understood. Here, RNA acts as a concentration-dependent regulator of two sequence-related α-helical peptides with fundamentally different assembly behaviors: the cross-α amyloid-forming Staphylococcus aureus virulence factor PSMα3 and the non-amyloidogenic human host-defense peptide LL-37. RNA drives PSMα3 through distinct assembly states, from liquid-like condensates to fibrillar polymorphs, while preserving cytotoxic and antimicrobial activity over time. In contrast, RNA attenuates LL-37 cytotoxicity toward host cells while maintaining antibacterial activity, consistent with a host-protective immunomodulatory effect. Together with the opposing effects of epigallocatechin gallate, which redirects both peptides into amorphous assemblies, these findings support a mechanistic model in which biological activity is governed by supramolecular architecture, assembly trajectory, and dynamics rather than by monomer abundance or mature fibrils alone. More broadly, our findings identify RNA as an environmental regulator of α-helical peptide assemblies, and establish assembly-state control as a tunable determinant of virulence and host defense.

While magnesium isoglycyrrhizinate (MgIG) is a clinically approved therapy for alcohol-associated liver disease (ALD), its precise molecular targets and mechanisms remain uncharacterized. This study aimed to define MgIG's hepatoprotective actions in chronic-binge ALD mouse models and ethanol/palmitic acid-exposed AML-12 hepatocytes. Through an integrated strategy encompassing RNA sequencing, molecular docking, and microscale thermophoresis, we discovered that MgIG directly binds to hydroxysteroid 11-beta dehydrogenase 1 (HSD11B1) at residue 187, a finding corroborated by molecular dynamics simulations. In vivo, MgIG markedly attenuated alcohol-induced liver injury, evidenced by ameliorated histological damage, reduced hepatic steatosis, and normalized liver-to-body weight ratios. In vitro, it effectively reduced lipid accumulation, inflammation, and apoptosis. Mechanistically, RNA sequencing identified isopentenyl diphosphate delta isomerase 1 (IDI1) as a key downstream effector. Hepatocyte-specific genetic manipulations confirmed that MgIG modulates the SREBP2-IDI1 axis, thereby suppressing lipogenesis, inflammatory responses, and apoptotic pathways. We reveal HSD11B1 as a novel direct molecular target of MgIG and elucidate its therapeutic mechanism through the HSD11B1-SREBP2-IDI1 signaling axis, which profoundly impacts ALD pathogenesis. These findings not only validate MgIG's clinical utility but also highlight a promising new therapeutic target for ALD.

Over the last century, invasive species have emerged as an important driver of global biodiversity loss. Lantana camara is one of the hundred most problematic invasive species globally, yet its genetic diversity patterns remain poorly understood. Previous studies hypothesize that invasive L. camara is a species complex of hybrid origin, though this remains untested. We investigated the population genetic patterns of L. camara by sampling 359 plants representing diverse flower colour variants across 36 locations in India. Analyses of the population structure using 19,008 SNPs revealed a strong genetic structure in India. However, this structure showed little correlation with geography; instead, individuals with similar flower colours clustered together irrespective of location in the structure analysis. Low genetic distance between most of the individuals indicated the absence of multiple species. A high inbreeding coefficient and low proportion of heterozygous sites suggested predominant self-fertilization, confirmed by bagging experiments. Thus, we infer that L. camara exists as homozygous inbred lines formed by self-fertilization, associated with distinct flower colours. These results refute the hypothesis that L. camara is a species complex. Our findings highlight a hitherto unknown role for mating systems in invasive species, furthering our understanding of evolution in invasive species.

PKMζ is a persistently active atypical PKC (aPKC) isoform thought to maintain late-phase long-term potentiation (late-LTP) and long-term memory. PKMζ-knockout mice, however, still exhibit hippocampal LTP and spatial memory while lacking neocortical LTP, questioning whether this kinase is fundamental to enduring synaptic potentiation and memory. Tsokas et al. (2016) suggested that the other aPKC, PKCι/λ, may compensate for PKMζ during maintenance in the hippocampus of PKMζ-null mice. In wild-type mice, PKCι/λ drives early-LTP and short-term memory, whereas in PKCι/λ-knockout mice, PKMζ compensates by supporting both early- and late-phase processes. Here, we show that PKCι/λ is persistently upregulated during maintenance in two mouse models: PKMζ-conditional knockout mice, and double-knockout mice carrying both conditional deletion of PKCι/λ and constitutive loss of PKMζ. Because PKCι/λ-gene excision is inducible in the double-knockout line, we could characterize the persistent increase of PKCι/λ in late-LTP prior to its deletion. To examine PKCι/λ function, we induced its deletion in the hippocampus. Whereas mutual compensation preserves LTP when either PKCι/λ or PKMζ alone is knocked out, double-knockout of both PKCι/λ and PKMζ eliminates late-LTP. Double-knockout also abolishes spatial long-term memory without affecting short-term memory. Thus, when PKMζ is absent, PKCι/λ persists to maintain hippocampal late-LTP and long-term memory.

Hippocampus, a key hub of neural circuits for spatial learning and memory, has attracted tremendous studies. Neuronal information processing in the hippocampus can be regulated by many types of neuropeptides. Cholecystokinin (Cck), the most abundant neuropeptide in the central nervous system that is involved in modulating neuronal functions, such as cognition, memory, and neuroplasticity, is widely expressed in the hippocampus. However, whether local excitatory Cck neurons modulate hippocampal function is still unclear. In this study, we showed that CA1 pyramidal neurons receive projections from excitatory Cck neurons in area CA3 (CA3Cck neurons) in adult mice. Subsequently, activation of the CA1-projecting CA3Cck neurons triggers the release of Cck. Then, we found that the activity of CA3Cck-CA1 neurons supports the hippocampal-dependent tasks. Furthermore, inhibition of CA3Cck-CA1 projections or knockdown of CA3Cck gene expression markedly impaired the behavioral tasks and neuroplasticity. Taken together, these results may add to a better understanding of how neuromodulators regulate the neural functions in the central nervous system.

Individual variability shapes how diseases manifest, how patients respond to therapy and how rare phenotypes arise. Conventional experimental approaches obscure variation by averaging which limits mechanistic insight and predictive accuracy. We present a computational framework that builds digital twins of human-induced pluripotent stem cell-derived cardiomyocytes from a single optimized voltage clamp experiment. The framework depends on massive synthetic datasets comprising simulated cells that span broad ionic and electrophysiological ranges. These synthetic data make it possible to control parameters precisely, explore biological variability comprehensively, and train models beyond the limits of experimental data. A neural network trained on synthetic data then inferred biophysical parameters from experimental recordings from live cells, reproducing distinct electrophysiological features. Our study unites computational modeling, data simulation, and learning to enable scalable, precise, individualized cardiac electrophysiology modeling and can be readily extended to any electrically active cell type.