Autonomic dysreflexia is a life-threatening medical condition characterized by episodes of uncontrolled hypertension that occur in response to sensory stimuli after spinal cord injury (SCI)1. The fragmented understanding of the mechanisms underlying autonomic dysreflexia hampers the development of therapeutic strategies to manage this condition, leaving people with SCI at daily risk of heart attack and stroke2–5. Here we expose the neuronal architecture that develops after SCI and causes autonomic dysreflexia. In parallel, we uncover a competing, yet overlapping neuronal architecture activated by epidural electrical stimulation of the spinal cord that safely regulates blood pressure after SCI. The discovery that these adversarial neuronal architectures converge onto a single neuronal subpopulation provided a blueprint for the design of a mechanism-based intervention that reversed autonomic dysreflexia in mice, rats and humans with SCI. These results establish a path towards essential p

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A select few genes act as pivotal drivers in the process of cell state transitions. However, finding key genes involved in different transitions is challenging. Here, to address this problem, we present CellNavi, a deep learning-based framework designed to predict genes that drive cell state transitions. CellNavi builds a driver gene predictor upon a cell state manifold, which captures the intrinsic features of cells by learning from large-scale, high-dimensional transcriptomics data and integrating gene graphs with directional connections. Our analysis shows that CellNavi can accurately predict driver genes for transitions induced by genetic, chemical and cytokine perturbations across diverse cell types, conditions and studies. By leveraging a biologically meaningful cell state manifold, it is proficient in tasks involving critical transitions such as cellular differentiation, disease progression and drug response. CellNavi represents a substantial advancement in driver gene predictio

In vitro-transcribed and circularized RNAs (ivcRNAs) represent a robust platform for sustained protein translation, offering promising potential for localized therapeutic delivery in joint diseases. Osteoarthritis (OA), the most prevalent degenerative joint disorder, remains a major clinical challenge due to its progressive nature and the lack of disease-modifying treatments. In this study, we identify Musashi2 (Msi2) deficiency in articular chondrocytes as a key contributor to OA pathogenesis. To evaluate the efficacy of ivcRNA-mediated protein replacement therapy, we developed a localized delivery strategy that enables high-yield and prolonged protein expression in chondrocytes. Using a destabilization of the medial meniscus (DMM) mouse model, we demonstrate that intra-articular delivery of ivcRNA encoding MSI2 effectively mitigates OA progression in male mice. Furthermore, therapeutic supplementation of SOX5, a downstream effector of MSI2, via ivcRNA delivery further validates this

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Iron is an essential nutrient for most bacteria and is often growth-limiting during infection, due to the host sequestering free iron as part of the innate immune response. To obtain the iron required for growth, many bacterial pathogens encode transporters capable of extracting the iron-containing cofactor heme directly from host proteins. Pathogenic E. coli and Shigella spp. produce the outer membrane transporter ChuA, which binds host hemoglobin and extracts its heme cofactor, before importing heme into the cell. Heme extraction by ChuA is a dynamic process, with the transporter capable of rapidly extracting heme from hemoglobin in the absence of an external energy source, without forming a stable ChuA-hemoglobin complex. In this work, we utilise a combination of structural modelling, Cryo-EM, X-ray crystallography, mutagenesis, and phenotypic analysis to understand the mechanistic detail of this process. Based on this understanding we utilise artificial intelligence-based protein d

Endurance exercise promotes adaptive growth and improved function of myocytes, which is supported by increased mitochondrial activity. In skeletal muscle, these benefits are in part transcriptionally coordinated by peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α). The importance of PGC-1α to exercise-induced adaptations in the heart has been unclear. Here we show that deleting PGC-1α specifically in cardiomyocytes prevents the expected benefits from exercise training and instead leads to heart failure after just 6 weeks of training. Consistent with this, in humans, rare genetic variants in PPARGC1A, which encodes PGC-1α, are associated with increased risk of heart failure. In this model, we identify growth differentiation factor 15 (GDF15) as a key heart-secreted mediator that contributes to this dysfunction. Blocking cardiac Gdf15 expression improves cardiac performance and exercise capacity in these mice. Finally, in human heart tissue, lower cardiomyocyt

In this Review, we present a comprehensive analysis of preclinical models used to study immune checkpoint inhibitor-associated myocarditis (hereafter ICI-myocarditis), a potentially lethal immune-related adverse event. We begin by providing an overview of immune checkpoint inhibitors, highlighting how their efficacy in cancer treatment is counterbalanced by their predisposition to cause immune-related adverse events. Next, we draw from human data to identify disease features that an effective mouse model should ideally mimic. After that, we present a critical evaluation of a wide variety of existing mouse models including genetic, pharmacological and humanized models. We summarize insights gathered about the underlying mechanisms of ICI-myocarditis and the role of mouse models in these discoveries. We conclude with a perspective on the future of preclinical models, highlighting potential model improvements and research directions that could strengthen our understanding of ICI-myocardit

Hematoxylin and eosin (H&E) is a common and inexpensive histopathology assay. Though widely used and information-rich, it cannot directly inform about specific molecular markers, which require additional experiments to assess. To address this gap, we present ROSIE, a deep-learning framework that computationally imputes the expression and localization of dozens of proteins from H&E images. Our model is trained on a dataset of over 1300 paired and aligned H&E and multiplex immunofluorescence (mIF) samples from over a dozen tissues and disease conditions, spanning over 16 million cells. Validation of our in silico mIF staining method on held-out H&E samples demonstrates that the predicted biomarkers are effective in identifying cell phenotypes, particularly distinguishing lymphocytes such as B cells and T cells, which are not readily discernible with H&E staining alone. Additionally, ROSIE facilitates the robust identification of stromal and epithelial microenvironment

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On the basis of extensive mechanistic research over three decades, Parkinson disease (PD) and related synucleinopathies have been proposed to be combined proteinopathies and lipidopathies. Evidence strongly supports a physiological and pathogenic interplay between the disease-associated protein α-synuclein and lipids, with a demonstrable role for lipids in modulating PD phenotypes in the brain. Here, we refine this hypothesis by proposing PD to be a disease specifically involving metabolic dysregulation of fatty acids, a ‘fatty acidopathy’. We review extensive findings from many laboratories supporting the perspective that PD centres on fatty acid dyshomeostasis — alterations in the fatty acid-ome — as the critical feature of lipid aberration in PD and other α-synucleinopathies. This construct places transient α-synuclein binding to fatty acid side chains of cytoplasmic vesicles as a principal contributor to the biology of PD-relevant α-synuclein–membrane interactions. We propose 

Neuropsychiatric disorders remain difficult to treat due to complex and poorly understood mechanisms. NeuroPainting is a high-content morphological profiling assay based on Cell Painting and optimized for human stem cell–derived neural cell types, including neurons, progenitors, and astrocytes. The assay quantifies over 4000 features of cell structure and organelle organization, generating a dataset suitable for phenotypic screening in neural models. Here, we show that, in studies of the 22q11.2 deletion—a strong genetic risk factor for schizophrenia—we observe cell-type-specific effects, particularly in astrocytes, including mitochondrial disruption, altered endoplasmic reticulum organization, and cytoskeletal changes. Transcriptomic analysis shows reduced expression of cell adhesion genes in deletion astrocytes, consistent with post-mortem brain data. Integration of RNA and morphology data suggests a link between adhesion gene dysregulation and mitochondrial abnormalities. These resu

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Sound structures such as phonemes and words have highly variable durations. Therefore, there is a fundamental difference between integrating across absolute time (for example, 100 ms) versus sound structure (for example, phonemes). Auditory and cognitive models have traditionally cast neural integration in terms of time and structure, respectively, but the extent to which cortical computations reflect time or structure remains unknown. Here, to answer this question, we rescaled the duration of all speech structures using time stretching and compression and measured integration windows in the human auditory cortex using a new experimental and computational method applied to spatiotemporally precise intracranial recordings. We observed slightly longer integration windows for stretched speech, but this lengthening was very small (~5%) relative to the change in structure durations, even in non-primary regions strongly implicated in speech-specific processing. These findings demonstrate tha

Copy number variations (CNVs), the gain or loss of genomic regions, are associated with disease, especially cancer. Single cell technologies offer new possibilities to capture within-sample heterogeneity of CNVs and identify subclones relevant for tumor progression and treatment outcome. Several computational tools have been developed to identify CNVs from scRNA-seq data. However, an independent benchmarking of them is lacking. Here, we evaluate six popular methods in their ability to correctly identify ground truth CNVs, euploid cells and subclonal structures in 21 scRNA-seq datasets. We discover dataset-specific factors influencing the performance, including dataset size, the number and type of CNVs in the sample and the choice of the reference dataset. Methods which include allelic information perform more robustly for large droplet-based datasets, but require higher runtime. Furthermore, the methods differ in their additional functionalities. We offer a benchmarking pipeline to ide

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Legionella pneumophila, an intracellular bacterial pathogen, injects effector proteins into host cells to manipulate cellular processes and promote its survival and proliferation. Here, we reveal a unique mechanism by which the Legionella effector PieF perturbs host mRNA decay by targeting the human CCR4–NOT deadenylase complex. High-resolution cryo-electron microscopy structures and biochemical analyses reveal that PieF binds with nanomolar affinity to the NOT7 and NOT8 catalytic subunits of CCR4–NOT, obstructing RNA access and displacing a catalytic Mg²⁺ ion from the active site. Additionally, PieF prevents NOT7/8 from associating with their partner deadenylases NOT6/6L, inhibiting the assembly of a functional deadenylase complex. Consequently, PieF robustly blocks mRNA poly(A) tail shortening and degradation with striking potency and selectivity for NOT7/8. This inhibition of deadenylation by PieF impedes cell cycle progression in human cells, revealing a novel bacterial strategy to

Discovering how computations are implemented in the brain at the level of monosynaptic connectivity requires probing for connections from potentially thousands of presynaptic candidate neurons. Two-photon optogenetics is a promising technology for mapping such connectivity via sequential stimulation of individual neurons while recording postsynaptic responses intracellularly. However, this technique is currently not scalable because stimulating neurons one by one requires prohibitively long experiments. Here we developed novel computational tools that, when combined, enable learning of monosynaptic connectivity from high-speed holographic ensemble stimulation. First, we developed a model-based compressed sensing algorithm that identifies connections from postsynaptic responses evoked by stimulating many neurons at once, greatly increasing mapping efficiency. Second, we developed a deep-learning method that isolates the postsynaptic response to each stimulus, allowing stimulation to rap

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