Biology subjects

Zeng, X.

Publications and source records attributed to Zeng, X..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Targeting Gi/o-coupled GPCRs to inhibit nociceptors: insights from the serotonin receptor Htr1b and triptans

Pain perception is initiated upon activation of nociceptors of the dorsal root ganglia (DRG) and trigeminal ganglia. We identified G protein-coupled receptors (GPCRs) expressed in CGRP+ mouse and human DRG neurons and found that agonists of several identified Gi/o-coupled and orphan GPCRs attenuated neuronal excitability. Experiments focusing on the Gi/o-coupled serotonin receptor Htr1b, which is expressed in mouse and human CGRP+ DRG neurons, revealed that Htr1b/1d agonists, the triptans sumatriptan and zolmitriptan, attenuated CGRP+ neuron excitability in vitro and exhibited analgesia across several pain models, including neuropathic pain. Conditional genetic deletion experiments showed that triptan-induced analgesia is mediated by Htr1b expressed in A-fiber mechanonociceptors. Also, triptan-associated adverse effects are partially mediated by Htr1b-independent targets. Further testing identified the GPCR Gpr19 as an additional promising target for treating pain. These findings establish a preclinical screening platform for identifying novel analgesics and reveal nociceptor GPCRs that may be targeted to treat pain.

neuroscience↗

bioRxiv2026

PanVariants: Best Practice for Pangenome-based Variant Calling Pipeline and Framework

BackgroundAlthough pangenome references offer richer population diversity compared to linear references, current mainstream pangenome-based variant callers are limited to detecting only known variants stored in the graph. To address this limitation, we developed PanVariants, a novel pipeline designed to improve the detection of both known and novel variants accurately. We systematically evaluated its performance against the traditional linear alignment solution (BWA+GATK/Manta) and the existing pangenome-aware solution (DRAGEN/PanGenie) in three contexts: small variants (SNVs/indels) and structural variants (SVs) accuracy in Genome in a Bottle samples, clinical detection on positive samples, and application in cohort-based joint calling. ResultsBy integrating k-mer-based and mapping-based methods, PanVariants significantly reduced variant errors (FPs + FNs), achieving a 73% reduction compared to BWA+GATK and a 45% reduction compared to DRAGEN for SNVs. Retraining the DeepVariant model with high-quality DNBSEQ data further decreased errors by 15%. For SVs detection, PanVariants attained an F1-score of 89.39%, markedly outperforming DRAGEN (68.18%) and BWA+Manta (58.33%), approaching long-read sequencing performance (95.22%). In validation using clinical positive samples, PanVariants successfully detected all expected pathogenic variants while PanGenie failed. In the cohort joint-calling analysis, PanVariants detected more variants, made fewer Mendelian inheritance errors, and gave better per-sample accuracy than GATK. ConclusionsPanVariants establishes a robust framework and best-practice pipeline for pangenome-based variant detection, achieving both sensitive novel variant discovery and high accuracy for SNVs, indels and SVs. Our systematic evaluation of optional processing steps and input variables offers practical guidance for users. Validated across diagnostic and population-based applications, our findings strongly support the transition from linear to pangenome references in future genomics.

bioinformatics↗

bioRxiv2026

Spatial transcriptomic landscape of the Ciona adult brain: functional zonalisation and cellular composition in a sessile chordate brain and a novel insight into neural gland function

The ascidian Ciona provides a key model for understanding the evolutionary origin of the vertebrate brain. While the larval nervous system has been extensively characterized, the molecular and cellular organization of the adult neural complex remains poorly defined. Here, we generated spatial transcriptomic maps of the adult Ciona neural complex from three individuals, with four serial sections per donor, using the 10x Visium platform. Clustering-based analysis identified five major tissue domains, including the cerebral ganglion, neural gland, ciliated funnel, neural gland duct/dorsal strand, and body wall muscle. To further refine spatial resolution, we computationally reconstructed approximately 980 super-resolution gene expression maps by integrating transcriptomic measurements with histological image features. The super-resolution maps enabled precise delineation of molecular territories within the neural complex. In the cerebral ganglion, high-resolution reconstruction revealed clear molecular zonation, distinguishing the cortex and medulla. Within the cortex, the central region facing the neural gland and anteroposterior distal regions showed distinct molecular properties. In the neural gland, we identified coordinated enrichment of cell-cell interaction- and extracellular matrix-related genes, suggesting specialized structural and physiological properties. We propose that the neural gland play a pivotal role for the cerebral ganglion in maintaining homeostasis, supporting development, and providing a signaling interface, which is reminiscent of a primitive form of the choroid plexus and meninges found in vertebrates. Together, this study provides the first spatially resolved transcriptomic atlas of the adult Ciona neural complex and establishes a molecular framework for investigating functional regionalization and brain evolution in chordates.

genomics↗

bioRxiv2026

An EEG-fMRI Jointly Constrained Digital Twin Brain and Its Application in Alzheime's Disease

Current digital twin brain (DTB) models are typically optimized using single-modality functional neuroimaging data, which restricts their ability to simulate brain dynamics across multiple spatiotemporal scales. Here, we bridged this gap by developing a two-stage DTB (TS-DTB) modeling framework jointly constrained by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data. Validation in both the healthy young and Alzheimers disease (AD) cohorts demonstrated that the TS-DTB model simultaneously captures subject-specific features of multi-scale brain dynamics. In particular, the TS-DTB models of AD patients successfully recapitulated spectral-temporal signatures of cognitive decline, mechanistically linking these deficits to excitation-inhibition (E-I) imbalances. By simulating responses to repetitive transcranial magnetic stimulation (rTMS), we further revealed that cognitive recovery in AD patients can be driven by E-I rebalancing via synaptic reconfiguration and background suppression. Overall, these findings underscore the potential of the TS-DTB to advance the mechanistic understanding of the brain and inform personalized digital therapeutics.

neuroscience↗

bioRxiv2026

VesSynth: Tubes Are All You Need for Robust Cross-Scale Cross-Modal 3D Vessel Segmentation

The cerebral vasculature is central to brain function, with alterations linked to numerous cerebrovascular and neurological disorders. Yet, no single imaging modality can capture the entire cerebral vascular network in humans. Instead, an array of techniques are sensitized to different spatial scales, while trading off resolution for coverage. Magnetic Resonance Imaging (MRI) typically resolves only large pial vessels, while high-resolution microscopy allows micrometer-scale vessels to be mapped over limited spatial extents. These techniques must therefore be combined to obtain a complete mapping of the cerebral angioarchitecture, which underscores the need for automatic, cross-modal vessel segmentation. Here, we introduce VesSynth, a flexible vessel segmentation framework that achieves state-of-the-art accuracy across multiple modalities and spatial resolutions (MR, optical and X-ray imaging), despite being trained entirely on synthetic data. By enabling consistent vascular mapping across scales, this framework paves the way to comprehensive investigation of cerebrovascular organization and its role in health and disease.

bioengineering↗

bioRxiv2026

Regulation of cyanobacterial type IV pilus-dependent functions by interaction between a c-di-GMP receptor and two transcription factors

Cyanobacteria utilize type IV pili for many behavioural responses, such as phototaxis, aggregation, floating, and DNA uptake. Type IV pilus-dependent functions are regulated by the nucleotide second messengers, c-di-GMP and cAMP. In this study, we investigated the role of a recently identified c-di-GMP receptor (CdgR) in cyanobacteria that harbours a ComFB domain. ComFB-domain proteins are widespread in cyanobacteria and are also present in heterotrophic bacteria. We demonstrated that the CdgR homolog from the cyanobacterium Synechocystis sp. PCC 6803, a model organism for studying type IV pilus-dependent functions, specifically binds to c-di-GMP. Genetic and phenotypic analyses revealed that Synechocystis CdgR is involved in phototactic motility and natural competence. Inactivation of cdgR resulted in altered expression of specific sets of minor pilins, which are essential for motility or natural competence. We identified interactions between CdgR and the CRP-family transcription factors, SyCRP1 and SyCRP2. Disruption of these CdgR-SyCRP1 and CdgR/SyCRP2 complexes is initiated by elevated c-di-GMP levels. Moreover, the assembly and stability of these complexes are influenced by other cyclic nucleotides, such as cAMP and c-di-AMP. These observed interactions imply a complex regulatory mechanism by which CdgR influences gene expression in response to cyclic nucleotide messenger signalling, particularly c-di-GMP. The present findings highlight the importance of CdgR in c-di-GMP signalling and its role in regulating type IV pilus-dependent functions in Synechocystis. The modulation of the expression of specific minor pilin genes by CdgR, through interactions with the transcription factors SyCRP1 and SyCRP2, contributes to the establishment of multiple type IV pilus functions and adaptive behaviours of cyanobacteria.

microbiology↗

bioRxiv2026

Tract-explainable and underexplained synchrony play complementary roles in the functional organization of the brain

Macroscale functional connectivity emerges from a complex interplay between structural wiring and non-tract-mediated mechanisms. However, the intrinsic entanglement of these contributions obscures their specific roles in brain organization. Here, we introduce a computational modeling framework to disentangle functional synchrony into two fundamentally distinct components: tract-explainable and tract-underexplained synchrony. Validated across two large-scale human cohorts (n = 1214) and an independent marmoset dataset (n = 24), this dissociation reveals an evolutionarily conserved architectural principle. We show that tract-explainable synchrony aligns closely with structural connectomes to facilitate global integration. Conversely, tract-underexplained synchrony drives local modularity and is anchored by multiscale cortical similarity, encompassing microstructural, receptor, and transcriptomic profiles. Crucially, these components gradually dissociate from sensori-motor to higher-order association cortices, with the importance of tract-underexplained synchrony progressively increasing. Furthermore, the tract-underexplained synchrony exhibits greater individual variability and demonstrates a stronger association with individual cognitive performance. Ultimately, tract-based and non-tract-mediated mechanisms serve distinct yet complementary roles, jointly shaping a functional organization that balances conserved macroscopic stability with higher-order cognitive flexibility.

neuroscience↗

bioRxiv2026

APEX1 Protects Cardiomyocytes Against Oxidative Stress by Regulating p53 Protein Stability

Withdrawal statementThe authors have withdrawn this manuscript because the preprint was uploaded to bioRxiv before all co-authors had been fully informed and had provided their explicit consent to make the manuscript publicly available as a preprint. Following internal discussions, the authors have reached a consensus that they do not wish this version to be publicly available. We therefore kindly request that this preprint be withdrawn on the bioRxiv platform. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

molecular biology↗

bioRxiv2026

The m6A Landscapes on the RNA of Mumps Virus and the Host 3 Modulate the Viral Replication and Antiviral Innate Immunity

N6-methyladenosine (m6A) is the most prevalent internal modification in eukaryotic mRNA and has emerged as a critical regulator of RNA virus infection. However, its role in the mumps virus (MuV), a non-segmented negative-strand (NNS) RNA virus, remains undefined. Here, we comprehensively characterize the m6A epitranscriptome of MuV JL2 strain and its functional impact on viral replication and host innate immunity. Using single-base resolution GLORI-seq, we identified abundant m6A modifications on MuV genomic, antigenomic and messenger RNAs, with uneven distribution and non-canonical motif enrichment. Genetic depletion of METTL3 enhanced viral replication by facilitating RNA synthesis and nucleocapsid encapsidation in Vero-E6 cells. In epithelial A549 and monocytoid THP1-iDC cells, MuV infection triggered robust type I/III interferon and proinflammatory responses by activating multiple pattern recognition receptors in a m6A-dependent manner and induced incomplete iDC maturation characterized by downregulated HLA class II; however, the latter molecule can be partially restored by METTL3-knockdown. The MuV infection reshaped the host m6A landscape with a positive correlation between m6A enrichment and transcripts enhancement of numerous innate immune genes in a cell type-specific manner. Functional analyses revealed that host m6A machinery modulates antiviral signaling, which varies between viral infection and viral RNA-transfection due to the infection-induced host m6A machinery fluctuation. Collectively, our findings demonstrate that m6A serves as a bidirectional regulator during MuV infection, simultaneously constraining viral replication and modulating host immune recognition, thereby highlighting RNA methylation as a pivotal determinant of MuV pathogenesis and a potential target for optimized immunogenicity. ImportanceN6-methyladenosine (m6A) modification has emerged as a key regulator of RNA virus infection, yet its role in mumps virus (MuV) remains undefined. Here we map the single-base resolution m6A landscape of a recombinant MuV and demonstrate that viral m6A restricts genome encapsidation and replication while attenuating innate immune sensing. MuV infection extensively remodels host m6A profiles in epithelial and dendritic cells, preferentially enhancing methylation of innate immune transcripts. Strikingly, m6A-deficient MuV RNA exhibits increased affinity for RIG-I-like and Toll-like receptors, eliciting stronger interferon and proinflammatory responses. Our findings identify m6A as a bidirectional regulator of MuV replication and immunity and suggest an epitranscriptomic strategy to optimize live-attenuated vaccine design.

immunology↗

bioRxiv2026

MG53 Protects Against Intestinal Inflammation by Inhibiting NLRP3 Inflammasome Activation

Background and ObjectivesInflammatory bowel disease (IBD) involves dysregulated immune responses and chronic intestinal inflammation. The nod-like receptor pyrin domain-containing 3 (NLRP3) inflammasome plays a critical role in IBD pathogenesis, but regulatory mechanisms remain not fully understood. Mitsugumin 53 (MG53, also known as TRIM72), originally identified as a critical membrane repair protein, has emerged as a novel regulator of inflammatory processes. To investigate the protective role of MG53 in colitis and elucidate its mechanisms in regulating NLRP3 inflammasome activation in IBD. MethodsWe used dextran sodium sulfate (DSS)-induced colitis models comparing MG53 knockout (MG53-/-) and wild-type (WT) mice, assessing disease severity, MG53 tissue uptake, and therapeutic effects of recombinant human MG53 (rhMG53). In vitro studies examined rhMG53s effects on NLRP3 inflammasome activation, caspase-1 cleavage, interleukin-1{beta} (IL-1{beta}) secretion, and MG53-NLRP3 interactions. ResultsMG53-/- mice showed more severe colitis with increased weight loss, higher disease activity scores, shortened colons, and greater inflammation. DSS treatment induced the accumulation of circulating MG53 in inflamed colonic tissue. rhMG53 administration ameliorated colitis severity in MG53-/- mice and dose-dependently suppressed NLRP3 inflammasome activation in vitro. MG53 interacted with NLRP3 and reduced apoptosis-associated speck-like protein containing a CARD (ASC) speck formation and NLRP3 oligomerization without affecting upstream signaling or NLRP3 stability. ConclusionMG53 is a physiological regulator of NLRP3 inflammasome activation that protects against colitis, suggesting therapeutic potential for IBD.

immunology↗

bioRxiv2026

Multi-dimensional diffusion MRI at ultra-high gradient strength for mapping axonal architecture and microstructure in the primate brain

We present the most comprehensive sampling of the macaque and human brain with diffusion MRI to date. As part of the BRAIN CONNECTS center for Large-scale Imaging of Neural Circuits, we leverage ultra-high-gradient MRI systems, including the first-of-its-kind Connectome 2.0, for post-mortem acquisitions. Each sample is imaged for [~]250 hours at multiple spatial resolutions down to 0.25 mm isotropic for whole macaque brains and 0.4 mm isotropic for human hemispheres. Our optimized protocols allow us to sample both species across [~]50 diffusion shells varying in b-value, diffusion time, and echo time, reaching ultra-high b-values up to 64000 s/mm2 with high signal-to-noise ratio. We demonstrate that these multi-dimensional data resolve not only white matter connectional architecture but also cortical and subcortical cytoarchitectonic boundaries, at a level of detail previously inaccessible in whole-brain noninvasive imaging. As such, these data are an important resource for both technical development and basic and clinical neuroscience.

neuroscience↗

bioRxiv2026

T2T Genome and Population Resequencing Reveal OfCCD4 Alleles Orchestrate Petal Color and Scent in Osmanthus fragrans

Osmanthus fragrans is prized for its floral color and fragrance, both key targets for genetic improvement. However, the lack of a complete genome assembly and comprehensive population structure hinders gene dissection and marker development for breeding. To investigate the genetic basis of color-scent interaction, a telomere-to-telomere (T2T) genome assembly is generated, and whole-genome resequencing of 100 cultivars is performed. Integrative population and metabolomic analyses reveal a trade-off: orange-red ( Aurantiacus) cultivars accumulate high /{beta}-carotene but low aromatic apocarotenoids (/{beta}-ionone), while yellow-white cultivars show the opposite pattern. Divergence mapping identifies OfCCD4 as the major underlying locus. Three alleles are characterized--functional (A), partially functional (aDel), and a frameshift null (aStop)--with aStop strictly co-segregating with the Aurantiacus phenotype. Transient and stable transformations confirm that A and aDel cleave /{beta}-carotene into /{beta}-ionone, while aStop abolishes this activity. A co-dominant PCR marker is developed based on allele-specific polymorphisms. OfCCD4 is thus established as the key regulator of the color-scent trade-off in osmanthus. The identified alleles and molecular marker enable rapid, low-cost genotyping at the seedling stage--offering particular value for marker-assisted selection in ornamental breeding, where extended juvenility makes phenotypic selection highly time- and resource-intensive.

plant biology↗

bioRxiv2026

Multi-agent reasoning enables predictive design of living materials

Artificial intelligence is increasingly used to accelerate scientific discovery, but most successful frameworks operate within well-defined molecular, protein or materials spaces. Living materials present a more formidable computational problem because functions emerge from context dependent coupling among cells, matrices, fabrication processes and evaluation conditions. Here we introduce LiveMat, a multi-agent reasoning framework that transforms unstructured literature into a computable design space for living materials. LiveMat standardizes 34,215 living material records, integrating 16,769 microorganism and 17,446 polymer entries into a knowledge graph linking living components, abiotic matrices, functional outputs, evaluation contexts and performance metrics. Benchmarking across five large language models shows that living material reasoning is limited mainly by cross-domain feature integration rather than coarse classification. LiveMat overcomes this limitation through constraint decomposition, provenance-aware extraction, consistency checking and expert-anchored ranking. In a prospective wound-healing task, it prioritizes a four-component design with state-of-the-art in vivo performance, establishing a scalable infrastructure for interpretable, evidence-grounded living material discovery.

biochemistry↗

bioRxiv2026

Pragmatic analysis with knowledge-guided for unraveling peptide-protein pairwise non-covalent mechanisms

Understanding peptide-protein interactions is vital for decoding cellular signaling and developing targeted therapies. However, the complexity of multi-molecular associations and diverse non-covalent interactions make accurate prediction and site-specific annotation challenging. Here, we propose KGIPA, a knowledge-guided pragmatic analysis framework that incorporates pragmatic concepts from natural language into life science, capturing the influence of biological environments on non-covalent interactions. KGIPA integrates intra- and extra-linguistic contextual information to combine multimodal single-molecule features and build residue-level interaction maps. It also uses biological prior knowledge to coordinate various non-covalent interaction types. Benchmark tests demonstrate KGIPA outperforms the state-of-the-art methods in evaluating molecular binding, including protein and peptide binding residues and residue-pair interactions. Furthermore, KGIPA demonstrates strong performance in peptide-protein binding affinity prediction and peptide virtual screening. Wet-lab experiments validate its reliability, revealing high consistency between predicted and experimentally measured binding behaviors. These results highlight KGIPAs potential to accelerate peptide drug discovery and establish pragmatic analysis as an effective paradigm for decoding the molecular language of interactions.

bioinformatics↗

bioRxiv2026

Simulating Thermoresponsive Behavior of Disordered Proteins with Temperature-Dependent Coarse-Grained Potentials Derived from Hydration Free Energies

Thermoresponsive phase transitions of intrinsically disordered proteins (IDPs), including both upper critical solution temperature (UCST) and lower critical solution temperature (LCST) transitions, are widely observed in natural and synthetic sequences. However, most existing coarse-grained (CG) models employ temperature-independent interactions and fail to capture solvation-driven LCST behavior. To address this, we introduce TEA (Temperature-dependent Energetics derived from hydrAtion free energies), a physics-based framework that augments CG models with temperature-dependent interactions derived from hydration thermodynamics. Leveraging extensive all-atom molecular dynamics simulations, we demonstrate that inter-residue interaction strengths quantified by excess free energies correlate linearly with hydration free energies. This correlation, combined with a validated combination rule for heterotypic interactions, allows the derivation of temperature-dependent potentials across continuous temperature space. We map these atomistic energetics to CG potentials via a single global scaling parameter, ensuring minimal overfitting and high transferability. The TEA-augmented CG models robustly distinguish UCST- and LCST-type sequences, successfully identify experimentally reported outliers, and accurately reproduce LCST-type single-chain compaction trends and phase diagrams of multiple disordered proteins. Collectively, our work provides a transferable and physically interpretable framework that bridges atomistic hydration thermodynamics and phase behavior of IDPs, enabling the simulation of thermoresponsive sequences with minimal phenomenological fitting.

biophysics↗

bioRxiv2026

Multimodal Behavior Scoring quantifies depression-like severity across chronic stress models and identifies stress-resilient mice

IntroductionRodent chronic-stress models are central to preclinical depression research, yet behavioral phenotypes are often assessed assay-by-assay, which can reduce cross-cohort comparability and mask heterogeneity across symptom domains (e.g., anhedonia, anxiety-like behavior, and behavioral despair). An integrative behavioral index may provide a more robust and interpretable measure of overall stress-induced severity and facilitate stratification of susceptible versus resilient individuals. MethodsWe developed the Multimodal Behavior Scoring (MBS) algorithm to integrate outcomes from standard assays--sucrose preference test (SPT), open field test (OFT), and forced swim test (FST)--into a unified severity score. MBS was evaluated in two independent paradigms, chronic unpredictable mild stress (CUMS) and chronic social defeat stress (CSDS), quantifying group discrimination, reproducibility (intraclass correlation coefficient, ICC), and phenotypic stratification. ResultsMBS robustly separated stressed from control mice (CUMS: +39.8%, p<0.0001; CSDS: +121.8%, p<0.0001) and showed high cross-cohort reproducibility (ICC>0.88). MBS further identified stress-resilient subpopulations and uncovered paradigm-specific behavioral signatures, with despair-dominant patterns in CUMS (FST-MBS, r=0.61) and anxiety-centric patterns in CSDS (OFT-MBS, r=-0.74). Excluding TST did not reduce discrimination performance. ConclusionMBS provides an integrative framework for quantifying depression-like behavioral severity and heterogeneity, enabling streamlined protocols and improving the translational utility of preclinical cohorts for biomarker discovery and antidepressant screening.

animal behavior and cognition↗

bioRxiv2026

Coacervate Droplets as a Liquefier for the Solid-to-Liquid Transition of RNA Aggregates

Biomolecular condensates are functionally gated by their material properties. While RNA is a primary structural scaffold, its sequence-specific interactions can aberrantly drive condensates into dysfunctional solid aggregates. Yet, actively reversing this solidification to restore function remains a fundamental challenge, impeding progress in synthetic biology and therapeutics interventions. Here, we establish that complex coacervates can be engineered as liquefiers to actively remodel solid RNA-peptide aggregates into functional liquid droplets. Integrating systematic experiments with all-atom simulations, we decode a multiscale mechanism: coacervate infiltration at the micrometer scale mediates molecular buffering at the nanometer scale, which fluidizes the crosslinked network to drive macroscopic liquefaction. This capability is governed by a design rule, where coacervates formed by small-molecular anions exhibit optimal efficacy. We deploy this principle to functionally rescue silenced RNA within a model of pathologically solidified ribonucleoprotein assemblies. Our work provides a general framework for the active, compositional control of biomolecular phase behavior, with direct implications for managing pathological aggregation and engineering functional condensates in synthetic and living systems.

biophysics↗

bioRxiv2026

Characterization of Atypical Ebola Virus Disease in Ferrets

Ebola virus (EBOV) typically results in a severe--and often lethal--acute disease. However, increasing evidence suggests that EBOV can persist in certain immune-privileged tissues, which may then serve as reservoirs for the later reemergence of EBOV and disease recrudescence. Here, we report atypical EVD recrudescence in a ferret model inoculated with an otherwise lethal dose of EBOV and treated with low doses of a highly potent monoclonal antibody cocktail. Among 32 antibody-treated ferrets, 14 animals survived, while 8 succumbed to acute EVD within about 5-8 days. The remaining 10 animals succumbed to atypical EVD between 12 and 18 days post-infection (DPI) despite having shown no, or very minor, signs of illness during the acute phase of disease. While viremia disappeared by 14 DPI in most animals that succumbed to atypical EVD, it rebounded modestly just prior to death. Unlike animals that died of acute EVD, those that died of atypical EVD showed only a moderate systemic inflammatory response and few signs of organ dysfunction, in line with low levels of virus in the liver and spleen. Interestingly, however, ferrets that died of atypical EVD showed high levels of virus in the brain, consistent with increased markers of inflammation in the central nervous system and significant pathological changes, including a breakdown in the blood-brain barrier and severe meningoencephalitis. Not only does this study shed important light on the atypical and underappreciated manifestations of EVD, but it also establishes the ferret as a valuable model of EBOV persistence and recrudescence. AUTHOR SUMMARYOver the last several years, it has become increasingly apparent that Ebola virus is capable of persisting in survivors and, occasionally, reemerging to cause a recrudescent disease that is often distinct from the acute illness and characterized by neurological involvement. Since nearly all existing Ebola virus animal models are uniformly lethal, it has been difficult to understand the processes that lead to persistence and atypical manifestations of disease. In this study, we describe a late-onset, atypical Ebola virus disease in ferrets that was characterized by high levels of virus in the brain and significant markers of brain inflammation. Our comprehensive analysis of the pathogenic processes that contributed to this disease not only provides critical insight into Ebola virus persistence and recrudescence, but it also establishes the ferret as the first tractable model for investigating these atypical outcomes.

microbiology↗