Biology subjects

Kim, S.

Publications and source records attributed to Kim, S..

At least 109 records · Page 6Linked to original sources

bioRxiv2026

Meningeal inflammation and arachnoid barrier breakdown in a mouse model of neonatal bacterial meningitis

Newborns are especially susceptible to bacterial meningitis, primarily caused by Group B Streptococcus (GBS), due to incomplete maturation of immune and barrier systems. While meningitis is well known to break down the blood-brain barrier (BBB), how the meningeal arachnoid barrier, a critical component of the blood-cerebrospinal fluid barrier (B-CSFB), responds to infection is poorly understood. Using a neonatal mouse model of bacterial meningitis, we demonstrate that GBS infection significantly increases arachnoid barrier permeability, coinciding with alterations in Claudin-11 tight junction distribution and elevated meningeal production of proinflammatory cytokines (IL-6, TNF-, CXCL1). CD206+/Lyve1+ border-associated macrophages (BAMs) undergo significant morphological and molecular activation post-infection, but their depletion prior to GBS infection did not attenuate arachnoid barrier leakage or inflammatory cytokine levels during infection. We show that meningeal fibroblasts are a main source of proinflammatory cytokines in response to GBS infection and that exposure to the inflammatory cytokine TNF- alone is sufficient to induce neonatal arachnoid barrier breakdown. These results support neonatal arachnoid barrier is vulnerable to cytokine-induced breakdown in bacterial infection and highlight the role of non-immune meningeal cells like fibroblasts during bacterial infection.

neuroscience↗

bioRxiv2026

RankMap: Rank-based reference mapping for fast and robust cell type annotation in spatial and single-cell transcriptomics

Accurate cell type annotation is essential for the analysis of single-cell and spatial transcriptomics data. While reference-based annotation methods have been widely adopted, many existing approaches rely on full-transcriptome profiles and incur substantial computational cost, limiting their applicability to large-scale spatial datasets and platforms with partial gene panels. Here, we present RankMap (https://github.com/jinming-cheng/RankMap), an efficient and flexible R package for reference-based cell type annotation across both single-cell and spatial transcriptomics. RankMap transforms gene expression profiles into rank-based representations using the top expressed genes per cell, improving robustness to platform-specific biases and expression scale differences. A multinomial regression model trained with elastic net regularization is then used to predict cell types and associated confidence scores. We benchmarked RankMap on five spatial transcriptomics datasets, including Xenium, MERFISH, and Stereo-seq, as well as two single-cell datasets, and compared it with established methods such as SingleR, Azimuth, and RCTD. RankMap achieved competitive or superior annotation accuracy while consistently reducing runtime compared to existing methods, particularly for large spatial datasets. These results demonstrate that RankMap provides a scalable and robust solution for reference-based cell type annotation in modern single-cell and spatial transcriptomics studies.

bioinformatics↗

bioRxiv2026

In planta-specific transcriptional regulatory circuit regulates expression of MoHTR1, a nuclear effector gene of Magnaporthe oryzae

During host-pathogen interactions, fungal pathogens secrete effector proteins into host cells to manipulate the host immune system and facilitate infection. Although many effector genes are highly expressed during infection stages, there is limited information on the mechanisms regulating their in planta expression. Here, we characterize the promoter of MoHTR1, a nuclear effector gene of the rice blast fungal pathogen, to elucidate its in planta-specific expression. Using promoter deletion and mutation analyses, we identified a core cis-element (TATTTCGT) within the MoHTR1 promoter, designated the in planta active (IPA) element, which is crucial for in planta-specific expression. The IPA element is responsible for the expression of not only MoHTR1, but also other effector genes including a known effector Slp1. Furthermore, the IPA element enables the in planta expression of MobZIP14, a gene specifically expressed during vegetative growth. The IPA element plays a critical role in fungal virulence by enabling MoHTR1 expression and regulating host immune responses. Bioinformatic and DNA-protein interaction analyses revealed that RGS1, a transcription factor containing a winged-helix binding domain, acts as a transcriptional regulator of MoHTR1 by directly binding to the IPA element. Our findings provide new insights into the regulatory mechanisms driving the in planta-specific expression of fungal effector genes.

microbiology↗

bioRxiv2026

Bioactivity-driven discovery of repurposable antivirals as OSCAR inhibitors that promote cartilage protection via transcriptomic reprogramming

Osteoarthritis (OA) is a progressive degenerative joint disorder characterized by cartilage degradation, chronic pain, and impaired joint function. The avascular nature of cartilage isolates chondrocytes from systemic circulation, presenting significant challenges for therapeutic intervention. Despite extensive efforts, no clinically effective disease-modifying osteoarthritis drugs (DMOADs) are currently available. Targeting chondrocyte-specific receptors has therefore emerged as a promising strategy. The osteoclast-associated receptor (OSCAR), expressed on chondrocytes, has been implicated in the regulation of cartilage homeostasis and OA pathogenesis. Here, we applied sBEAR (Structurally similar Bioactive compound Enrichment by Assay Repositioning), a bioactivity-driven virtual screening framework independent of target structural information, to identify small-molecule inhibitors of the OSCAR-collagen interaction. By mining large-scale bioactivity profiles, we identified adefovir (ADV) and brivudine (BRV), as candidate OSCAR inhibitors. Molecular docking analyses indicated that both compounds occupy the collagen-recognition pocket within the OSCAR D2 domain. Intra-articular administration of these compounds in a post-traumatic OA mouse model significantly attenuated OA progression and enhanced chondrocyte regeneration. Both compounds increased Sox9 expression, and transcriptomic analyses revealed that BRV reverses inflammatory and extracellular matrix-degrading transcriptional programs. Together, these findings establish OSCAR as a therapeutically actionable target in OA and highlight ADV and BRV as potential DMOAD candidates.

bioinformatics↗

bioRxiv2026

A Multicopper Oxidase from Paenibacillus polyethylenelyticus JNU01 Oxidizes Polyethylene

Polyethylene (PE) is a widely used plastic that persists in the environment and resists breakdown via microbial degradation. In this work, we discovered a new bacterium, Paenibacillus polyethylenelyticus JNU01, that grows on a PE-like wax (PELW, 4 kDa) as its sole carbon source, causing chemical modifications to the substrate and releasing small-molecule products. Genomic and transcriptomic analyses identified a multicopper oxidase (PpMmcO) as a key enzyme candidate for this observed activity. PpMmcO caused surface oxidation, increased hydrophilicity, and the release of oxygenated products such as ketones, alkanes, and alkenoic acids. Scanning electron microscopy confirmed surface damage on both PELW and post-use greenhouse PE films. Weight loss analysis showed mass losses of 5.2% for the PELW powder and 1.6% for the greenhouse PE film after treatment with wild-type PpMmcO. We propose a radical-mediated pathway catalyzed by PpMmcO. These findings identify a new bacterium and enzyme capable of initiating PE oxidation and provide insight into biological processes that may act on polyethylene.

microbiology↗

bioRxiv2026

The Endothelial Cell-Expressed Prion Protein Prnd/Doppel Promotes Neovascularization and Long-term Recovery after Ischemic Stroke

Ischemic stroke remains a leading cause of mortality and long-term disability, yet therapeutic options for promoting recovery remain severely limited. Here, we investigate the role of Prnd/Doppel, a prion family member, in stroke pathophysiology and recovery. Using middle cerebral artery occlusion (MCAO) in mice genetically null for Prnd (KO), inducibly overexpressing Prnd in endothelial cells (ECs), or wild-type (WT) controls, we assessed outcomes through infarct volume measurements, behavioral analysis, and immunohistochemical evaluation of vascular integrity and inflammation. While acute infarct volumes at 24 hours were comparable between WT and KO mice, striking differences emerged during recovery: KO mice exhibited significantly impaired functional outcomes at both 14 and 30 days post-MCAO, accompanied by disorganized cerebrovascular architecture, increased brain atrophy, and elevated CD68-positive inflammatory infiltration by day 30. Conversely, endothelial-specific Prnd overexpression, though not affecting acute outcomes, markedly enhanced tight junction protein expression at day 7, promoted angiogenesis, and improved long-term neuronal survival in the ischemic territory. These findings establish Prnd as a critical mediator of post-stroke vascular remodeling and functional recovery, distinguishing it from acute neuroprotective mechanisms. Our results identify Prnd as a promising therapeutic target for enhancing organized neovascularization and promoting sustained functional recovery following ischemic stroke, with potential applications to other neurological disorders characterized by cerebrovascular dysfunction.

neuroscience↗

bioRxiv2026

Cellular Chemical Dynamics Governing Signal Transduction and Adaptive Gene Expression: Beyond Classical Kinetics

Cellular adaptation is inherently nonstationary processes with complex stochastic dynamics1-5. Despite remarkable progress in quantitative biology6-11, a quantitative understanding of the cell adaptation dynamics in terms of the underlying cellular network remains elusive. Here, we present the next-generation chemical dynamics model and theory for cellular networks, providing an effective, quantitative description of the adaptive gene expression dynamics in living cells responding to external stimuli. Unlike conventional kinetics, chemical dynamics of cellular network modules are characterized by their reaction-time distributions, rather than by rate coefficients12. For a general model of cell signal transduction and adaptive gene expression, we derive exact analytical expressions for the time-dependent mean and variance of protein numbers produced in response to external stimuli, validated by accurate stochastic simulations. These results provide a unified, quantitative explanation of the stochastic responses of diverse E. coli genes to antibiotic stress and transcriptional induction. Our analysis reveals existence of a general quadratic relationship between the mean and variance of activation times across diverse genes. The gene activation process influences transient dynamics of downstream protein levels, but not their steady-state levels. In contrast, post-translational maturation process affects both transient dynamics and steady-state variability of mature protein levels. This finding indicates that the gene expression variability measured by fluorescent reporter proteins depends on the maturation time of the reporters. This work suggests a new direction for the development of digital twins of living cells.

biophysics↗

bioRxiv2026

MRE11 suppresses germline mutagenesis at meiotic double-strand breaks in mice

SPO11 forms hundreds of double-strand breaks (DSBs) to initiate meiotic recombination that is normally error-free. However, SPO11 activity can be mutagenic when one chromatid incurs closely spaced DSBs (double cuts), especially when DSBs are dysregulated by loss of the ATM kinase. De novo indels and structural variants can arise via end joining at double cuts within a single hotspot (microdeletions) or at adjacent hotspots separated by at least 30 kb, as we now show, sometimes accompanied by ectopic insertions of double-cut fragments. Here, we investigate how meiotic DSB end processing influences end joining. In MRE11-deficient mouse spermatocytes, which do not resect their DSBs, deletions at double cuts occur readily, with end-joining breakpoint profiles closely matching SPO11 DSB profiles. Microdeletions suggest that two DSBs can be as close as [~]21 bp. The tyrosyl DNA phosphodiesterase TDP2 contributes to both deletion formation and ectopic insertion of double-cut fragments, presumably by removing SPO11 from DNA ends prior to joining. Finally, observations suggest a cooperative role for MRE11 and ATM in locally regulating DSB distributions. Our findings provide insight into the mechanism of de novo mutation origin, emphasizing the role of meiotic DSBs in shaping genome evolution.

molecular biology↗

bioRxiv2026

Predicting Obstetric and Non-obstetric Diagnoses Co-occurrences during Pregnancy

Pregnancy care often involves simultaneous obstetric and other medical conditions, but their co-occurrence patterns are rarely modeled explicitly in a systematic, network-based approach. In this work, we formulate obstetric and non-obstetric diagnoses co-occurrences as a link prediction problem on a diagnosis-level homogeneous graph constructed from pregnancy encounters. Diagnoses are represented as nodes connected by co-occurrence edges, with node features capturing graph structure and demographic statistics3. We address this challenge by leveraging collected electronic health records data and study several standalone and hybrid graph neural network (GNN) architectures, including GCN, GAT, GraphSAGE, and three hybrid encoders that combine complementary aggregation mechanisms, namely GCN+GraphSAGE, GCN+GAT, and GAT+GraphSAGE. All models used consistent train-validation-test splits and are evaluated on 5- fold cross-validation sets. Among standalone models, GraphSAGE achieved the strongest performance, whereas hybrid GraphSAGE-based models (GCN+GraphSAGE and GAT+GraphSAGE) are best performers. The GCN+GraphSAGE hybrid, reaching an AUROC and AUPRC of approximately 0.90, consistently outperformed all other architectures. Further analysis of top-ranked predicted links revealed clinically plausible associations between pregnancy stage and risk-related diagnoses and common endocrine, metabolic, and hematological conditions. These findings indicate that graph-based link prediction may effectively prioritize obstetric and non-obstetric diagnosis pairs, providing a scalable framework for identifying clinically meaningful comorbidity patterns. They may further support hypothesis generation and downstream obstetric risk stratification efforts. AvailabilityAll codes including data preparation scripts, training and validation recipes, and experimental configurations are available at: https://github.com/kabir-ai2bio-lab/ob-nonob-diagnoses-cooccurrences.

bioinformatics↗

bioRxiv2026

Divergent phenotypic and functional roles of human T follicular helper cells from infancy to adulthood

Antibody responses to T-dependent antigens are suboptimal in young children, yet the evolution of T follicular helper cell (Tfh) function across the human lifespan remains poorly defined. Using human tonsils, a physiologically relevant and abundant source of Tfh, we investigated age-associated differences in their repertoire and functional programs. Pediatric tonsils were enriched for cytokine-expressing Tfh subsets with increased clonal diversity and phenotypic plasticity. However, in response to influenza antigens, they exhibited reduced Th1 polarization, diminished IL-21 production, and limited B cell help. Across ages, high neutralizing flu antibody responses were associated with robust Tfh1 activation, which was ICOS dependent in adults but not in children. Interestingly, Tfh depletion strategies revealed enhanced Tfh differentiation from distinct precursors in pediatric donors, yet antibody responses during early life were less reliant on Tfh help. Together, these findings define developmentally programmed differences in Tfh differentiation and function with implications for pediatric vaccine design.

immunology↗

bioRxiv2026

Disrupted Sleep-Dependent Neural Oscillations and Transcriptomic Changes in the Cacna1g Loss-of-Function Mouse Model Implicated in Schizophrenia

Sleep is an essential biological process for maintaining brain function, and impaired sleep microarchitecture is a well-established feature of schizophrenia (SCZ). Rare loss-of-function variants in CACNA1G, encoding the T-type calcium channel CaV3.1, confer substantial risk for SCZ, implicating sleep-regulatory circuits in disease pathophysiology. Here, we first show that among individuals with SCZ, carriers of rare CACNA1G missense variants exhibit more pronounced alterations in sleep neurophysiology than non-carriers, identifying a human phenotype suggestive of altered channel function. Motivated by this observation, we examined the consequences of Cacna1g loss of function in mice. Cacna1g deficiency produced profound disruptions in sleep microarchitecture, including reduced sleep spindles, altered slow oscillations, impaired spindle-slow oscillation coupling, and reduced cortical synchrony--closely recapitulating neurophysiological signatures observed in individuals with schizophrenia. Beyond sleep structure, Cacna1g loss disrupted the normal coordination of brain rhythms across the 24-hour cycle, most notably abolishing diurnal modulation of theta oscillations. At the molecular level, Cacna1g loss uncoupled sleep-wake state from gene and protein regulation, producing widespread, sleep-phase-biased transcriptional and synaptic alterations across brain regions and cell types, particularly in cortical excitatory neurons. These network and molecular disruptions were accompanied by behavioral hyperactivity during the active phase. Together, these findings establish CACNA1G/CaV3.1 as a key regulator linking sleep-dependent brain dynamics to molecular homeostasis and behavior, and provide a mechanistic framework through which genetic risk for SCZ may drive disease-relevant sleep and circuit dysfunction. One Sentence SummaryLoss of CACNA1G disrupts sleep rhythms and gene regulation, creating a mismatch between brain state and behavior linked to schizophrenia.

neuroscience↗

bioRxiv2026

BiomarkerKB: FAIR and Integrated Biomarker Knowledge Connecting Biomolecular and Clinical Data Types

Biomarkers are essential tools for disease detection, risk assessment, therapeutic monitoring, and precision medicine. However, biomarker data are dispersed across heterogeneous resources, inconsistently reported in the literature, and rarely standardized for computational use. This fragmentation limits reproducibility, cross-study integration, and the discovery of novel biomarker and disease relationships. We developed BiomarkerKB, a knowledgebase designed to harmonize and integrate biomarker information under a standardized data model. The model follows the FDA-NIH BEST biomarker definition and captures both core fields (biomarker entity, disease/condition, exposure agent) and contextual metadata (specimen, biomarker role, evidence, provenance). Biomarker data and related annotations were either curated from publications or collected from public resources (e.g., OpenTargets, GWAS Catalog, ClinVar, CIViC, OncoMX) and were also contributed by the Common Fund Data Coordinating Centers and the Early Detection Research Network (EDRN). Standardization was achieved using ontologies and reference resources such as Disease Ontology, UBERON, UniProtKB, and HUGO Gene Nomenclature Committee (HGNC) gene symbols. BiomarkerKB data were ingested into a Neo4j-based knowledge graph and integrated with the Common Fund Data Ecosystem (CFDE) Knowledge Graph. The initial release of BiomarkerKB contains over 200,000 biomarker-disease associations spanning genes, proteins, metabolites, glycans, and chemical elements. The knowledge graph comprises more than 300,000 nodes and 1.2 million edges, enabling structured exploration of biomarker relationships within CFDE data as demonstrated through the knowledge graph query-based use cases presented in this study. A publicly accessible web portal (https://biomarkerkb.org) provides keyword search, filtering, data downloads, and access to graph visualization to support both researchers and computational analyses. BiomarkerKB addresses a critical gap in biomarker informatics by providing an integrated, FAIR (Findable, Accessible, Interoperable, and Reusable), and unified framework for biomarker knowledge exploration and discovery.

bioinformatics↗

bioRxiv2026

Dual-antigen Doggybone DNA vaccine induces potent anti-tumor immunity against immunosuppressive oral cancer

Anti-PD1 blockade benefits only a subset of patients with head and neck squamous cell carcinoma (HNSCC), highlighting the need for approaches that overcome tumor immune resistance. Here, using the doggybone DNA (dbDNATM) platform, we developed CaVac OPT, an optimized dual-antigen DNA vaccine targeting MAGED4B and FJX1, which are overexpressed in most HPV-negative HNSCC and multiple solid tumors. In the MOC-2 oral cancer model, CaVac OPT significantly reduced tumor growth and when combined with anti-PD1 therapy, further delayed progression and improved survival. Immune profiling showed increased infiltration of CD4+ and CD8+ T cells, expansion of stem-like Tcf1+ populations, without increase in regulatory T cells, a reduced M2/M1 macrophage ratio and activation of interferon gamma associated pathways with suppression of tumor-promoting signals. These findings demonstrate that CaVac OPT reprograms the tumor microenvironment, converting cold HNSCC into T cell-inflamed responsive tumors. CaVac OPT represents a promising strategy for achieving durable control of aggressive, immunotherapy-resistant head and neck cancer.

cancer biology↗

bioRxiv2026

Deep Plasma Proteomics Coupled with Functional Genomics Reveals Drivers of Parkinson's Disease Progression and Levodopa Response

Parkinsons disease (PD) is a progressive neurodegenerative disorder lacking disease-modifying therapies, and its clinical management is limited by the absence of accessible biomarkers for tracking disease progression and treatment response. To map these complex disease trajectories, we implemented an ultra-deep plasma proteomics workflow integrating Mag-Net extracellular vesicle enrichment with Orbitrap Astral mass spectrometry to profile longitudinal samples from PD patients. This approach quantified 6,481 plasma proteins at unprecedented depth in PD studies, revealing distinct signatures directly associated with disease duration and dopaminergic therapy exposure. Candidate biomarkers were subsequently validated in an independent cohort using ELISA, demonstrating robust predictive utility in AI-driven prediction models. To uncover the mechanistic drivers underlying these systemic changes, we intersected our proteomic data with novel proteome-wide gene overexpression perturbation screens designed to identify regulators of alpha-synuclein pre-formed fibril (PFF) uptake and PFF-induced neuronal toxicity. Finally, an integrative network analysis combining three independent proteome-wide assays revealed that key pathological hubs, such as CD14, IFNG, and PLAT, are targets of currently approved pharmacological agents. Collectively, these findings provide a comprehensive, systems-level map for PD biomarker discovery and highlight druggable pathways to advance precision medicine strategies.

neuroscience↗

bioRxiv2026

NetCrafter: Ontology-derived gene network modeling and interpretation

Understanding the complex nature of multi-functional interactions among genes is crucial for interpreting omics data. We developed NetCrafter, an ontology-driven platform for constructing de novo gene networks that are specific to each input gene list and quantitatively defined by ontology-weighted similarity. By incorporating the probabilistic association of ontology or curated gene sets into a weighted Tanimoto similarity metric, NetCrafter transforms enrichment results into quantitative semantic similarity scores between genes, enabling the creation of context-specific statistical networks. These networks can be further decomposed into optimal sub-networks, facilitating multi-functional interpretation and the identification of gene interaction hotspots. NetCrafter also supports the integration of heterogeneous omics-derived gene lists through consensus ontology scoring. Importantly, this list-specific, quantitative framework reveals functional hotspots and target-biomarker relationships - even in cases where ontology terms alone are not predictive of node-level attributes such as CRISPR efficacy. NetCrafter provides an interactive platform for constructing and interpreting dynamic, context-specific gene networks, leveraging ontology-based functional associations to uncover underlying mechanisms and identify key nodes. It is freely available at https://netcrafter.sookmyung.ac.kr and integrated into Q-omics platform (https://qomics.ai) to enhance the utility of cancer omics data.

bioinformatics↗

bioRxiv2026

Comprehensive molecular profiling of single-cell proteome via gel electrophoresis and 3D single-molecule imaging

Recent advances in shotgun proteomics and immunoassays have yielded powerful single-cell proteomics technologies. However, current methods lack the sensitivity required to comprehensively quantify protein abundances in individual cells. Here, we present single-cell PAGE-PISA, an ultra-sensitive proteome profiling strategy that combines gel electrophoresis with 3D single-molecule fluorescence imaging. Our approach labels all proteins in single cells with fluorescent dyes, separates them by electrophoresis, and counts with single-molecule resolution. This technique quantified over 107 protein copies from a single mammalian cell with the sensitivity to detect low-abundance proteins down to 104 copies per species. Single-cell PAGE-PISA successfully classified cells into distinct cell types based on their proteomic profiles. Furthermore, our single-cell proteome data strongly correlated with predicted developmental states during cardiomyocyte differentiation, providing complementary information to single-cell transcriptome data. Together, single-cell PAGE-PISA enables highly sensitive and quantitative proteome profiling at the single-cell level, capturing subtle proteomic differences that distinguish diverse cellular states.

biophysics↗

bioRxiv2026

Spatial organization of mAbs in complex with the virus glycoprotein determines the success of intravenous rabies therapy

Rabies is a complex disease that has defied efforts to develop an effective treatment. Although it can be successfully prevented through vector control or pre- or post-exposure prophylaxis, no therapeutic options exist for symptomatic rabies, and the disease is still responsible for about 59 000 human deaths each year. Following our successful attempts to use the human monoclonal antibodies RVC20 and RVC58 for intracerebroventricular treatment of rabies, we report here on the effects of these mAbs when administered intravenously and intramuscularly to symptomatic mice. Only a small proportion of mAbs was detected to cross the blood-brain barrier, yet this limited amount sufficiently cleared the viral infection and improved the survival rate of infected mice. Remarkably, these mAbs appeared more effective when used individually than when combined as a cocktail. RVC58 achieved an 80% survival rate, potentially due to its distinct binding topography, potent viral neutralization, and enhanced Fc{gamma}R engagement in the presence of virions. These findings suggest that monotherapy with a single mAb fully capable of engaging Fc{gamma}R may be possible, which could substantially lower the costs and improve treatment accessibility in under-resourced settings, potentially contributing to improved equity of global health. Taken together, this study supports mAb therapy as a promising preclinical approach for symptomatic rabies and provides a foundation for further development of an accessible treatment option.

microbiology↗

bioRxiv2026

DelPi Learns Generalizable Peptide-Signal Correspondence for Mass Spectrometry-Based Proteomics

Peptide identification in mass spectrometry-based proteomics has traditionally relied on handcrafted features or simplified probabilistic approaches that limit the interpretation of structured peptide evidence. We present DelPi, an open-source peptide identification framework that learns generalizable peptide-signal correspondence from raw spectra through self-supervised pre-training followed by task-specific fine-tuning. With model distillation enabling practical deployment, DelPi expands the interpretation of peptide evidence across data-independent and data-dependent acquisition while maintaining robust false discovery control.

bioinformatics↗