Biology subjects

Kim, S.

Publications and source records attributed to Kim, S..

At least 91 records · Page 5Linked to original sources

bioRxiv2026

Early Visual Cortex Represents Sensory and Mnemonic Orientations in Separate Subspaces with Preserved Geometry

How early visual cortex (EVC) represents working memory (WM) content while continuing to process incoming sensory input remains unclear. Using fMRI with prolonged delays to isolate mnemonic activity, together with analyses of cross-decoding, low-dimensional subspace structure, and representational geometry, we examined the relationship between sensory and mnemonic orientation representations in human EVC. Cross-decoding generalized poorly between sensory and mnemonic epochs, but this did not imply unrelated codes. Rather, the two occupied separable low-dimensional subspaces while preserving representational geometry across epochs. During discrimination and estimation, sensory- and mnemonic-trained decoders yielded dissociable readouts of concurrent sensory and mnemonic information from the same EVC measurements. Mnemonic coding showed little dependence on the retinotopic radial bias that characterized sensory coding, and trial-by-trial variability in mnemonic representation predicted both discrimination choices and estimation reports. Our findings support a population-level account in which mnemonic information in EVC is re-expressed in a separable but geometrically preserved format.

neuroscience↗

bioRxiv2026

High-variance phenome database reveals important roles of WD40 proteins in the plant pathogenic fungus Fusarium graminearum

WD40 is a highly conserved protein domain in eukaryotes, playing a critical role in various cellular process. We conducted genome-wide functional analysis of WD40 genes in Fusarium graminearum-a phytopathogenic fungus that causes severe yield loss and mycotoxin contamination in major cereal crops. Comprehensive phenome analysis of 119 WD40 gene deletion mutants across 22 distinct phenotypic traits revealed phenotypic divergence within the phenome, establishing a strong correlation between virulence and sexual reproduction. Notably, 21 core WD40 genes were identified, offering valuable insights into divergent biological processes. Pilot interactome studies of Fgwd101 and Fgwd133 provided further insights into their potential pathobiological functions. Our investigation contributes to broadening our knowledge of the biological mechanisms underlying fungal pathogenesis and may assist in the identification of targets for antifungal agents.

molecular biology↗

bioRxiv2026

Coordinated Tuning of Ionizable Lipids and Formulation Redirects mRNA Vaccines Toward Lymphoid-Specific CD4+ T Cell Immunity

The global success of mRNA vaccines has underscored the pivotal role of lipid nanoparticles (LNPs), yet how subtle chemical variations in ionizable lipids and their formulation parameters orchestrate complex immune landscapes remains largely elusive. Here, we report a novel ionizable lipid, N4Z, and demonstrate that its distinct chemical signature selectively intensifies early innate immune programs compared to its structural analogue, N4Y. Single-cell transcriptomic profiling at the injection site reveals that N4Z-based LNPs uniquely prime inflammatory and type I interferon-related transcriptional programs, accompanied by a rapid influx of B and CD4+ T cells. Beyond lipid chemistry, we show that formulation-level tuning, that is independent of the ionizable lipid structure, can reshape the systemic biodistribution from hepatic dominance toward lymphoid tissues. This optimization substantially enhances macrophage-associated antigen expression, which in turn amplifies polyfunctional CD4+ T cell responses, T follicular helper cell differentiation, and germinal center reactions. Our findings establish that the coordinated interplay between lipid engineering and formulation design provides a programmable platform for precision mRNA vaccination, achieving superior protective efficacy and neutralizing activity over clinically validated benchmarks.

bioengineering↗

bioRxiv2026

Lifetime-based multiplexed detection of viral RNA using fluorogenic aptamers

Fluorogenic aptamers (FAPs) are emerging molecular probes for viral RNA and DNA sensing. However, their use in multiplexed nucleic acid sensing has been hindered by cross-reactivity and overlapping emission spectra. Here we address these limitations by introducing a fluorescence-lifetime-based multiplexed detection strategy using variants of the DNA fluorogenic aptamer Lettuce that exhibits distinct fluorescence lifetimes when complexed with the fluorogen TO1-biotin. To effectively evolve Lettuce for diverse lifetimes, we developed a large-scale screening platform, termed FAP-FLIM-NGS (fluorogenic aptamer-based fluorescence lifetime imaging microscopy on next-generation sequencing chips), which measures the fluorescence lifetimes of [~]104 Lettuce/TO1-biotin complexes directly on an Illumina MiSeq flow cell. Using this approach, three variants with markedly different lifetimes were identified: a single mutant (smC14T, 6.0 ns) and two double mutants (dmA5T/C14T, 5.2 ns, and dmA5T/T22A, 4.4 ns). To demonstrate the utility of these Lettuce variants in multiplexed detection, a set of split Lettuce probes targeting viral RNA fragments derived from SARS-CoV-2, MERS-CoV, and influenza A were designed and tested. Phasor plot analysis confirmed that these probes can robustly distinguish individual targets as well as mixtures containing any two or all three targets purely based on distinct fluorescence lifetimes of probes, thereby overcoming the challenges of cross-reactivity and spectral overlap. Beyond this proof of concept, our findings establish a generalizable strategy for engineering FAPs with customized photophysical properties, opening new avenues for next-generation diagnostics and molecular sensing technologies.

bioengineering↗

bioRxiv2026

sigNATURE maps cohort-specific T-cell states to reproducible programs of ICI response

Immune checkpoint inhibitors (ICIs) can induce durable responses across cancers, yet T-cell biomarkers of response remain difficult to reproduce across single-cell RNA-seq studies. A major reason is that T-cell states are typically defined de novo within each cohort, making reported marker genes sensitive to cohort composition and analytic choices rather than stable cellular programs. Here we present sigNATURE (signature Normalization and Atlas-based T-cell Understanding for Reproducibility and Evaluation), a reference-guided framework that maps query cells onto large CD4+ and CD8+ T-cell atlases, evaluates published ICI-response markers in an atlas-aligned coordinate system, and quantifies the atlas support of mapped cells through a cell-level identifiability score. We applied sigNATURE to two independent ICI scRNA-seq cohorts comprising 36 non-small cell lung cancer patients and 15 skin cancer patients (11 basal cell carcinoma and 4 squamous cell carcinoma). Across cohorts, sigNATURE-derived features more robustly resolved response-associated T-cell structure than cohort-derived state definitions, yielding clearer unsupervised separation of responders and non-responders, enabling integrated analysis of independent studies in a shared atlas-aligned space, and improving mean response-prediction AUC from 0.469 to 0.746. Using identifiability score, we further identify terminally differentiated effector CD8+ T cells and regulatory CD4+ T cells as prominent response-associated states across studies, prioritizing published markers in terms of robust, atlas-resolvable cell states. Using this framework. Together, these results establish sigNATURE as a framework for improving the reproducibility, cross-cohort comparability, and mechanistic interpretability of single-cell ICI biomarkers.

immunology↗

bioRxiv2026

Concordant transcriptional and morphological remodeling revealed by in vivo Perturb-CLEAR

The principle that form follows function has long guided thinking in biology and architecture alike. In the nervous system, however, form does more than reflect function: neuronal morphology actively constrains input patterns, synaptic integration, and circuit wiring. During postnatal neurodevelopment, dendritic architectures are assembled and remodeled through genetically encoded programs and activities, transforming molecular programs into circuit architecture. However, dendritic morphogenesis has been difficult to quantify at scale while systematically testing how genetic variants, including neurodevelopmental disorder (NDD) risk genes, alter these structures in vivo. We developed Perturb-CLEAR, which integrates pooled CRISPR screening and whole-mount imaging to quantify brain-wide cytoarchitecture, and paired it with Perturb-seq to link structural phenotypes to transcriptomic changes. Applying Perturb-CLEAR to the developing mouse cortex revealed morphogenesis trajectories accompanied by transcriptomic dynamics. Moreover, systematic perturbation of NDD risk genes uncovered gene-specific multimodal phenotypes. Adnp perturbation remodels basal dendrites in L4/5 IT (intratelencephalic) neurons but not other dendritic compartments or cell types, alongside consistent transcriptional shifts. Combined morphology and transcriptome analyses link NDD risk genes to concordant multimodal cellular phenotypes in the developing brain, highlighting diverse paths of perturbation effect propagation across modalities.

neuroscience↗

bioRxiv2026

Resolution of recursive data corruption to transform T-cell epitope discovery

Accurate prediction of MHC class I-presented peptides is essential for any vaccine or T-cell therapy design, yet reported gains on in silico benchmarks have not translated into clinical successes. Here we show that this discrepancy may come from a common methodological error: immunopeptidomics datasets are fundamentally contaminated by existing prediction models through prediction-based deconvolution and filtering, resulting in an iterative confirmation bias. An audit of the IEDB, the biggest database in the field, reveals that as of January 2025, 55.8% of assessable data are labeled by computational models rather than verified experimentally. This inflates in silico benchmarks while degrading real-world applicability on new data, effectively making it impossible to objectively test model performance, which can lead to choosing suboptimal solutions and decreasing the chance of any therapys clinical success. In silico simulation shows that iterative data corruption maintains high AUROC while top-of-list retrieval collapses. We reframe epitope discovery as a protein-centric learning-to-rank task and introduce deepMHCflare, a model evaluated exclusively on clean data. deepMHCflare achieves 0.80 Precision@4 on mono-allelic benchmarks versus 0.55-0.65 for gold-standard prediction models. A preclinical cancer vaccine study validated that 2 of the 4 deepMHCflare-nominated peptides were immunogenic, with a third independently confirmed in the literature.

bioinformatics↗

bioRxiv2026

Retrosplenial cortex vulnerability links severe hypoglycemia to cognitive impairment through neuron-microglia crosstalk

Severe hypoglycemia remains a serious adverse effect of insulin therapy in individuals with diabetes and is linked to cognitive decline, yet the mechanisms by which transient metabolic stress leads to persistent neuronal dysfunction remain poorly defined. Using mouse models of acute severe hypoglycemia and integrated screening, we identified the retrosplenial cortex as a previously unrecognized brain region that is particularly vulnerable to hypoglycemia-induced neuronal damage. This injury is driven by a feedforward interaction between neuron-specific Drp1-dependent mitochondrial fission and microglial IL-1 signaling, as pharmacological or genetic targeting of either pathway suppressed the other, rescued neuronal damage, and reversed cognitive impairment. These findings identify a region-specific neuron-microglia injury circuit that links severe hypoglycemia to cognitive dysfunction and suggest a therapeutic strategy to protect brain function without compromising diabetes management.

neuroscience↗

bioRxiv2026

Refined USP25/28 inhibitors with improved selectivity towards c-Myc driven squamous lung cancer cells

The ubiquitin specific protease 28 (USP28) is implicated in tumorigenesis by controlling the turnover of substrates including the oncogene c-MYC and the ubiquitin ligase FBW7. Here, we describe small molecule inhibitors of USP25 and USP28, leading to cancer cell cycle arrest and death. However, genetic deletion of USP25/28 does not replicate this effect. An integrated -omics approach revealed off-target effects for thienopyridine and thienopyrazine carboxamide compounds in protein translation. Chemoproteomics and biochemical analyses suggested binding of such compounds to a region near the ribosome complex polypeptide exit tunnel. Structural analysis of a USP28-inhibitor complex enabled the design of modified USP25/28 inhibitor molecules which minimized translation-related off-target effects. In distinction to earlier compounds, the optimized inhibitors were non-toxic to breast cancer cells yet retained potent anti-proliferative activity in squamous lung carcinoma cells, where USP28 is associated with disease progression. Together, our results demonstrate that refined USP25/28 inhibitors can selectively suppress tumor growth by targeting c-MYC driven pathways, offering a more precise therapeutic strategy for treating squamous lung cancers whilst minimizing undesired cytotoxicity.

cancer biology↗

bioRxiv2026

Mechanotherapeutic Potential of Survivin in Glioblastoma

Glioblastoma Multiforme (GBM) is a highly aggressive brain cancer characterized by rapid proliferation and extensive remodeling of the extracellular matrix (ECM), leading to progressive tissue stiffening. Although ECM stiffness is known to promote GBM progression, the molecular mechanisms linking mechanical cues to tumor growth remain insufficiently defined. In this study, transcriptomic comparison of GBM tumors and non-neoplastic brain tissue revealed coordinated upregulation of cell cycle regulators and matrisome-associated genes, with survivin (BIRC5) identified as a central node linking proliferative signaling and ECM remodeling networks. Analysis of GBM patient specimens further showed strong nuclear survivin expression in regions with elevated collagen deposition. To directly evaluate stiffness-dependent regulation of survivin, GBM cells were cultured on fibronectin-infused hydrogels with tunable stiffness. Stiff matrices increased survivin expression along with cyclin D1 and cyclin A, consistent with increased cell cycle progression. Pharmacologic inhibition or siRNA-mediated suppression of survivin reduced stiffness-induced proliferation and attenuated expression of matrisome components, including collagens and lysyl oxidase. These findings indicate that survivin functions as a mechanosensitive regulator that coordinates cell cycle progression with ECM production in stiff tumor microenvironments. Collectively, this study identifies survivin as a key mediator linking ECM stiffness to GBM growth and matrisome remodeling. Targeting survivin and its effectors may offer a mechanosensitive strategy to limit GBM growth.

pathology↗

bioRxiv2026

X-Cell: Scaling Causal Perturbation Prediction Across Diverse Cellular Contexts via Diffusion Language Models

Causal models of cellular systems hold the promise to empower broad biological discovery, including the systematic identification of novel targets for drug discovery. Predicting how genetic and pathway perturbations reshape gene expression across diverse cellular contexts is a prerequisite for building generalizable cellular foundation models. However, current methods typically fail to extrapolate beyond their training distributions because they rely predominantly on observational expression atlases rather than interventional perturbation data. We present X-Atlas/Pisces, the largest genome-wide CRISPRi Perturb-seq compendium to date, comprising 25.6 million perturbed single-cell transcriptomes across 16 biologically diverse contexts, including widely used cell lines, induced pluripotent stem cells (iPSCs), resting and CD3/CD28 activated Jurkat T lymphoma cells, and multi-lineage differentiating iPSCs. Leveraging this resource, we develop X-Cell, a diffusion language model that predicts perturbation responses by iteratively refining control-to-perturbed state transitions through cross-attention to multi-modal biological priors derived from natural language, protein language models, interaction networks, genetic dependency maps, and morphological profiles. X-Cell outperforms existing state-of-the-art models by up to five-fold on key metrics such as Pearson{Delta} (correlation between predicted and observed perturbation-induced log-fold changes), and demonstrates zero-shot prediction of T cell inactivating perturbations in stimulated Jurkat cells. We scale X-Cell to 4.9 billion parameters (X-Cell-Ultra), the largest causal perturbation model to date. We demonstrate for the first time that perturbation prediction follows power-law scaling with an exponent matching large language models. X-Cell-Ultra demonstrates zero-shot generalization to novel biological contexts, including unseen iPSC-derived melanocyte progenitors and primary human CD4+ T cells from multiple donors, and outperforms all baselines after self-supervised test-time adaptation. These results demonstrate that coordinated scaling of causal perturbation data and model capacity yields foundation models capable of generalizable perturbation prediction across cellular contexts, with potential applications for improving computational target identification, validation, and context-specific therapeutic prioritization.

systems biology↗

bioRxiv2026

Network-targeted TMS modulates task-related striatal activity during motor skill learning

The striatum, a critical hub for motor skill learning, is located deep within the subcortical region, making noninvasive stimulation particularly challenging. Nevertheless, recent studies suggest that transcranial magnetic stimulation (TMS) can modulate subcortical activity indirectly by targeting functionally connected cortical areas. In this study, we applied TMS to the dorsolateral prefrontal cortex (DLPFC) immediately before the fMRI session measuring task-related activity in the striatum during motor learning. We examined whether continuous theta-burst stimulation (cTBS) and high-frequency stimulation (20 Hz) could modulate motor learning and associated striatal responses with opposing effects. There was no significant effect of either stimulation condition on the overall motor learning performance. However, cTBS significantly reduced performance-related striatal activity, while 20 Hz stimulation did not show any modulatory effect. These findings demonstrate that cTBS targeting the corticostriatal network can suppress striatal activity and suggest its potential use in clinical trials for treating disorders such as addiction associated with hyperactive striatal responses.

neuroscience↗

bioRxiv2026

Genome-scale functional mapping of the mammalian whole brain with in vivo Perturb-seq

Functional genomics studies have provided critical insights into cell type-specific gene regulatory programs, but to date most have been conducted in wild-type tissues or cell cultures. Here, we present a gene expression functional atlas across the mouse brain. We use an enhanced in vivo Perturb-seq platform to analyze transcriptome-wide responses to loss of 1,947 disease-associated genes, profiling over 7.7 million cells spanning major brain regions and neuronal populations. We find striking cell-type-specific essentiality and transcriptional programs and show that closely related disease genes such as two NMDA receptor subunits can drive opposing transcriptional programs. Together, this work reveals insights into the genetics and mechanisms of neurodevelopmental, psychiatric, and neurodegenerative diseases in vivo, paving the way for the design of future genetic medicine.

genomics↗

bioRxiv2026

Emerging invasion risks of non-native urban trees in continental Europe under a changing climate

Urban green areas often harbour numerous non-native urban trees, many of which have characteristics that predispose them to escape from cultivation and become potentially invasive. Climate change is expected to exacerbate this risk by creating favourable conditions for species that are currently climatically restricted. The potential risks for invasiveness of urban tree species in continental Europe are not yet known. Here, we provide a comprehensive risk screening of 34 non-native urban tree species in continental Europe, for both current and projected future climate scenarios. Using the Terrestrial Plant Species Invasiveness Screening Kit (TPS-ISK v2.4), we assessed invasion risk based on biogeography, ecology, and projected responses to climate change. Results showed that under current conditions, 10 species (29.4%) were categorised as high risk, 23 (67.6%) as medium risk and one (2.9%) as low risk. The inclusion of climate change projections increased the number of high risk species to 11, with seven species categorised as very high risk. These taxa exhibit strong ecological plasticity, high reproductive performance and broad environmental tolerance, which together with projected warming, emphasises their significant potential for further spread. Our results emphasise the urgent need for early detection, continuous monitoring and proactive management of non-native urban trees in Europe, especially those that are widely used in horticulture and forestry. By integrating invasion biology with climate change risk screening, this study provides an important basis for evidence-based policy and management strategies to mitigate future ecological and economic impacts of invasions by urban trees.

ecology↗

bioRxiv2026

A network-based deep learning model integrating subclonal architecture for therapy response prediction in cancer

Predicting treatment response remains challenging in oncology, particularly given the growing diversity of therapeutic options. Despite efforts using gene expression signatures, or integrative multi-omics frameworks, robust and interpretable biomarkers remain limited. We present SubNetDL, a deep learning framework that integrates subclonal mutation profiles and protein-protein interaction networks via network propagation. Unlike condition-specific approaches, SubNetDL leverages somatic mutations alone and is applicable across diverse cancer types and treatment modalities. Applied to ten TCGA cancer-drug combinations, SubNetDL achieved consistently strong performance (median AUROC = 0.74) and successfully generalized to two independent immunotherapy datasets (median AUROC = 0.77). Importantly, it identified candidate biomarker genes with treatment-specific relevance. SubNetDL prioritized genes that were not central in the network, highlighting its ability to capture context-specific patterns beyond traditional metrics. In conclusion, our approach offers a robust and interpretable framework for identifying predictive biomarkers and stratifying patients based on mutation profiles and network context. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/711567v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@d31c74org.highwire.dtl.DTLVardef@1f12b13org.highwire.dtl.DTLVardef@430916org.highwire.dtl.DTLVardef@109a13c_HPS_FORMAT_FIGEXP M_FIG C_FIG MotivationIntratumoral heterogeneity is a fundamental driver of therapeutic resistance, yet most predictive models rely on aggregate mutational burdens or static gene expression signatures, overlooking the subclonal dynamics that shape treatment outcomes. While network biology offers a functional lens to interpret genomic alterations, a framework that explicitly bridges subclonal architecture with system-level molecular interactions has been lacking. To address this, we developed SubNetDL, a deep learning framework that integrates patient-specific subclonal profiles with protein-protein interaction networks. By leveraging only somatic mutation data, SubNetDL captures the functional convergence of subclonal evolution, providing a robust and interpretable platform for patient stratification and biomarker discovery across diverse oncological contexts.

cancer biology↗

bioRxiv2026

Age-dependent pathogenicity of two severe fever with thrombocytopenia syndrome viruses in a ferret model

SFTSV is an emerging tick-borne pathogen associated with high case fatality rates, particularly in elderly patients. While severe pathogenicity has been reported in aged ferrets, lethal or clinically significant infection in younger animals and genotype-dependent differences in pathogenicity remain insufficiently defined. In this study, we established a ferret infection model using two Korean clinical isolates representing genotypes B and F and systematically compared disease progression between one-year-old and three-year-old ferrets. Three-year-old ferrets exhibited rapid fever onset, marked body weight loss, early clinical deterioration, severe thrombocytopenia and leukopenia, significant elevations in AST and ALT levels, and earlier peak viremia with higher tissue viral loads, indicating impaired early viral control and accelerated systemic dissemination. Notably, one-year-old ferrets also developed measurable pathogenic manifestations, including febrile responses, progressive weight loss, detectable viremia, and multiorgan viral distribution, although disease progression was delayed and less severe compared with older animals. Within the same age group, differences in pathogenicity between genotypes B and F were limited. These findings demonstrate that host age is a major determinant of SFTSV disease severity and support the use of an age-stratified ferret model for preclinical evaluation of vaccines and antiviral therapeutics. ImportanceSFTS is an emerging tick-borne disease that can cause high fever, thrombocytopenia, and multi-organ failure, with particularly severe outcomes in older adults. Currently, no approved vaccines or specific antiviral treatments are available. Reliable animal models that recapitulate human disease are therefore essential for the development of effective countermeasures. Ferrets have recently been proposed as a useful model for SFTS, especially in aged animals, but the susceptibility of younger ferrets and the impact of viral strain differences remain unclear. Here we show that host age strongly determines disease severity in ferrets infected with two genetically distinct SFTS virus strains, establishing a flexible animal model for evaluating vaccines and antiviral therapies.

microbiology↗

bioRxiv2026

Mechanistic insights into the color transformation of a non-FRET substrate for RNase activity detection

DNA-templated silver nanoclusters (DNA/AgNCs) have created a new class of non-FRET DNase substrates, termed Subak, that exhibits a color change upon DNase digestion. Although Subak substrates offer advantages such as ratiometric readouts and low manufacturing costs over traditional FRET substrates, the mechanism governing AgNC color switching remains unclear. Here, using a site-specific cleavage strategy, we identify color-switching hotspots and demonstrate that AgNC transformation can be controlled by the cleavage positions within the nucleic acid host. Our data support a cleavage-driven reorganization of the AgNC coordination environment, converting a non-emissive precursor into a red-emitting cluster, rather than direct enzyme-cluster interactions. Leveraging this insight, we engineer rSubak, an RNA-incorporated Subak that displays 95 nm red shift (530 to 625 nm) upon RNase cleavage. In amplification-free CRISPR/Cas13 assays for SARS-CoV-2, influenza A (A/H5N1), and measles viruses (MV) detection, rSubak achieved a limit of detection of 0.3 pM, superior to that of the commercial RNaseAlert ([~]250 pM). Collectively, our results establish Subak as a generalizable, non-FRET platform for sensitive ratiometric reporting the activities of diverse nucleases.

bioengineering↗

bioRxiv2026

3D chromatin compartment of round spermatids encodes the spatiotemporal program of histone-to-protamine exchange in spermiogenesis

Sperm formation requires a radical chromatin reorganization, where nucleosomes are replaced by transition proteins (TNPs) and subsequently by protamines (PRM1 and PRM2). Although essential for fertility, the regulatory logic governing this exchange is unknown, but its presumed to be stochastic and unregulated. Using endogenously tagged PRM mouse models and stage-resolved, genome-wide profiling, we revise the order of histone-to-protamine exchange and show that the chromatin remodeling process is highly programmed. Imaging and biochemical experiments reveal a direct histone-to-PRM1 exchange, while TNPs appear after PRM1 but precede PRM2 incorporation. This temporal uncoupling of PRM1 and PRM2 incorporation coincides with dynamic, region-specific chromatin remodeling that is not governed by histone acetylation but is instructed by the three-dimensional nuclear architecture of round spermatids. Therefore, by integrating ATAC-seq, CUT&Tag, and Hi-C, we define a compartment-encoded "blueprint" that prescribes the assembly of the mature sperm epigenome and establishes a complex molecular hierarchy for the histone-to-protamine exchange.

genomics↗