Biology subjects

Chen, T.

Publications and source records attributed to Chen, T..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

Efficient Generation of Humanized Cloned Cattle via Embryonic Stem Cell-Mediated Dual-Round Site-Specific Editing

Transgenic cloning allows genetic modification in cattle, yet its translational potential is constrained by low cloning efficiency and limited gene-editing capacity, due largely to the restricted proliferative ability of somatic donor cells. Pluripotent stem cells (PSCs), with their unlimited self-renewal ability, represent a promising alternative to overcome these bottlenecks. Here, we first established stable bovine epiblast stem cell lines (bEpiSCs) and achieved site-specific integration of the HCSN2-HLF-GFP cassette into the {beta}-casein locus, with high transfection (72.07%) and correct integration efficiency (80.48%). Following Cre LoxP mediated excision of the selectable marker, we obtained marker free HCSN2-HLF bEpiSCs that retained pluripotency markers, tri lineage differentiation potential, and supported target protein expression in immortalized bovine mammary epithelial cells. When used as nuclear donors, these cells produced blastocysts with quality comparable to in vitro fertilization (IVF) embryos. Notably, cloning with HCSN2-HLF bEpiSCs resulted in the birth of twelve calves, with significantly higher birth and survival rates (30.71% and 16.74%, respectively) than those obtained using fibroblasts. Both PCR-based genotyping of the edited loci and microsatellite kinship analysis confirmed that all cloned calves were genetically derived from the HCSN2-HLF bEpiSCs. Collectively, this study establishes an efficient embryonic stem cell based platform for targeted gene editing and rapid production of multigene edited cloned cattle, offering a scalable strategy with strong potential for industrial translation.

bioengineering↗

bioRxiv2025

Metabolic dysfunction related to specific genetic variant sites of methylmalonic acidemia

Methylmalonic acidemia (MMA) is a genetic metabolic disease, with mut (mut-MMA, caused by MMUT gene variation) and cblC (cblC-MMA, caused by MMACHC gene variation) being the predominant subtypes. MMA patients carrying c.729_730insTT and c.609G>A exhibit severe clinical manifestations and have poorer outcomes. Little information is known on the potential mechanism. The metabotypes of MMA patients carrying c.729_730insTT and c.609G>A were profiled. The potential effect of genetic variation sites on the metabolism were explored. Leveraging the gene variation spectrum and the targeted metabolomics, propionic acid, indolelactic acid, myristoleic acid, tetradecenoic acid, tridecanoic acid, and oxoadipic acid showed strong enrichment in c.729_730insTT carrying mut-MMA patients. Whereas mutation c.609G>A caused adipic acid, pimelic acid, fructose, AMP, hexanylcarnitine, and methionine significnatly altered in cblC-MMA patients. Furthermore, the metabolomic profiles were obviously different in c.729_730insTT and non-c.729_730insTT carrying mut-MMA patients after treatment. Different metabolic alterations in cblC-MMA patients carrying c.609G > A and non-c.609G > A under treatment were also observed. Our findings provide unique insights into the metabolic basis of MMA specific subtypes and its implications across different variants.

molecular biology↗

bioRxiv2025

A Unified Mechanism for Depth Perception and Figure-Ground Organization

Understanding how the visual system unifies depth perception and figure-ground organization remains a central challenge in vision science. Stereoscopic disparity and border ownership have long been treated as distinct processes, yet both arise with similar timing in area V2 and exhibit strikingly parallel properties. Here we propose a unified mechanism that integrates both within a single hierarchical framework. Building upon the layered disparity representation introduced in our prior work, we show that border ownership--serving as the key component of figure- ground organization--emerges intrinsically from thresholded relative-disparity differencing, providing a unified basis for contour-based segmentation and depth perception. Using biologically constrained network models (TcNet), we demonstrate that this mechanism generalizes across binocular and illusory depth domains, bridging physical and non-epipolar disparity cues. At the systems level, we identify complementary dual feedback pathways from area V4: V4[->]V1, which refines and re-weights layered disparity maps, and V4[->]V2, which integrates global contextual priors such as shape continuity and category consistency. Together, these recurrent loops balance fine-scale depth precision with large-scale figure-ground coherence, offering a unified explanation for perceptual alternations such as the Rubin Face-Vase and Kanizsa illusions. This framework integrates intrinsic disparity computation, border-ownership dynamics, and hierarchical feedback modulation into a coherent, biologically plausible model. It provides a mechanistic account of how early visual areas interact with higher cortical feedback to achieve stable yet flexible perception of depth and figure-ground organization within a unified temporal cycle.

neuroscience↗

bioRxiv2025

Synchronized Latency Reversal and Immune Clearance by a Multifunctional Fusion Protein Enables HIV-1 Reservoir Reduction

Strategies that concurrently reactivate latent reservoirs and enhance immune-mediated clearance hold significant promise for achieving an HIV cure. Here, we developed hyperIL-15xsCD4-Fc (15xsCD4-Fc), a fusion protein that integrates latency reactivation, targeted immune engagement, and effector-mediated killing into a single molecule. This agent not only potently reverses HIV-1 latency in CD4+ T cells from people living with HIV-1 (PLWH) through coordinated IL-15 receptor signaling and sCD4-mediated HIV-1 envelope (Env) engagement, but also enhances antigen-specific CD8+ T cell response in PBMCs derived from PLWH. Furthermore, 15xsCD4-Fc enables Env-specific elimination of reactivated latently infected cells by NK cells while preventing off-target cytotoxicity. In PBMCs from PLWH, 15xsCD4-Fc reduced replication-competent HIV-1 DNA by 93.8%. In antiretroviral treated HIV-1-infected humanized mice, the molecule demonstrated both safety and efficacy in diminishing viral reservoir in lymphoid organs. This spatiotemporally coupled approach to reservoir exposure and immune recognition establishes a clinically viable strategy for clearing the HIV-1 reservoir. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=195 SRC="FIGDIR/small/693434v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@8f8f0org.highwire.dtl.DTLVardef@10f63f7org.highwire.dtl.DTLVardef@16c1de2org.highwire.dtl.DTLVardef@11e1787_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

GatorSC: Multi-Scale Cell and Gene Graphs with Mixture-of-Experts Fusion for Single-Cell Transcriptomics

Single-cell RNA sequencing (scRNA-seq) enables high-resolution characterization of cellular heterogeneity, but its rich, complementary structure across cells and genes remains underexploited, especially in the presence of technical noise and sparsity. Effectively leveraging this multi-scale structure is essentially an information fusion problem that requires integrating heterogeneous graph-based views of cells and genes into robust low-dimensional representations. In this paper, we introduce GatorSC, a unified representation learning framework that models scRNA-seq data through multi-scale cell and gene graphs and fuses them with a Mixture-of-Experts architecture. GatorSC constructs a global cell-cell graph, a global gene-gene graph, and a local gene-gene graph derived from neighborhood-specific subgraphs, and learns graph neural network embeddings that are adaptively fused by a gating network. To learn noise-robust and structure-preserving embeddings without labels, we couple graph reconstruction and graph contrastive learning in a unified self-supervised objective applied to both cell- and gene-level graphs. We evaluate GatorSC on 19 publicly available scRNA-seq datasets covering diverse tissues, species, and sequencing platforms. Experiments showed that GatorSC consistently outperforms state-of-the-art deep generative, graph-based, and contrastive methods for cell clustering, gene expression imputation, and cell-type annotation. The learned embeddings are used for accurate trajectory inference, recovery of canonical marker gene programs, and cell-type-specific pathway signatures in an Alzheimers disease single-nucleus dataset. GatorSC provides a flexible foundation for comprehensive single-cell transcriptomic analysis and can be readily extended to multi-omic and spatial modalities.

bioinformatics↗

bioRxiv2025

Structure of human lymphoid-specific helicase HELLS in its autoinhibitory state

Helicase, Lymphoid Specific (HELLS), also known as Lymphoid-Specific Helicase (LSH), is a member of the SNF2 chromatin-remodeling family that regulates DNA methylation and heterochromatin organization. Unlike most chromatin remodelers, HELLS is catalytically inactive in its apo form and requires the DNA-binding protein CDCA7 for activation, though the underlying mechanism has remained unclear. Here, we combine biochemical, biophysical and cryo-electron microscopy analyses to define the structural basis of HELLS autoinhibition. HELLS alone assembles into a hexameric (trimer of dimers) architecture stabilized by interactions between its N-terminal coiled-coil (CC) domain and ATPase Lobe-1, while ATPase Lobe-2 remains flexible and disengaged. The CC domain functions both as an oligomerization scaffold and as an autoinhibitory module that restricts catalytic activity. Binding of CDCA7 and DNA promotes formation of an active HELLS-CDCA7-DNA ternary complex. CDCA7 recognizes hemimethylated CpG dinucleotides in both B-form and non-B-form DNA and stimulates HELLS ATPase activity. Together, these findings reveal the mechanism of HELLS autoinhibition and its activation by CDCA7 and DNA, providing new insight into how the HELLS-CDCA7-DNA ternary complex maintains DNA methylation and heterochromatin integrity. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/692698v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@106b73borg.highwire.dtl.DTLVardef@1e7b33aorg.highwire.dtl.DTLVardef@a7be6eorg.highwire.dtl.DTLVardef@c75a5b_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIHELLS alone assembles into an inactive hexameric trimer-of-dimers C_LIO_LIThe N-terminal coiled-coil mediates oligomerization C_LIO_LICDCA7 and DNA binding activate the HELLS-CDCA7-DNA complex C_LIO_LIThe complex recognizes hemimethylated CpG in non-B-form DNA C_LI

molecular biology↗

bioRxiv2025

Latent gene network expression underlies the reversion of a polyphenic trait in the worker caste of ants

Polyphenisms--where alternative phenotypes develop from a single genome in response to environmental cues--are not only widespread in nature, but also occur at multiple levels of biological organization, from cells to individuals to societies. Polyphenism is thought to promote phenotypic diversification through the gain, loss, and re-evolution of alternative phenotypes. After the origin of a polyphenism, one of the alternative phenotypes often retains the developmental capacity to produce the ancestral trait, thereby permitting the other to evolve rapidly. Yet, little is known about the developmental processes underlying the re-evolution of polyphenic traits and how they may produce phenotypic diversification. Here we address this question by focusing on the caste polyphenism in ant societies, which produces a winged queen caste and a wingless worker caste in a single colony in response to environmental cues. We show, in a hyperdiverse group of ants, that a caste-specific trait called the ocelli (3 simple eyes on the dorsal head) is always present across queen castes but was lost and partially re-evolved multiple times, giving rise to novel patterns (1 ocelli) in the worker castes. Surprisingly, we discovered that a hidden (latent) expression of the ocelli gene regulatory network in worker castes that lost ocelli underlies the partial re-evolution of ocelli in this group. We therefore propose that latent developmental potentials may generally persist across polyphenic systems, including ant castes, and may facilitate the partial re-evolution of novel phenotypic patterns.

evolutionary biology↗

bioRxiv2025

Hidden-driver inference reveals synergistic brain-penetrant therapies for medulloblastoma

Effective therapies for high-risk medulloblastoma (MB), particularly MYC-driven Group 3 (G3) MB, remain elusive due to limited druggable mutations, poor blood-brain barrier (BBB) penetration, and rapid resistance. We developed SINBA (Synergy Inference by Data-driven Network-Based Bayesian Analysis), a systems biology framework that computationally prioritizes synergistic, BBB-permeable drug combinations by identifying hidden drivers sustaining oncogenic programs. Integrating MB-specific networks, transcriptomic data, and drug-gene interactions, SINBA nominated 32 candidates, of which 19 were experimentally validated as synergistic. Through iterative prioritization and experimental refinement, the MEK inhibitor mirdametinib and p38 inhibitor regorafenib emerged as the top brain-penetrant pair, suppressing G3 MB progression and extending survival in xenograft and immunocompetent models, with efficacy enhanced by low-dose radiation. Single-cell analysis revealed selective targeting of developmental origins and immune reprogramming. These findings establish SINBA as a computationally assisted discovery framework for clinically actionable combinations in high-risk MB.

cancer biology↗

bioRxiv2025

How to Improve the Reliability of Aperiodic Parameter Estimates in M/EEG: A Method Comparison and Recommendations for Best Practices

Interest in broadband aperiodic brain activity (1/f phenomenon) has increased exponentially over recent years, partly fueled by the development of tools to parameterize it (i.e., estimate its offset/intercept and exponent/slope) using the M/EEG power spectrum. Broadband aperiodic activity needs to be separated from narrowband periodic activity before its parameters are computed. A popular method, the fooof toolbox (Donoghue et al., 2020), is based on the data-driven detection of narrowband-periodic peaks, whose maximum number is set by the user. While increasing analytic flexibility, variability in the number of detected peaks may increase sensitivity to noise and reduce the reliability of aperiodic parameter estimates and the power of analytic pipelines. Here, we present an investigation of the effects of analytic choices (e.g., number of peaks, spectral estimation method) on metrics indicating the adequacy of spectral parametrization. These include the internal consistency (odd-even reliability) of aperiodic estimates, the number of outliers generated, and their ability to detect effects. Across two different data sets (resting state and task-based) we found a decrease in the reliability of intercept and slope estimates as more peaks were allowed to be extracted. To ameliorate this problem, we propose a theory-driven modification of fooof labelled censored regression, whereby a theory-driven range of frequencies expected to contain periodic activity is removed from all spectra, and the remaining power values are regressed on the remaining frequencies to obtain parameter estimates. This method shows more reliable and robust estimates compared to fooof, while avoiding overfitting.

neuroscience↗

bioRxiv2025

SpecLig: Energy-Guided Hierarchical Model for Target-Specific 3D Ligand Design

Structure-based generative models often optimize single-target affinity with ignorance of specificity, resulting in the generation of high-affinity candidates that exhibit promiscuous binding across unrelated targets. This decoupling of affinity and specificity not only compromises therapeutic efficacy but also elevates off-target risks that constrain translational potential. Therefore, we introduce SpecLig, a unified structure-based framework that jointly generates small molecules and peptides with improved target affinity and specificity. SpecLig represents a complex as a block-based graph, combining a hierarchical SE(3)-equivariant variational autoencoder with an energy-guided geometric latent-diffusion model. Chemical priors derived from block-block contact statistics are explicitly incorporated, biasing generation towards pocket-complementary fragment combinations. We benchmark SpecLig on peptide and small-molecule tasks using standard public datasets and propose precision/breadth testing paradigms to quantify specificity. Across multiple evaluations, ligand candidates generated by SpecLig usually bind to the target pocket with high specificity and affinity while maintaining competitive advantages in other attributes. Ablations indicate that both hierarchical representation and energy guidance contribute to success. Finally, we present multiple real applications that demonstrate how SpecLig improves ligands in natural complexes to mitigate potential off-target risks. SpecLig, therefore, provides a practical route to prioritize higher-specificity designs for downstream experimental validation. The codes are available at: https://github.com/CQ-zhang-2016/SpecLig.

bioinformatics↗

bioRxiv2025

Disruption of ADAMTSL4 Causes Ectopia Pupillae in Zebrafish via COL8A1-Driven Cell Migration

Congenital ectopia lentis (EL) poses a significant threat to visual outcomes in children. Although emerging causative genes have been identified, their biological functions remain poorly understood. In this study, we investigated the contribution of ADAMTSL4 variants to congenital EL in a cohort of 702 pedigrees. Pathogenic variants in ADAMTSL4 were identified in 23 probands (3.28%), comprising 29 distinct variants, with 14 being novel, making ADAMTSL4 the second most frequently mutated gene in the cohort. All affected individuals exhibited EL, and 26.09% also presented with ectopia pupillae (EP), also referred to as ectopia lentis et pupillae. To explore the functional consequences of ADAMTSL4 deficiency, we generated a CRISPR/Cas9-mediated adamtsl4-knockout zebrafish model, that faithfully recapitulated cardinal human disease features with an incidence comparable to that observed in affected patients. Histological and ultrastructural analysis revealed disrupted zonular fiber anchorage at the lens capsule, even in mutated eyes without overt EL or EP. Transgenic overexpression of adamtsl4 successfully reversed the ocular phenotypes, confirming the genes essential role in ocular development. Single-cell RNA-sequencing and fluorescence in situ hybridization demonstrated enriched ADAMTSL4/adamtsl4 expression in the equatorial lens epithelium, retinal pigment epithelium (RPE), iris anterior pigmented epithelium, and choroid fibroblast. Functional assays using zebrafish and human RPEs revealed that ADAMTSL4 deficiency compromised cell adhesion and promoted cell migration. Transcriptomic profiling revealed significant enrichment of extracellular matrix organization and cell adhesion pathways, with cross-species validation identifying consistent upregulation of COL8A1/col8a1b. Notably, COL8A1 knockdown in ADAMTSL4-deficient RPEs partially reversed the aberrant migratory phenotype, suggesting a functional interaction. Together, these findings establish ADAMTSL4 as a major causative gene in ectopia lentis et pupillae, highlight its role in orchestrating ocular integrity via regulation of extracellular matrix and cell behavior.

genetics↗

bioRxiv2025

Design and Development of DNA Damage Chemical Inducers of Proximity (DD-CIP) for Targeted Cancer Therapy

Many chemotherapies are effective against cancers that display high levels of genome instability by disrupting or overwhelming the DNA damage response to induce cell death. PARP inhibitors (PARPi) exploit this vulnerability by stalling DNA repair particularly in homologous recombination (HR)-deficient cancer cells. Although PARPi are now used to treat BRCA1/2-mutated cancers such as ovarian and breast cancers, they are still limited to a narrow range of clinical indications and are susceptible to acquired resistance. Here, we introduce "DNA Damage Chemical Inducers of Proximity" (DD-CIPs), bivalent molecules that rewire the mechanism of action of conventional PARPi. The DD-CIPs function through chemical induced proximity between PARP1/2 and the chromatin remodeling protein, BRD4. From a candidate library of DD-CIPs, we identified DD-CIP1 which induces the DNA damage response (DDR) and apoptosis to a range of cancer lines at two-digit nanomolar concentrations. Further optimization yielded DD-CIP2, which induces tumor cell death at nanomolar concentrations across diverse blood and solid cancer cells, including cancer types that are insensitive to PARPi. Using small-cell lung cancer (SCLC) as a model, we found that DD-CIP2 triggers DDR, cell cycle arrest, and apoptosis in vitro, leading to anti-tumor efficacy without substantial toxicity in preclinical SCLC xenograft models at well tolerated doses. Our findings demonstrate that DD-CIPs may provide an opportunity to address the limitations of traditional PARPi and establish chemical induced proximity as a strategy for modulating the DDR in cancer.

cancer biology↗

bioRxiv2025

Pan-cancer analysis reveals that tumor microbiomes generate specific epitopes through transcriptional reprogramming

The therapeutic promise of tumor microbial peptides faces a critical safety paradox: tumors and peritumor tissues harbor compositionally similar microbiomes, yet safe immunotherapy demands tumor-specific targeting. We performed comprehensive transcriptomic analysis of microbial communities from 1,868 tumor and peritumor samples across 11 cancer types. Although 88.5% of microbial species are present in both tissue types, tumor and peritumor tissues exhibit profound transcriptional divergence: only 34.1% of microbial genes are expressed in both tissue types, and merely 18.2% of predicted HLA-binding peptides overlap. This transcriptional divergence represents microbial transcriptional reprogramming (MTR) in tumor tissues, through which microbes activate distinct genomic regions while adapting to tumor microenvironmental pressures, thereby generating tumor-specific peptide repertoires. Using Microbial Transcriptional Reprogramming Index (MiTRI) to quantify MTR, we found that higher MiTRI values correlate with expanded immunogenic peptide repertoires. Mass spectrometry-based immunopeptidomics confirmed that 25% of naturally presented microbial peptides originate from MTR regions. Crucially, in microsatellite-stable colorectal cancer, neo-adjuvant therapy responders exhibited significantly higher MiTRI values. T cell receptors (TCRs) recognized MTR-derived epitopes at 2.10-fold higher rates than genome-wide epitopes, accounting for 79.1% of clonally expanded TCRs. These findings establish MTR as the principle that ensures the tumor-specificity and safety of micorbiome-directed immunotherapy.

cancer biology↗

bioRxiv2025

Sensitive Hormone and Neurotransmitter Detection with Carbon Flower Electrodes

Carbon-based electrochemical sensors have attracted substantial attention for continuous health monitoring due to their high surface area, wide potential window, capability for repeated measurements, and compatibility with soft wearable electronics. However, they face challenges when detecting target biomarker concentrations in the low nanomolar range (sensitivity) and differentiating them in a mixture (selectivity), limiting their applicability in real scenarios. Herein, we present sensitive and selective carbon flower sensors, fabricated via a facile and patternable spray-coating process on soft, stretchable substrates. The carbon flowers--obtained through the synthesis of polyacrylonitrile and subsequent heat treatments--exhibit unique hierarchical morphologies, high surface area, and excellent conductivity, ideal for mass transport and electrochemical detection. We demonstrate the detection of estradiol, serotonin, melatonin, dopamine, uric acid, and ascorbic acid with detection limits as low as sub-nanomolar. The carbon flower sensors exhibit good repeatability across 100 cycles and over several weeks, robustness to pH and salt variations, and excellent performance in complex bio-fluids such as artificial saliva. In mixtures containing up to four analytes, they differentiate individual molecules, demonstrating the selectivity of the carbon flowers. This combination of sensitivity, selectivity, and mechanical compatibility makes carbon flower sensors well-suited for biomolecular sensing in soft, skin-conformable wearable electronic patches.

bioengineering↗

bioRxiv2025

CSDE1 promotes passenger strand cleavage of miR-486

Strand separation of the RNA duplex is a critical step in miRNA biogenesis, in which only the guide strand is retained to form the mature miRISC. The erythroid miRNA miR-486-5p uniquely requires AGO2 catalytic slicing of its passenger strand miR-486-3p, yet the factors regulating this process remain poorly understood. Here, we identify the RNA-binding protein CSDE1 as a key cofactor that promotes AGO2-dependent passenger-strand removal and miR-486-5p maturation. CSDE1 interacts with the N-terminal domain of AGO2, essential for strand release. Loss of CSDE1 increased the miR-486-3p/5p ratio, reduced cleavage efficiency, and derepressed miR-486 targets such as FOXO1 and PTEN. Restoring full-length CSDE1 expression restored AGO2-dependent passenger strand cleavage and this function requires the N-terminal cold-shock domain CSD1, which mediates CSDE1 interaction with AGO2. Together, these results demonstrate CSDE1 as a critical regulator of AGO2-mediated strand separation, potentiating miR-486-5p function in target gene silencing associated with in hematological malignancies.

molecular biology↗

bioRxiv2025

Large-language-model-based antibiotic resistance gene prediction and resistomes mining in cyanobacterial blooms

The rapid spread of antibiotic resistance genes (ARGs) in aquatic ecosystems poses a serious public health threat. Conventional ARG detection methods, including sequence-alignment and machine learning, are limited by high false-negative or false-positive rates, especially due to sequence diversity and class imbalance in metagenomic data. Moreover, few tools offer comprehensive solutions for ARG identification, classification, and functional annotation. To address these limitations, we developed ESMARG, a novel protein language model framework based on ESM1v, for accurate ARG identification, classification, and annotation. Trained with both ARG and abundant non-ARG sequences from real-world metagenomes to enhance robustness and reduce false positives, ESMARG significantly outperformed traditional alignment-based (BLAST, DIAMOND) and recent deep learning models (ARGNet, ARG-SHINE), achieving 0.998 precision, 0.939 recall, and 0.968 F1-score for ARG identification. Functional and mechanism classification modules also demonstrated high accuracy (0.986 and 0.987) and computational efficiency. We applied ESMARG to analyze 26 cyanobacterial aggregate (CA) metagenomes collected from Lake Taihu across a full annual cyanobacterial harmful algal bloom (CyanoHAB) cycle. A total of 110 ARGs, spanning 24 drug classes and multiple resistance mechanisms, were detected, with seasonal shifts in ARG abundance and composition. Strong correlations were identified between CA resistomes and microbial community structure, especially with cyanobacteria. Further, ARG abundance was shaped by both biotic factors--such as cyanobacterial dominance and bacterial functional profiles--and abiotic variables, including biochemical oxygen demand and water temperature. Our findings demonstrate the power of ESMARG for high-resolution ARG profiling and reveal complex ecological interactions between resistomes, microbiomes, and environmental factors in CyanoHAB. HighlightsO_LIDeveloped ESMARG model outperforms BLAST/other deep learning models in ARG detection C_LIO_LIARG showed seasonal dynamics in cyanobacterial metagenomes across algal bloom cycle C_LIO_LIARG was strongly correlate with microbiome structure and cyanobacteria dominance C_LIO_LIARG abundance was shaped by cyanobacteria, bacterial functions, BOD and temperature C_LI

bioinformatics↗

bioRxiv2025

Neutrophil-microglia interaction drives reversible motor dysfunction in neuromyelitis optica model induced by subarachnoid AQP4-IgG

Neutrophils and neutrophil extracellular traps (NETs) contribute to early neuromyelitis optica (NMO) histopathology initiated by IgG targeting astrocytic aquaporin-4 water (AQP4) channels. Yet, the mechanisms recruiting neutrophils and their pathogenic roles in disease progression remain unclear. To investigate molecular-cellular events preceding classical complement cascade activation in a mouse NMO model, we continuously infused, via spinal subarachnoid route, a non-complement-activating monoclonal AQP4-IgG. Parenchymal infiltration of netting neutrophils containing C5a ensued with microglial activation and motor impairment, but no blood-brain barrier leakage. Motor impairment and neuronal dysfunction both reversed when AQP4-IgG infusion stopped. Two-photon microscopy and electron-microscopy-based reconstructions revealed physical interaction of infiltrating neutrophils with microglia. Ablation of either peripheral neutrophils or microglia attenuated the motor deficit, highlighting their synergistic pathogenic roles. Of note, mice lacking complement receptor C5aR1 exhibited reduction in neutrophil infiltration, microglial lysosomal activation, neuronal lipid-droplet burden and motor impairment. Pharmacological inhibition of C5aR1 recapitulated this protection. Immunohistochemical analysis of an NMO patients early spinal cord lesions revealed analogous pathological findings. Our study identifies neutrophil-derived C5a signaling through microglial C5aR1 as a key early driver of reversible motor neuron dysfunction in the precytolytic phase of NMO. One Sentence SummaryNeutrophil-derived C5a coactivates microglia to drive reversible motor paresis initiated by a non-complement-activating aquaporin-4-IgG binding to astrocytes in a mouse model of neuromyelitis optica.

neuroscience↗

bioRxiv2025

Using DNA origami to study nanoscale organization of plasma membranes

Plasma membrane (PM) lipids and proteins are organized into nanoscale regions called nanodomains, which regulate essential cellular processes by controlling local membrane organization. Despite advances in super-resolution microscopy and single particle tracking, the small size and temporal instability of nanodomains make them difficult to study in living cells. To overcome these challenges, we built fluorescent DNA origami probes that insert into the PM via lipid anchors displayed on the cell. The number and spatial distribution of anchors between the origami and the cell surface were precisely defined by the origami, enabling nanometer-scale sampling of the cell surface. Inserting these DNA origami particles into the membrane with lipid anchors allowed them to passively diffuse across the membrane, and we tracked their movement using single particle tracking to survey the PM landscape. By varying the number and spatial arrangement of lipid anchors connecting the DNA origami to the cell surface, we showed that immobilization of DNA origami particles requires simultaneous interactions with multiple nanodomains. Disruption of the actin cytoskeleton reduced immobilization, confirming its role in supporting nanodomain stability. Moreover, transient mechanical stretching of cells led to reversible increases in DNA origami mobility, indicating that mechanical force can reversibly regulate PM nanodomain organization. Altogether, we present a novel membrane-integrated DNA origami approach that provides mechanistic insights into PM nanodomain architecture and dynamics in living cells.

cell biology↗