Biology subjects

Wei, L.

Publications and source records attributed to Wei, L..

At least 19 recordsLinked to original sources

bioRxiv2026

Chikungunya virus live-cell reporter systems reveal cell-type-dependent antiviral responses to ribavirin

Chikungunya virus (CHIKV) remains a significant global public health threat, yet antiviral discovery is limited by experimental systems that provide little mechanistic resolution and rely heavily on transformed cell lines of uncertain physiological relevance. Here, we developed complementary live-cell CHIKV reporter systems comprising a dual-fluorescent subgenomic replicon and fluorescent infectious viruses that enable real-time monitoring of distinct stages of the viral replication cycle. By coupling an nsP3-tagged reporter to genomic RNA translation and replication with a subgenomic reporter of structural gene expression, we achieved temporal resolution of early and late replication events in living cells. Functional validation using replication-defective and subgenomic RNA-deficient mutants confirmed that the system distinguishes between viral RNA replication and downstream structural gene expression. Application of these reporters revealed distinct antiviral responses across transformed and physiologically relevant human cell models. Ribavirin and the nsP2 inhibitor RA-0002034 potently inhibited CHIKV replication in transformed cell lines. Unexpectedly, ribavirin activity was markedly diminished in primary human skeletal muscle myoblasts and dermal fibroblasts, despite robust antiviral effects in commonly used screening cell lines. Mechanistic studies showed that ribavirin-mediated inhibition was rescued by guanosine but not inosine supplementation and was recapitulated by mycophenolic acid, consistent with guanine nucleotide depletion as the predominant antiviral mechanism under the conditions tested. Notably, inhibition through this pathway was substantially less effective in primary human cells. Together, these findings establish versatile live-cell CHIKV reporter systems for mechanistic studies of viral replication and reveal striking cell-type-dependent differences in response to guanine nucleotide depletion. Our results demonstrate that host-cell context can profoundly influence both CHIKV replication and antiviral susceptibility, highlighting the importance of physiologically relevant models for understanding virus-host interactions and antiviral mechanism.

microbiology↗

bioRxiv2026

DNA sequence sets the destination of spontaneous epimutations in plants

Plant genomes gain and lose cytosine methylation spontaneously, far faster than DNA mutates, and these epimutations are inherited. The genome is thought to set a steady state around which methylation fluctuates. Here we show that this steady state, the set point, is written in the DNA of each CG site: REED, a deep-learning model trained on one methylome, reads it. In pedigrees of seven Arabidopsis thaliana accessions, gains concentrated at high set points and losses at low ones, and the equilibrium implied by their rates matched the set point, tracking it within genes. Stress sped epimutation but barely shifted its destination. The set point predicted germline epimutations in rice and changes accumulated in the wild and in a 330-year-old tree. Its rules, cooperation between nearby CG sites and transcription-factor motifs that lower methylation, are shared across flowering plants, so a genome and one methylome reveal where epigenetic variation is heading.

plant biology↗

bioRxiv2026

An update on the mosquito fauna of Aguascalientes, Mexico: morphological and molecular identifications, host-feeding patterns, and the removal of Aedes idahoensis (Theobald) from Mexico

To better understand taxonomic identity and feeding patterns of mosquitoes in all physiographic regions and sub-regions of Aguascalientes, Mexico, we conducted surveillance followed by morphologic analysis and DNA metabarcoding. We targeted the Sierra Madre Occidental, Central Plateau, and Neo-Volcanic Axis. We sampled natural larval habitat for immature stages and adults were collected using Shannon and CDC light traps, mechanical aspirators, and human landing catch method. In addition, curated specimens from Aguascalientes deposited in a local institution were re-examined. Overall, 5,227 mosquito specimens consisting of 357 larvae, 199 larval exuviae, 373 pupal exuviae, 33 pupae, 3,000 females, 1,264 males, and 1 male genitalia were examined using taxonomic keys. Most immature and adult species (n = 20/34 species representing 58.8% coverage) were barcoded for molecular species designation, and a subsample (n = 64) of engorged female mosquitoes were analyzed for blood feeding patterns. Two subfamilies, four tribes, seven genera, 14 subgenera, and 34 species were identified, which one tribe, three genera, nine subgenera, and 22 species are new records. Phylogenetic analysis of specimens from Aguascalientes identified 20 species. Morphological observation of mosquitoes deposited in entomological collections resulted in the removal of Aedesidahoensis from Mexico, leaving the country with a total of 255 species documented. Bloodmeal metabarcoding was successful in 23 mosquitoes of five species, revealing evidence of feeding on more than a single vertebrate species in 17 individuals. Birds were the most common host with the cattle egret (Ardea ibis) found in 18 out of 23 mosquitoes. This update on the taxonomic identity and mosquito richness and feeding patterns from Aguascalientes affords a better understanding of the ecology of medically important mosquito species.

zoology↗

bioRxiv2026

Relation-Aware Graph-Hypergraph Networks for Antibody Sequence-Structure Co-Design

Motivation: The design of antibody complementarity-determining regions (CDRs) is pivotal for therapeutic development, yet remains challenged by the complexity of the antibody-antigen interface. Existing methods, which largely rely on pairwise relationships, struggle to capture intrinsic multi-body interactions and global structural constraints. Results: We propose the Relation-Aware Graph-Hypergraph Network (RA-GHN), a framework that unifies graph-based pairwise modeling with biophysical hypergraphs to capture high-order cooperative effects. Its heterogeneous hypergraph uses epitope-paratope interaction patches, spatial structural clusters, and high-degree hub structures. A dual-pathway architecture integrates a Relation-Aware Equivariant Graph Network with an Equivariant Hypergraph Encoder while preserving SE(3)-equivariance. We also introduce the Structure-Sequence Decoupling Index, which balances structural reliability with sequence novelty. Experiments show that RA-GHN outperforms state-of-the-art baselines across standard benchmarks and affinity optimization tasks.

bioinformatics↗

bioRxiv2026

Reaction-aware hypergraph learning of chemical representations with LARK

Molecular pretraining offers a route to learning transferable chemical representations from unlabelled data. However, existing approaches pretrained on isolated molecular structures struggle to generalize to reaction-specific tasks because their pretraining objectives provide limited information about chemical transformations. A key challenge is to integrate this reaction information into chemical representations. Here we introduce LARK, a unified framework for learning atom-molecule-reaction knowledge from chemical transformations. Using approximately 0.6 million reactions encompassing 0.88 million unique molecular components, LARK combines bidirectional bond-electron reconstruction with structural and geometric supervision through a role-aware hierarchical hypergraph Transformer that links local atom representations to reaction context. The learned representations transfer to both reaction prediction and single-molecule property prediction, with strong performance across 42 tasks covering reaction outcomes, physicochemical properties, toxicity, metabolism and pharmacokinetics. Reactioncentre analyses show that LARK identifies where bonds break and form, while targeted interventions reveal that the learned atom representations contribute to reconstructing these changes. These findings indicate that LARK learns chemically informative representations of substructures involved in molecular transformations. Together, the results establish chemical transformations as a source of structured supervision for representation learning across molecular and reaction scales.

bioinformatics↗

bioRxiv2026

Risk-Window Palbociclib Exposure Delays ErbB2-Driven Mammary Tumorigenesis and Suppresses Mammary Epithelial Cell Stemness

ErbB2 overexpression occurs in 15-20% of invasive breast cancer (BC) and inactivation of Cyclin D1-CDK4/6 axis reduce mammary stem/progenitor cells in ErbB2-driven tumorigenesis. Here, we explored the preventive role of short-term palbociclib intervention in MMTV-ErbB2 mice. Palbociclib significantly inhibited the proliferation and stemness of ErbB2-overexpressed BC cells in vitro and in vivo. Furthermore, short-term palbociclib exposure during the early premalignant risk window significantly delayed mammary tumor development and reduced tumor multiplicity, accompanied by the suppression of epithelial proliferation and ductal/alveolar morphogenesis in premalignant tissues, as well as prolonged tumor-free survival compared with controls. Importantly, palbociclib reduced the luminal epithelial (CD24high/CD49flow), mammary reconstitution unit-enriched (CD24high/CD49fhigh) subpopulations, and luminal progenitor/TIC-enriched (CD61high/CD49fmid) subpopulations. These changes were accompanied by diminished mammary epithelial cell stemness functions, including colony-forming, mammosphere-forming, and 3D growth activities of mammary epithelial cells. Mechanistically, palbociclib-treated tissues showed inhibition of the Cyclin D1-CDK4/6-RB-E2F axis and coordinated attenuation of ER-, ErbB2- and Wnt/{beta}-catenin-associated signaling. Together, we demonstrate that short-term CDK4/6 inhibition during a premalignant risk window produces a sustained delay in ErbB2-driven mammary tumorigenesis, associated with remodeling of the mammary epithelial hierarchy and suppression of proliferative and stem/progenitor-associated activity, suggesting CDK4/6 inhibition as a strategy for ErbB2-positive BC prevention.

cancer biology↗

bioRxiv2026

Combinatorial sequence elements fine-tune mitochondrial protein import to facilitate dual localization

The dual targeting of mitochondrial proteins regulates a host of cellular processes, including metabolism, cofactor biosynthesis, mitophagy, and stress responsiveness. Despite this importance, the mechanisms by which proteins dually localize are incompletely defined. Here, we identify multiple sequence elements that compromise the matrix localization of the phosphatase PPTC7 to facilitate its accumulation at the outer mitochondrial membrane (OMM), where it regulates mitophagy. We find that PPTC7 has a moderately weak presequence, but this feature is insufficient to promote dual targeting of a generic cargo protein. Instead, our data suggest that a recently evolved glycine stretch decreases the helical potential of the PPTC7 presequence, weakening its import efficiency in vitro and in cells. Deletion of these glycine residues improves PPTC7 in vitro import and enrichment within the mitochondrial matrix, but only partially suppresses PPTC7-mediated regulation of mitophagy at the OMM. These data suggested additional elements may contribute to PPTC7 dual localization, including its mature phosphatase domain which has robust thermal stability and becomes further stabilized to an import-incompetent state upon binding to its requisite enzymatic co-factor manganese. Simultaneous increases in presequence strength and denaturation of the PPTC7 phosphatase domain are required to promote import in vitro, underscoring the multifactorial challenges associated with its matrix targeting. These data suggest that sequence-specific features can work combinatorially to impart dual-localization capacity to mitochondrial proteins, enabling functions across cellular compartments.

biochemistry↗

bioRxiv2026

A Comparative Chemical-Genetic Screen Reveals Divergent Roles for Host ALK2 Signaling Across Intracellular Bacterial Niches

Diverse vacuolar architectures present unique signaling challenges for intracellular bacteria, yet whether distinct niches share host requirements remains unclear. Because host kinases coordinate these complex cellular responses, we performed a comparative chemical-genetic screen of Coxiella burnetii, Chlamydia trachomatis, and Salmonella enterica. The screen revealed that C. burnetii is uniquely dependent on host kinase pathways. Deconvolution of these inhibitor profiles identified the bone morphogenetic protein (BMP) type I receptor, ALK2, as a critical regulatory checkpoint. While C. burnetii activates ALK2-mediated phospho-SMAD1/5/9 signaling to support replication, C. trachomatis actively suppresses this cascade. Mechanistically, ALK2 functions upstream of TFEB nuclear translocation and LAMP1 recruitment, driving the lysosomal biogenesis required to sustain the phagolysosome-like niche of C. burnetii while disrupting the non-lysosomal inclusion of C. trachomatis. Consequently, targeting ALK2 switches replication outcomes, demonstrating how distinct niches implement opposing strategies to exploit a single host signaling node.

microbiology↗

bioRxiv2026

Community diversity and interaction of bacterial, cyanobacteria and protist among aquatic and soil micro-food chains

Microorganisms, found in every environment, play an important role in recycling matter and providing energy to the ecosystem. Here, we used 18S and 16S metabarcoding to study bacteria, cyanobacteria and protist community diversity and functional ecology among different environments of typical lake, river, marine and soil from the Yangtze Delta of China. The results showed that the similarity of cyanobacteria and protozoa communities in soil and aquatic environments was both higher than that of bacteria and microalgae communities. While the diversity of cyanobacteria and bacteria in lake was higher than that in river, marine and soil, the diversity of microalgae and protozoa in river was higher than that in lake, marine and soil. The {beta} diversity of cyanobacteria and bacteria marked differences in lake from other environments. But The distribution of dominant families of cyanobacteria, bacteria, microalgae and protozoa is similar to diversity. While significant positive correlations were found among dominant species of cyanobacteria, protozoa, bacteria and microalgae in lake the dominant species among bacteria, microalgae and protozoa in river, marine and soil all showed more negative correlations. Our study provides the basis for understanding the functional ecology of microbes in the micro-food webs of different environments.

ecology↗

bioRxiv2026

Plasmodium blood-stage induces trained immunity in hepatocytes

Plasmodium parasites, the causative agents of malaria, are transmitted at high levels in endemic areas and sequential infections are common. Using a mouse model of infection we discovered liver burden was suppressed in blood-stage experienced, compared to naive, animals, independently of adaptive responses and inflammation. We observed greater chromatin accessibility of a subset of interferon stimulated genes in blood-stage experienced animals, and a rapid increase in transcription of these genes upon sporozoite challenge. Ex vivo stimulation of hepatocytes from blood-stage experienced mice also led to a rapid and elevated response upon treatment with an unrelated antigen, ctDNA. Taken together, these data are consistent with a model in which hepatocytes are reprogramed by blood stage Plasmodium infection to exhibit trained memory akin to what has been described in innate immune cells. The consequences of training of hepatocytes could be wide-reaching and might alter responses to diverse pathogens and other stimuli.

immunology↗

bioRxiv2026

Expanding genetic code to generate human brain organoids with both vasculature and microglia

Brain organoids offer an invaluable model system for studying human brain development and disease. However, the establishment of high-fidelity brain organoids with multiple cell lineages including vasculature and immune cells remains a huge challenge. Here, we present a new strategy to generate human cerebral organoids with vasculature and microglia-like cells using genetic code expansion technology (GCE-T) via site-specific protein engineering. The strategy integrates orthogonal genetic translation machinery in hPSCs via PiggyBac transposon system, enabling temporally control of ETV2 expression and endothelial differentiation in hPSC-derived cerebral organoids. The vascularized human cerebral organoids (vhCOs) exhibit coordinated development of multiple cell lineages and blood-brain barrier (BBB) features. Moreover, vhCOs form perfusable vascular network after transplanted in the immune-deficient mice. Single-nucleus RNA sequencing reveals enhanced neurovascular interactions, multi-brain-regional identities, diverse neuronal subtypes and specialized endothelial subclusters in vhCOs, closely resembling human fetal brain. Strikingly, we identify enriched microglia-like cells comprising three distinct subtypes in vhCOs, which contribute to microglia-vascular interactions and synergistically modulate vascular development. Upon Zika virus (ZIKV) infection, vhCOs show neurovascular dysfunction and impaired microglia development, offering new insights into viral-induced neurodevelopmental disorders. This study offers a unique platform for producing more valuable brain organoids with vasculature and immune components, opening a new avenue to advance organoid research and applications.

bioengineering↗

bioRxiv2026

MetaboRamics: Highly multiplexed metabolic imaging by stimulated Raman for spatial metabolomics in live cells

Cellular metabolism is highly dynamic, intertwined and spatially heterogenous, yet methods that can simultaneously visualize multiple metabolic pathways in living systems remain largely limited. Here, we present MetaboRamics: highly multiplexed metabolic imaging by stimulated Raman for spatial metabolomics in live cells. Through rational probe selection and careful optimization, isotope editing, and robust spectral unmixing, we establish a metabolic palette of 16-colors spanning glucose uptake and utilization, lipid uptake and synthesis, choline metabolism, DNA synthesis, and amino acid incorporation in addition to endogenous proteins, lipids and redox signals. Incorporation of organelle-targeted Raman probes further enables spatial interactomics and assessment of organelle activities. Applying MetaboRamics to epithelial-mesenchymal transition (EMT), we observe metabolic rewiring in mesenchymal cells, reflected by reduced glucose-derived biomass, lipid turnover, protein synthesis, and altered redox balance. Finally, we perform optical phenotyping of cellular states under metabolic stress of serum deprivation, nutrient overload (fructose, and saturated fatty acid), inflammation, and pharmacological perturbations to reveal subcellular metabolic changes. This work fully realizes the potential of stimulated Raman scattering (SRS) microscopy for super-multiplexed metabolic imaging by establishing, for the first time, a 16-plex platform for live-cell spatial metabolomics.

cell biology↗

bioRxiv2026

GeoEPred: A Multimodal Structure-Aware Geometric Deep Learning Framework for Gram-Negative Bacterial Secreted Effector Prediction with Sequence Semantics

Accurate prediction of effector proteins secreted by Gram-negative bacteria is important for elucidating bacterial pathogenic mechanisms and developing precise anti-infective strategies. Although existing methods have benefited from the strong sequence feature extraction capacity of pretrained protein language models, reliance on linear sequence information alone often fails to fully capture the three-dimensional conformational signals required for virulence functions. Meanwhile, conventional structure-based methods are limited by the scarcity of experimentally resolved protein structures. To address these challenges, We propose GeoEPred, a multimodal deep learning framework designed for the synergistic modeling of protein sequence and structure to identify Gram-negative bacterial effector proteins. Specifically, the model integrates sequence-contextual embeddings from a pretrained protein language model with three-dimensional structural representations predicted by ESMFold. A feature projection network refines fine-grained sequence signals associated with effector functions, while geometric vector perceptrons characterize inter-residue orientations, distances, and local spatial topology to capture potential structural conformational motifs. To further enable effective cross-modal fusion, we design a cross-modal alignment and feature-tokenized self-attention module. This module enhances consistency between the sequence-semantic and structural-geometric spaces through contrastive learning and models associations between linear functional motifs and spatial conformational patterns at a fine-grained token level. Extensive evaluations on multiple benchmark datasets show that GeoEPred achieves better predictive performance than existing leading models in T3SE, T4SE, and T6SE prediction tasks, while maintaining stable performance in remote homolog recognition scenarios. Moreover, the modular and extensible architecture of GeoEPred demonstrates strong generalization ability and substantial application potential for genome-scale effector protein discovery. Author summarySecreted effector proteins are central virulence factors used by many Gram-negative bacterial pathogens to execute infection strategies. Their functions are governed not only by secretion signals and short linear motifs in the amino acid sequence, but also by three-dimensional folds, local domains, and surface geometric patterns. However, current predictors mainly exploit sequence-contextual features, limiting their ability to model the correspondence between linear sequence signals and spatial conformational motifs, and thereby constraining accuracy and interpretability. Here, we present GeoEPred, a multimodal deep learning framework for secreted effector protein identification. GeoEPred couples sequence-semantic embeddings from a pretrained protein language model with structural representations learned by geometric vector perceptrons. A cross-modal alignment and interaction module uses contrastive learning to improve functional consistency between sequence and structure modalities, while feature-token attention captures fine-grained links between key linear and conformational motifs. Across benchmark datasets covering multiple effector types, GeoEPred outperforms existing state-of-the-art methods and provides interpretable evidence from sequence fragments, structural regions, and cross-modal associations, supporting functional annotation, pathogenic mechanism analysis, and experimental validation.

genomics↗

bioRxiv2026

AlaRS-mediated lactylation shields PARP1 from ubiquitination to promote HNSCC progression

Abstract textMetabolic reprogramming in solid tumors causes massive lactate accumulation, yet how this drives oncogenesis remains incompletely understood. Here, we identify alanyl-tRNA synthetase (AlaRS) as a cellular lactyltransferase that promotes head and neck squamous cell carcinoma (HNSCC) progression. AlaRS directly catalyzes site-specific lactylation of the DNA repair protein PARP1 at lysines K249 and K667. Crucially, we uncover a competitive post-translational crosstalk wherein this lactylation directly antagonizes PARP1 ubiquitination. This "lactyl-shield" prevents proteasomal degradation, hyper-stabilizing the PARP1-Mortalin complex and sustaining tumor proliferation via p53/p21 dysregulation. To therapeutically exploit this mechanism, we identified chelerythrine (CHE) as a potent, selective inhibitor that directly binds the AlaRS catalytic center. CHE abrogates AlaRS lactyltransferase activity, destabilizes PARP1, and robustly suppresses HNSCC xenograft growth in vivo. These findings establish a novel metabolic-post-translational axis linking lactate accumulation to oncoprotein stabilization, providing a blueprint for targeting tRNA synthetase moonlighting functions in cancer.

molecular biology↗

bioRxiv2026

BARseq3: a modular system for integrating spatial multi-omics and cellular barcoding in single cells

Understanding cellular identity requires multimodal measurements in single cells. Cellular barcoding provides powerful tools for recording the properties or history of individual cells in nucleic acids, while spatial omics techniques enable the measurement of a growing list of molecular features at micron resolution in tissue. However, existing methods that integrate these approaches in single samples are limited in the modalities they support, their flexibility, and efficiency. Here, we present BARseq3, a modular system that combines cellular barcoding with high-efficiency spatial transcriptomics and translatomics at subcellular resolution in tissue. BARseq3 is compatible with fixed samples, immunostaining, diverse species, and can be easily extended to include other spatial assays, enabling a multimodal understanding of cellular identity.

genomics↗

bioRxiv2026

Spatial-neighbour encoding enables fast RNA 3D structure search

Experimental and predicted RNA three-dimensional structures are expanding rapidly, but RNA structure search still lacks a compact residue-level representation that supports database-scale comparison. Using family-held-out ablations across the currently available experimental RNA structure collection, we found that spatial-neighbour features are markedly more informative for family-level discrimination than conventional backbone and base descriptors. Building on this result, we developed RiboSeek, a search framework based on a 20-letter geometric alphabet (RS-20), an 80-letter structure-and-base composite alphabet (RS-80). Across family-level classification and retrieval benchmarks, RS-80 delivered the strongest overall performance, whereas RS-20 most closely tracked US-align TM-score, indicating better preservation of geometric similarity. RiboSeek searches the full experimental RNA structure database in 204 ms per query and can be applied to predicted RNA structure libraries to prioritize candidate structural relationships for downstream analysis.

bioinformatics↗

bioRxiv2026

Dormant Bacteria's Fatal Attraction to RNA Bacteriophages

Phages are the most abundant biological entities on Earth, yet RNA phages are strikingly scarce compared to their DNA counterparts--a long-standing mystery in phage biology. Here, we use dsRNA phage phiYY as a model to demonstrate that while RNA phages efficiently infect growing bacteria, they are gradually eliminated inside dormant bacteria through weak and time-dependent ROS-mediated damage. The RNA phages decline over days rather than through immediate clearance inside dormant bacteria. Accordingly, scavenging ROS with mannitol or overexpressing ROS degradation enzymes AhpB/TrxB2 rescues RNA phages from elimination. Crucially, because the underlying ROS-mediated RNA damage is minimal, RNA phage survival hinges on genomic redundancy. In single-phage infections, the lone RNA genome is highly vulnerable to cumulative damage and is eventually inactivated. In contrast, during co-infection by multiple RNA phages, the presence of multiple genome copies provides functional redundancy, thereby allowing a fraction of RNA phages to survive inside dormant bacteria. Given that most environmental bacteria are dormant and subject to heterogeneous phage infection, this copy number-dependent vulnerability offers a possible explanation for the scarcity, but not perish, of RNA phages in nature.

microbiology↗

bioRxiv2026

Geometry-enhanced protein language modeling enables discovery of novel antibiotic resistance genes

The global antibiotic resistome remains largely unexplored, not because antibiotic resistance genes (ARGs) are rare in the environment, but because many are evolutionarily distant from known ARGs. Current computational approaches primarily rely on sequence homology, and thus miss distant homologues. We develop GeoARG, a geometry-enhanced framework that integrates structural features with protein language models through knowledge distillation, enabling efficient large-scale screening using sequence input alone. Across multiple benchmarks, GeoARG substantially improves the detection of remotely homologous ARGs, particularly under low sequence identity and fragmented conditions. Large-scale metagenomic analysis uncovers 1,485 high-confidence ARG candidates that are highly divergent from known ARGs, expanding the phylogenetic and functional landscape of the resistome. Structural analyses further show that these candidates preserve active-site geometry and maintain stable ligand-binding configurations consistent with known resistance mechanisms. These results demonstrate that geometric constraints enable systematic expansion of the resistome and facilitate the discovery of evolutionarily distant yet functionally conserved genes. A public web server is available at https://ycclab.cuhk.edu.cn/GeoARG/.

bioinformatics↗