Biology subjects

Zhao, H.

Publications and source records attributed to Zhao, H..

At least 181 records · Page 10Linked to original sources

bioRxiv2025

Discovery, characterization, and application of chromosomal integration sites in the hyperthermophilic crenarchaeon Sulfolobus islandicus

Sulfolobus islandicus, an emerging crenarchaeal model organism, offers unique advantages for metabolic engineering and synthetic biology applications owing to its ability to thrive in extreme environments. Although several genetic tools have been established for this organism, the lack of well-characterized chromosomal integration sites has considerably limited its potential as a cellular factory. Here, we systematically identified and characterized 13 artificial CRISPR RNAs (crRNAs) targeting eight chromosomal integration sites in S. islandicus using the CRISPR-COPIES pipeline and a multi-omics-informed computational workflow. By leveraging the endogenous CRISPR-Cas systems, we integrated the reporter gene lacS into these sites and validated heterologous gene expression through a {beta}-galactosidase reporter assay, which revealed significant positional effects on expression levels. As a proof of concept, we utilized these characterized sites to genetically manipulate lipid ether composition by overexpressing GDGT (glycerol dibiphytanyl glycerol tetraether) ring synthase B (GrsB) in S. islandicus, a key enzyme in GDGT biosynthesis. This study expands the genetic toolbox for S. islandicus and highlights a concept that could be widely applicable to other Sulfolobales, advancing their potential as robust platforms for archaeal synthetic biology and industrial biotechnology.

synthetic biology↗

bioRxiv2025

A Hyperbolic Discrete Diffusion 3D RNA Inverse Folding Model for functional RNA design

Generative design of functional RNAs presents revolutionary opportunities for diverse RNA-based biotechnologies and biomedical applications. To this end, RNA inverse folding is a promising strategy for generatively designing new RNA sequences that can fold into desired topological structures. However, three-dimensional (3D) RNA inverse folding remains highly challenging due to limited availability of experimentally derived 3D structural data and unique characteristics of RNA 3D structures. In this study, we propose RIdiffusion, a hyperbolic denoising diffusion generative RNA inverse folding model, for 3D RNA design tasks. By embedding geometric features of RNA 3D structures and topological properties into hyperbolic space, RIdiffusion efficiently recovers the distribution of nucleotides for targeted RNA 3D structures based on limited training samples using a discrete diffusion model. We perform extensive evaluations on RIdiffusion using different datasets and strict data-splitting strategies and the results demonstrate that RIdiffusion consistently outperforms baseline generative models for RNA inverse folding. This study introduces RIdiffusion as a powerful tool for the generative design of functional RNAs, even in structure-data-scarce scenarios. By leveraging geometric deep learning, RIdiffusion enhances performance and holds promise for diverse downstream applications.

bioinformatics↗

bioRxiv2025

SEHI-PPI: An End-to-End Sampling-Enhanced Human-Influenza Protein-Protein Interaction Prediction Framework with Double-View Learning

Influenza continues to pose significant global health threats, hijacking host cellular machinery through protein-protein interactions (PPIs), which are fundamental to viral entry, replication, immune evasion, and transmission. Yet, our understanding of these host-virus PPIs remains incomplete due to the vast diversity of viral proteins, their rapid mutation rates, and the limited availability of experimentally validated interaction data. Additionally, existing computational methods often struggle due to the limited availability of high-quality samples and their inability to effectively model the complex nature of host-virus interactions. To address these challenges, we present SEHI-PPI, an end-to-end framework for human-influenza PPI prediction. SEHI-PPI integrates a double-view deep learning architecture that captures both global and local sequence features, coupled with a novel adaptive negative sampling strategy to generate reliable and high-quality negative samples. Our method outperforms multiple benchmarks, including state-of-the-art large language models, achieving a superior performance in sensitivity (0.986) and AUROC (0.987). Notably, in a stringent test involving entirely unseen human and influenza protein families, SEHI-PPI maintains strong performance with an AUROC of 0.837. The model also demonstrates high generalizability across other human-virus PPI datasets, with an average sensitivity of 0.929 and AUROC of 0.928. Furthermore, AlphaFold3-guided case studies reveal that viral proteins predicted to target the same human protein cluster together structurally and functionally, underscoring the biological relevance of our predictions. These discoveries demonstrate the reliability of our SEHI-PPI framework in uncovering biologically meaningful host-virus interactions and potential therapeutic targets.

bioinformatics↗

bioRxiv2025

Leveraging cell-type specificity and similarity improves single-cell eQTL fine-mapping

Identifying cell-type-specific expression quantitative trait loci (eQTLs) is important to understanding the genetic regulation of gene expressions at the cell-type level and its relevance to complex traits. However, existing eQTL fine-mapping methods are limited in power and accuracy when cell types are analyzed separately. To improve eQTL mapping, we present CASE, a Bayesian framework to perform Cell-type-specific And Shared EQTL fine-mapping that simultaneously analyzes multiple cell types. CASE can effectively capture effect-sharing patterns across cell types while disentangling the confounding effects of linkage disequilibrium (LD). We demonstrate that CASE outperforms the existing single-trait (SuSiE) and multi-trait (mvSuSiE) eQTL methods through comprehensive simulations. When applied to the OneK1K data, CASE identified more genetic regulations of gene expressions, better capturing cell type specificity and functionally enriched and disease-associated eQTLs. The CASE framework for single-cell eQTL fine-mapping can be broadly applied to multi-tissue and multi-trait genetic studies.

genetics↗

bioRxiv2025

spVelo: RNA velocity inference for multi-batch spatial transcriptomics data

RNA velocity has emerged as a powerful tool to interpret transcriptional dynamics and infer trajectory from snapshot datasets. However, current methods fail to utilize the spatial information inherent in spatial transcriptomics and lack scalability in multi-batch datasets. Here, we introduce spVelo (spatial Velocity inference), a scalable framework for RNA velocity inference in multi-batch spatial transcriptomics data. Our model comparison studies show that spVelo compares favorably to existing methods regarding velocity consistency, transition accuracy, and direction correctness across expression levels, spatial graphs in each batch, and MNN graphs between batches. Furthermore, spVelo supports several downstream applications, including uncertainty quantification, complex trajectory pattern discovery, biologically significant state driver marker identification, gene regulatory network inference and temporal cell-cell communication inference. In conclusion, spVelo has the potential to provide deeper insights into complex tissue organization and underscore their biological mechanisms based on spatially-resolved patterns.

bioinformatics↗

bioRxiv2025

Asymmetric Histone Inheritance Regulates Differential Transcription Re-initiation and Cell Fate Decisions in Mouse Olfactory Horizontal Basal Cells

The olfactory epithelium (OE) possesses an adult stem cell population, the horizontal basal cells (HBCs), to permit lifelong tissue regeneration. Here we show that HBCs exhibit asymmetric inheritance of histone H4 but not H2A-H2B during OE regeneration in mice. Primary HBC cultures further revealed asymmetric histone inheritance for H3 and H3.3. Upon mitotic exit, asymmetric histone inheritance correlates with asynchronous transcription re-initiation and differential enrichment of p63, a key transcription factor for HBC cell fate. Disruption of asymmetric histone inheritance abolished these asymmetric cellular features and attenuated OE regeneration and smell behavior recovery. Single-cell RNA sequencing of paired HBC daughters in culture further supported asymmetric multilineage cell fate priming. Together, these findings reveal asymmetric histone inheritance in a mammalian adult stem cell lineage and highlight its biological significance in neural tissue regeneration and animal behavior.

developmental biology↗

bioRxiv2025

Pre-training Genomic Language Model with Variants for Better Modeling Functional Genomics

Sequence-to-function models can predict gene expression from sequence data and be used to link genetic information with transcriptomics data to understand regulatory processes and their effects on complex phenotypes. The genomic language models are pre-trained with large-scale DNA sequences and can generate robust representations of these sequences by learning the genomic context. How-ever, few studies can estimate the predictability of gene expression levels and bridge these two classes of models together to explore individualized gene expression prediction. In this manuscript, we propose UKBioBERT as a DNA language model pre-trained with genetic variants from UK BioBank. We demonstrate that UKBioBERT generates informative embeddings capable of identifying gene functions, and improving gene expression prediction in cell lines, thereby enhancing our understanding of gene expression predictability. Building upon these embeddings, we combine UKBioBERT with state-of-the-art sequence-to-function architectures, Enformer and Borzoi, to create UKBioFormer and UKBioZoi. These models exhibit better performance in predicting highly predictable gene expression levels and can be generalized across different cohorts. Furthermore, UKBioFormer effectively captures the relationship between genetic variants and expression variations, enabling in-silico mutation analyses and eQTL identification. Collectively, our findings underscore the value of integrating genomic language models and sequence-to-function approaches for advancing functional genomics research.

bioinformatics↗

bioRxiv2025

Neutralizing antibodies elicited by Nipah virus G-head nanoparticle target diverse sites and inhibit receptor binding and fusion

Nipah virus (NiV) and Hendra virus (HeV), two highly pathogenic Henipaviruses (HNVs), pose a significant public health threat. The attachment glycoprotein (G) plays a crucial role in viral attachment and entry, making it an attractive target for vaccine and therapeutic antibody development. However, the antigenic landscape and neutralization sensitivity of the G protein remain poorly defined. Here, we systematically characterize 27 monoclonal antibodies (mAbs) elicited by NiV G head (GH) nanoparticle-immunized mice. Among these, 25 mAbs exhibit neutralizing activity against two major NiV strains, NiV-Malaysia and NiV-Bangladesh, with five mAbs also cross-inhibiting HeV infection. Although all mAbs target GH, they inhibit viral infection through distinct mechanisms, either by blocking receptor ephrin-B2 engagement with the G protein or by preventing viral membrane fusion. Notably, mAbs from two distinct groups conferred complete protection to hamsters against lethal NiV-Malaysia infection. Structural analysis of NiV GH in complex with four representative Fabs reveals four non-overlapping epitopes, including two novel antigenic sites and one shared protective epitope identified across species. Our study provides a comprehensive map of the antigenic determinants of NiV GH, elucidates the relationship between epitopes, neutralization mechanisms, and protection, and offers new insights and opportunities for antibody-based therapies and vaccine development. HighlightsMost mAbs induced in G head nanoparticle-immunized mice are potent neutralizers MAbs recognizing the G head block receptor binding or viral fusion Structural analysis shows mAbs target four distinct epitopes on G head protein Receptor competition site on G head is a shared protective antigenic site in human and multiple animals

immunology↗

bioRxiv2025

The architecture, assembly, and evolution of a complex flagellar motor

Bacterial flagella drive motility in many species, likely including the last bacterial common ancestor 1,2. Knowledge of flagellar assembly and function has mainly come from studies of Escherichia coli and Salmonella enterica, which have simple flagellar motors 3-7. However, most flagellated bacteria possess complex motors with unique, species-specific adaptations whose mechanisms and evolution remain largely unexplored 8-10. Here, we deploy a multidisciplinary approach to build a near-complete model of the flagellar motor in Campylobacter jejuni, revealing its remarkable complexity in architecture and composition. We identify an E-ring around the MS-ring, a periplasmic cage with two distinctive conformations, and an intricate interaction network between the E-ring and cage. These scaffolds play critical roles in stabilizing and regulating 17 torque-generating stator complexes for optimal motility. In-depth evolutionary analyses uncover the ancient origin and prevalence of the E-ring in flagellated species of the domain Bacteria as well as a unique exaptation of type IV pili components PilMNOPQF in the ancestral motor of the phylum Campylobacterota. Collectively, our studies reveal novel mechanisms of assembly and function in complex flagellar motors and shed light on the evolution of flagella and modern bacterial species.

microbiology↗

bioRxiv2025

Application and mechanism of anticancer peptides in organoid models of intrahepatic cholangiocarcinoma

BackgroundThe clinical manifestations of intrahepatic cholangiocarcinoma (ICC) are non-specific, and only a small number of patients are eligible for surgical resection at the time of diagnosis, limiting treatment options. Anticancer peptides (ACPs) exhibit strong inhibitory activity against tumour cell lines, exhibit minimal side effects, are easily modified and optimised, and have low production costs. These attributes make ACPs a prospect for clinical applications. Simultaneously, the development of patient-derived three-dimensional organoids as a novel disease model has enabled the replication of the structure and heterogeneity of solid tumours. These organoids provide valuable tools for understanding disease mechanisms, conducting drug sensitivity tests, and developing targeted therapies. However, the effect of ACPs at the organoid level in ICC remains unknown. Therefore, this study aims to explore the potential of ACPs in treating ICC using patient-derived organoids as a model system to evaluate their efficacy and optimize therapeutic strategies. MethodsWe designed and synthesised a novel ACP sequence and applied it to a patient-derived organoids (PDOs) model. Organoids were evaluated using histochemistry analysis and whole-exome sequencing. ACPs were compared with representative chemotherapeutic drugs, including sorafenib and gemcitabine, to assess treatment differences. Additionally, scanning electron microscopy and flow cytometry were performed for further mechanistic analyses. ResultsOrganoid models exhibited histological characteristics similar to those of maternal tumours, especially showing strong positivity for EpCAM and KRT19 expression. Furthermore, whole-exome sequencing of the primary tumour and derived organoids revealed that PDOs closely recapitulated the genomic similarities of the primary tumour across multiple levels. Drug response analyses revealed that ACPs exhibited highly efficient anti-tumour effects, with specific differences observed between patients. ACPs affected the growth of tumour cells and exerted anticancer effects through direct membrane disruption and indirect induction of apoptosis. ConclusionIn this study, organoids derived from patients with ICC were established. This in vitro model provides valuable tools for evaluating the therapeutic response of ACPs and offers novel insight for the study of ICC.

cancer biology↗

bioRxiv2025

Transient Acute Neuronal Activation Response Caused by High Concentrations of Oligonucleotides in the Cerebral Spinal Fluid

Oligonucleotide (ON) therapeutics are promising as a disease-modifying therapy for central nervous system disorders. Intrathecal ON administration into the cerebral spinal fluid is a safe and effective delivery mode to the CNS. However, preclinical studies have shown acute toxicities following high-dose central ON delivery. Here we characterize a transient neurobehavioral change peaking 15 minutes after ON dosing and resolving after 120 minutes. Symptoms include shaking, muscle twitching, cramping, hyperactivity, stereotypic movements, hyperreactivity, vocalizations, tremors, convulsions, and seizures. These are collectively referred here as the acute neuronal activation response. Acute neuronal activation is observed in rats, mice, and non-human primates and is quantifiable using a simple scoring system. It is distinct from acute sedation seen with some phosphorothioate-modified antisense oligonucleotides, characterized by loss of spinal reflexes, ataxia, and sedation. The acute neuronal activation response is largely sequence-independent and is driven by ON chelation of divalent cations, particularly influenced by the divalent cations-to-ON ratio in the dosing solution. Acute neuronal activation can be safely mitigated by adjusting this ratio through magnesium supplementation in the ON formulation. We provide a comprehensive framework for quantifying and mitigating the acute neuronal activation response caused by high concentrations of centrally delivered ON therapeutics in preclinical species. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/638138v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@b18796org.highwire.dtl.DTLVardef@13c9898org.highwire.dtl.DTLVardef@1463019org.highwire.dtl.DTLVardef@ffe719_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

A Generalized Platform for Artificial Intelligence-powered Autonomous Protein Engineering

Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desired functions for practical applications remains slow, expensive, and specialist-dependent1-3. Here we report a generally applicable platform for autonomous protein engineering that integrates machine learning and large language models with biofoundry automation to eliminate the need for human intervention, judgement, and domain expertise. Requiring only an input protein sequence and a quantifiable way to measure fitness, this autonomous platform can be applied to engineer virtually any protein. As a proof of concept, we engineered Arabidopsis thaliana halide methyltransferase (AtHMT)4 for a 90-fold improvement in substrate preference and 16-fold improvement in ethyltransferase activity, along with developing a Yersinia mollaretii phytase (YmPhytase)5,6 variant with 26-fold improvement in activity at neutral pH. This was accomplished in four rounds over four weeks, while requiring construction and characterization of fewer than a total of 500 variants for each enzyme. This platform for autonomous experimentation paves the way for rapid advancements across diverse industries, from medicine and biotechnology to renewable energy and sustainable chemistry.

synthetic biology↗

bioRxiv2025

Transcriptomic and spatial GABAergic neuron subtypes in zona incerta mediate distinct innate behaviors

Understanding the anatomical connection and behaviors of transcriptomic neuron subtypes is critical to delineating cell type-specific functions in the brain. Here we integrated single-nucleus transcriptomic sequencing, in vivo circuit mapping, optogenetic and chemogenetic approaches to dissect the molecular identity and function of heterogeneous GABAergic neuron populations in the zona incerta (ZI) in mice, a region involved in modulating various behaviors. By microdissecting ZI for transcriptomic and spatial gene expression analyses, our results revealed two non-overlapping Ecel1- and Pde11a-expressing GABAergic neurons with dominant expression in the rostral and medial zona incerta (ZIrEcel1 and ZImPde11a), respectively. The GABAergic projection from ZIrEcel1 to periaqueductal gray mediates self-grooming, while the GABAergic projection from ZImPde11a to the oral part of pontine reticular formation promotes transition from sleep to wakefulness. Together, our results revealed the molecular markers, spatial organization and specific neuronal circuits of two discrete GABAergic projection neuron populations in segregated subregions of the ZI that mediate distinct innate behaviors, advancing our understanding of the functional organization of the brain.

neuroscience↗

bioRxiv2025

Infectome Analysis of Small Mammals in Southern China Reveals Ecological Associations and Emerging Threats from Diverse Pathogens

Small mammals harbor a diverse array of zoonotic pathogens. To date, however, metagenomic surveys of these species have primarily focused on viral diversity, with limited attention paid to bacteria and eukaryotic pathogens. Additionally, the ecological determinants of pathogen diversity within these mammals have not been systematically examined. Herein, we employed a metatranscriptomics approach to survey the pathogen infectome of 2,408 individual samples, representing lung, liver, and gut tissues from 858 animals collected throughout Guangdong province, China, with a study design that accounted for host species, tissue, season, and geographic location. We identified 76 pathogen species, including 29 RNA viruses, 12 DNA viruses, 5 bacteria, and 30 eukaryotic pathogens, 33 of which were newly discovered. Tissue distribution analysis revealed distinct organotropisms, suggesting varied transmission routes, while host distribution analysis showed that each animal carried an average of one pathogen, with 10 pathogens widely distributed among mammalian orders. Our characterization of the geographic and seasonal patterns revealed that pathogen richness was primarily influenced by region, host species, and season, while pathogen composition was largely shaped by host genetic distance. Collectively, these data provide the first comprehensive insight into the dynamics of the pathogen infectome in these key mammalian disease reservoirs, highlighting the major factors driving pathogen diversity and transmission.

microbiology↗

bioRxiv2025

Knockoff procedure improves causal gene identifications in conditional transcriptome-wide association studies

Transcriptome-wide association studies (TWASs) have been developed to nominate candidate genes associated with complex traits by integrating genome-wide association studies (GWASs) with expression quantitative trait loci (eQTL) data. However, most existing TWAS methods evaluate the marginal association between a single gene and the trait of interest without accounting for other genes within the same genomic region or the same gene from different tissues. Additionally, false-positive gene-trait pairs can arise due to correlations with the direct effects of genetic variants. In this study, we introduce TWASKnockoff, a new knockoff-based framework for detecting causal gene-tissue pairs using GWAS summary statistics and eQTL data. Unlike marginal testing in traditional TWAS methods, TWASKnockoff examines the conditional independence for each gene-trait pair, considering both correlations in cis-predicted expression across genes and correlations between gene expression levels and genetic variants. TWASKnockoff estimates the theoretical correlation matrix for all genetic elements (cis-predicted expression across genes and genotypes for genetic variants) by averaging estimations from parametric boot-strap samples and then performs knockoff-based inference to detect causal gene-trait pairs while controlling the false discovery rate (FDR). Through empirical simulations and an application to type 2 diabetes (T2D) data, we demonstrate that TWASKnockoff achieves superior FDR control and improves the average power in detecting causal gene-trait pairs at a fixed FDR level.

genetics↗

bioRxiv2025

Feature-Driven Whole-Tissue Imaging with Subcellular Resolution

Understanding how subcellular structures and events shape tissue-wide phenomena remains a central challenge in biology. We present Multiscale Cleared Tissue Axially Swept Light-Sheet Microscopy (MCT-ASLM), a platform combining cm-scale imaging with targeted high-resolution interrogation of intact tissues in human-guided or autonomous modes. Capable of capturing fields of view up to 21 mm at micron-scale resolution, MCT-ASLM can seamlessly transition to submicron (~300 nm) isotropic resolution for targeted imaging. This versatility enables comprehensive studies of hierarchical organization and spatially complex processes, including mapping neuronal circuits in rat brains, visualizing glomerular innervation in mouse kidneys, and examining metastatic tumor microenvironments. By bridging subcellular to tissue-level scales, MCT-ASLM offers a powerful method for unraveling how local events contribute to global biological phenomena.

biophysics↗

bioRxiv2025

Identification and Engineering of UDP-rhamnosyltransferase from Trillium tschonoskii for Heterologous Biosynthesis of Polyphyllin II in Engineered Yeast

AbstractPolyphyllins, a class of isospirostan-type steroidal saponins, exhibit potent cytotoxicity against a variety range of cancer cells. Although extensive research efforts have been made, the complete biosynthetic pathway of these compounds remains unclear. In order to elucidate these pathways, different tissues from Trillium tschonoskii were collected for sequencing that yielded 173,382 high-quality unigene sequences, among which 353 were annotated as glycosyltransferases. Then, a novel rhamnosyltransferase gene, UGT738A3, was characterized, which catalyzes the conversion of triglycoside polyphyllin III and pennogenin 3-O-beta-chacotrioside into tetraglycoside polyphyllin II and polyphyllin VII. The key residues that affect the catalytic activity of UGT738A3 were identified through site-directed mutation that the mutant A158T/P101L exhibited improved catalytic activity towards polyphyllin III and pennogenin 3-O-beta-chacotrioside by 2.5-fold and 6.5-fold respectively. We thus reconstructed the biosynthesis pathway of polyphyllin II in yeast by introducing the UGT93M3(catalyzing the formation of polyphyllin VI) and UGT738A3A158T/P101L3. This study not only elucidates the pivotal role of UGT738A3 in catalyzing the formation of tetraglycoside, but also provides highly efficient enzymatic components essential for the heterologous biosynthesis of polyphyllin saponins.

synthetic biology↗

bioRxiv2025

UNICORN: Towards Universal Cellular Expression Prediction with an Explainable Multi-Task Learning Framework

Sequence-to-function analysis is a challenging task in human genetics, especially in predicting cell-type-specific multi-omic phenotypes from biological sequences such as individualized gene expression. Here, we present UNICORN, a new method with improved prediction performances than the existing methods. UNICORN takes the embeddings from biological sequences as well as external knowledge from pre-trained foundation models as inputs and optimizes the predictor with carefully-designed loss functions. We demonstrate that UNICORN outperforms the existing methods in both gene expression prediction and multiomic phenotype prediction at the cellular level and the cell-type level, and it can also generate uncertainty scores of the predictions. Moreover, UNICORN is able to link personalized gene expression profiles with corresponding genome information. Finally, we show that UNICORN is capable of characterizing complex biological systems for different disease states or perturbations. Overall, embeddings from foundation models can facilitate the understanding of the role of biological sequences in the prediction task, and incorporating multi-omic information can enhance prediction performances.

bioinformatics↗