Biology subjects

Zhao, H.

Publications and source records attributed to Zhao, H..

At least 163 records · Page 9Linked to original sources

bioRxiv2025

How Bacterial Vesicles Trigger Hearing Loss: The Role of Pyroptosis in Cochlear Damage

Withdrawal StatementThe authors have withdrawn this manuscript because [After submitting my article, I discovered some important missing data that is crucial for supporting my viewpoint and research findings. Although I tried to supplement this data through other means, I was unable to complete it before the deadline. Therefore, I believe the best approach is to withdraw the article so that I can further refine and present my research findings in a complete manner.]. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

immunology↗

bioRxiv2025

Anks3 mediates cilia dependent polycystin signaling and is essential for adult kidney homeostasis

The existence of a cilia-dependent cyst activation (CDCA) pathway underlying autosomal dominant polycystic kidney disease was identified by showing that cyst progression following loss of polycystins is significantly suppressed by removal of structurally intact cilia. We applied translating ribosome affinity purification RNASeq on pre-cystic mouse kidneys to determine a cell-autonomous in vivo translatome associated with CDCA and identified Glis2 as an early effector of polycystin signaling. Here, to discover additional components of CDCA, we used polycystin-dependent Glis2 expression as a functional readout and identified Anks3, from the CDCA pattern translatome, as a candidate cytosolic regulator of polycystin signaling. Anks3 regulates polycystin-dependent Glis2 expression both in vitro and in vivo. Anks3 also undergoes polycystin-dependent changes in phosphorylation state. Inactivation of Anks3 in Pkd1 mouse models suppresses cyst progression, but also results in rapidly progressive kidney injury independent of polycystins. Anks3 inactivation also normalizes a broader spectrum of polycystin-dependent CDCA translatome changes. These findings define Anks3 as a central regulator of cilia dependent polycystin signaling, functioning downstream of cilia and polycystins and upstream of Glis2, and show that Anks3 has broader functions in maintaining renal structural and functional homeostasis.

genetics↗

bioRxiv2025

LOSS OF ROR2 TYROSINE KINASE RECEPTOR IS ASSOCIATED WITH ENDOTHELIAL DYSFUNCTION IN PAH VIA INAPPROPRIATE INTEGRIN BETA 1 ACTIVATION

RationaleEndothelial dysfunction is a key feature of pulmonary arterial hypertension (PAH). We previously identified Wnt7a, a ligand of the Wnt planar cell polarity (PCP) pathway, as essential for pulmonary angiogenesis, with its loss linked to PAH. Given the importance of Wnt/PCP to lung endothelial function and angiogenesis, our goal is to elucidate how Wnt/PCP regulates angiogenic responses in pulmonary microvascular endothelial cells (PMVECs). ROR2, a tyrosine kinase receptor specific to Wnt/PCP, is crucial for cardiovascular development, but its role in PAH is unclear. We hypothesized that ROR2 supports endothelial homeostasis, and its loss would impair angiogenesis, contributing to PAH. MethodsEndothelial-specific ROR2 knockout (ROR2 ECKO) and wild-type (WT) mice were studied under normoxia and chronic hypoxia using echocardiography, hemodynamics, and lung morphometry. PMVECs from healthy and PAH lungs were transfected with ROR2 siRNA/constructs for functional and molecular studies. Focal adhesion (FA) activation and force generation were assessed via FRET-based methods. Bulk and single-cell transcriptomic analyses were performed on siROR2 PMVECs and ROR2 ECKO lungs. ResultsROR2 ECKO mice exhibited worsened pulmonary hypertension, right ventricular remodeling, microvascular loss, and muscularization in hypoxia. Single-cell RNA sequencing of lung endothelial cells showed dysregulation of pathways involved in barrier formation and angiogenesis. Evans blue dye extravasation confirmed reduced endothelial barrier integrity in ROR2 ECKO mice. ROR2-deficient PAH PMVECs displayed increased adhesion, permeability, and FA numbers, with reduced VE-cadherin at cell junctions. Confocal imaging revealed ROR2 localization in FAs, interacting with integrin {beta}1 (ITGB1). FRET analysis showed that ITGB1 remained in an active, adhesion-promoting state in ROR2-deficient cells. Restoring ROR2 in PAH PMVECs normalized adhesion, barrier function, and FA abundance. Transcriptomic analysis identified Rab12 as a key mediator of ROR2-ITGB1 crosstalk, with Rab12 knockdown mimicking ROR2 deficiency in PMVECs. ConclusionsROR2 regulates pulmonary angiogenesis by maintaining endothelial barrier integrity and facilitating integrin recycling. Restoring ROR2 signaling could be a potential therapeutic approach for PAH.

cell biology↗

bioRxiv2025

Evolution of a fuzzy ribonucleoprotein complex in viral assembly

Previously we showed that the genetic diversity of SARS-CoV-2 nucleocapsid (N) protein explores a wide range of biophysical properties facilitated by non-local impact of point mutations to its intrinsically disordered regions (Nguyen et al., 2024). This includes modulation of self-association, such as the creation of a de novo binding interface through the P13L mutation characteristic of Omicron variants. In the present work we focus on the key function of N condensing viral RNA into ribonucleoprotein particles (RNPs) for viral assembly. Lacking high-resolution structural information, biochemical and biophysical approaches have revealed architectural principles of RNPs, which involve cooperative interactions of several protein-protein and protein-RNA interfaces, initiated through oligomerization of conserved transient helices in the central disordered linker of N. Here we study the impact of defining N-protein mutations in variants of concern on RNP formation, using biophysical tools, a virus-like particle assay, and reverse genetics experiments. We find convergent evolution in repeated, independent introduction of amino acid substitutions strengthening existing binding interfaces, compensating for other substitutions that promote viral replication but decrease RNP stability. Furthermore, we show that the P13L mutation of Omicron variants enhances RNP assembly and increases viral fitness. Overall, our data reveal RNP complexes to be highly variable not only in sequence and conformations, but also in thermodynamic and kinetic stability, with its pleomorphism affecting basic architectural principles. We hypothesize that the formation of polydisperse, fuzzy N-RNA clusters with multiple distributed weak binding interfaces optimizes reversible RNA condensation, while supporting host adaptation and allowing for a large sequence space to be explored.

biophysics↗

bioRxiv2025

spRefine Denoises and Imputes Spatial Transcriptomics with a Reference-Free Framework Powered by Genomic Language Model

The analysis of spatial transcriptomics is hindered by high noise levels and missing gene measurements, challenges that are further compounded by the higher cost of spatial data compared to traditional single-cell data. To overcome this challenge, we introduce spRefine, a deep learning framework that leverages genomic language models to jointly denoise and impute spatial transcriptomic data. Our results demonstrate that spRefine yields more robust celland spot-level representations after denoising and imputation, substantially improving data integration. In addition, spRefine serves as a strong framework for model pre-training and the discovery of novel biological signals, as highlighted by multiple downstream applications across datasets of varying scales. Notably, spRefine enhances the accuracy of spatial ageing clock estimations and uncovers new aging-related relationships associated with key biological processes, such as neuronal function loss, which offers new insights for analyzing ageing effect with spatial transcriptomics.

genomics↗

bioRxiv2025

Deciphering the Sequence Basis and Application of Transcriptional Initiation Regulation in Plant Genomes Through Deep Learning

Transcription initiation is a critical regulatory step in plant gene expression, yet its sequence determinants remain largely elusive. Here we introduce GenoRetriever, an interpretable deep learning model that deciphers the sequence basis of transcriptional initiation regulation across plant genomes. Trained on STRIPE-seq data from 16 soybean tissues and six other crop species, GenoRetriever identifies 27 core sequence motifs that govern transcription start site (TSS) selection and usage. The model predicts TSS locations and usage levels with high accuracy, as validated by in silico motif insertions, saturation mutagenesis, and CRISPR-Cas9 promoter editing. It further reveals that 31.85% of natural variation between wild and domesticated soybean drives shifts in promoter motif usage during domestication, and uncovers lineage-specific motif effects between monocots and dicots. This interpretable model and its user-friendly web server for promoter analysis and design make GenoRetriever both a methodological innovation and practical tool for plant functional genomics and crop improvement.

plant biology↗

bioRxiv2025

Exploring the double-stranded DNA viral landscape in eukaryotic genomes

Endogenous viral elements record past virus-host interactions, yet those derived from Baltimore class I double-stranded DNA viruses remain poorly explored in eukaryotes. Here, we identified 781,111 viral regions in 7,103 eukaryotic genome assemblies, revealing their broad but uneven distribution across both eukaryotic host lineages and viral taxa. Viral regions showed pronounced accumulation in several invertebrates, where they accounted for up to 16% of a single genome assembly. Taxonomic analyses of viral regions revealed 72 previously unrecognized endogenization-based associations between viral major taxa and eukaryotic lineages, including those involving invertebrates such as bivalves, starfish, sponges, flatworms, and ribbon worms, as well as diverse algae. In addition, we uncovered cases of iridoviral endogenization in ray-finned fishes. Overall, our study provides a baseline view of the landscape of the endogenous dsDNA virosphere across both eukaryotic and viral lineages.

microbiology↗

bioRxiv2025

Recognition-dependent activation of the RRS1-R/RPS4 immune receptor complex

The Arabidopsis TIR-NLR immune receptors RPS4 and RRS1 function together to enable recognition of multiple effector proteins including AvrRps4 and PopP2. We show here that both in the presence and absence of effector, RPS4 and RRS1 form an oligomer that does not change in size upon effector provision. Oligomer formation involves interactions between the RPS4 and RRS1 TIR domains and requires nucleotide binding capacity in RPS4. RPS4 mutants that lose TIR domain NADase activity abrogate immune activation but retain oligomerization. A cysteine residue in the RPS4 LRR domain contributes to oligomer stabilization. We propose that upon effector recognition, conformational changes in the complex relieve inhibition of RPS4 TIR domains by RRS1 TIR domains, enabling proximity between RPS4 TIR domains to create NADase activity.

plant biology↗

bioRxiv2025

BRAPH 2: a flexible, open-source, reproducible, community-oriented, easy-to-use framework for network analyses in neurosciences

As network analyses in neuroscience continue to grow in both complexity and size, flexible methods are urgently needed to provide unbiased, reproducible insights into brain function. BRAPH 2 is a versatile, open-source framework that meets this challenge by offering streamlined workflows for advanced statistical models and deep learning in a community-oriented environment. Through its Genesis compiler, users can build specialized distributions with custom pipelines, ensuring flexibility and scalability across diverse research domains. These powerful capabilities will ensure reproducibility and accelerate discoveries in neuroscience.

neuroscience↗

bioRxiv2025

PepPCBench is a Comprehensive Benchmark for Protein-Peptide Complex Structure Prediction with AlphaFold3

Peptides mediate up to 40% of protein-protein interactions (PPIs), offering high specificity and the ability to target binding sites inaccessible to small molecules, making them promising candidates for drug development. Accurate modeling of protein-peptide complexes is crucial for understanding fundamental biological processes and for advancing peptide-based drug design. However, due to the high conformational flexibility of peptides, predicting their interactions with proteins remains a significant challenge. Recent advancements in artificial intelligence (AI), exemplified by all-atom protein folding neural networks (PFNNs) such as AlphaFold3 (AF3), have expanded predictive capabilities beyond proteins to encompass protein-peptide complexes. Nevertheless, existing evaluations of these methods are often limited in scope and lack systematic, multi-dimensional, and fair comparisons of PFNN performance in protein-peptide complex prediction. Here, we introduce PepPCBench, a comprehensive benchmark framework specifically developed to evaluate AF3s capabilities in predicting protein-peptide complexes. This study utilizes a carefully curated dataset named PepPCSet, which is excluded from the AF3s training or validation sets. This dataset includes 261 protein-peptide complexes with peptide lengths ranging from 5 to 30 residues. Our benchmark results indicate that AF3 outperforms other PFNNs in prediction accuracy and structural validation. However, its performance remains insufficient for practical peptide drug discovery, indicating room for improvement. It is expected that PepPCBench can provide some valuable insights into the enhancement of protein-peptide complex structure prediction and the development of peptide-based therapeutics. The dataset and pipeline protocols are available at https://github.com/zhaisilong/PepPCBench.

bioinformatics↗

bioRxiv2025

Influence of Macrocyclization Strategies on DNA-Encoded Cyclic Peptide Libraries

Discovery of cyclic peptide hits using DNA-encoded libraries (DELs) has recently been extensively researched, with significant efforts directed toward developing DEL-compatible macrocyclization methods. To investigate how different cyclic linkers influence DEL selection outcomes, we constructed eight distinct sub-libraries and screened them against two protein targets, MDM2 and GIT1, resulting in two representative yet contrasting scenarios. Validation studies for MDM2 revealed that structural similarity patterns observed across multiple sub-libraries could enhance confidence in the authenticity of identified hits. Notably, cmp-10 demonstrated potent inhibitory activity, exhibiting an inhibition constant of 11 nM. In contrast, selections against GIT1 produced discrete enrichment patterns. From these outcomes, two exemplary compounds were selected and validated through both on-DNA and off-DNA modes. cmp-17 was confirmed to bind specifically to the desired binding pocket, displaying a dissociation constant of 1.22 M. Furthermore, ITC experiments using mutant GIT1 proteins (GIT1L271A/L279A and GIT1L271L/L279L) provided additional insights into the mechanism by which cmp-17 disrupts the interaction between GIT1 and {beta}-Pix.

biochemistry↗

bioRxiv2025

PERADIGM: Phenotype Embedding Similarity-based Rare Disease Gene Mapping

Identifying genes associated with rare diseases remains challenging due to the scarcity of patients and the limited statistical power of traditional association methods. Here, we introduce PERADIGM (Phenotype Embedding similarity-based RAre DIsease Gene Mapping), a novel framework that leverages natural language processing techniques to integrate comprehensive phenotype information from electronic health records for rare disease gene discovery. PERADIGM employs an embedding model to capture relationships between ICD-10 codes, providing a nuanced representation of individual phenotypes. By utilizing patient similarity scores, it enhances the identification of candidate genes associated with disease-specific phenotypes, surpassing conventional methods that rely on binary disease status. We applied PERADIGM to the UK Biobank dataset for three rare diseases: autosomal dominant polycystic kidney disease (ADPKD), Marfan syndrome, and neurofibromatosis type 1 (NF1). PERADIGM identified additional candidate genes associated with ADPKD-related and Marfan syndrome-related phenotypes, some of which are supported by existing literature, and demonstrated enhanced signal detection for NF1-specific phenotypes beyond traditional methods. Our findings demonstrate the potential of PERADIGM to identify genes associated with rare diseases and related phenotypes by incorporating phenotype embeddings and patient similarity, providing a powerful tool for precision medicine and a deeper understanding of rare disease genetics and clinical manifestations.

genetics↗

bioRxiv2025

brain network dynamics during multi-task demands predict children math achievement

Dynamic reconfiguration of neural network and flexible information integration across multiple tasks has been considered critical to characterize individual difference in complex cognition and general intelligence. A promising and underexplored question is how these neurocognitive processes related to childrens academic achievements, a hallmark of high-order cognitive abilities that integrate attention, memory and problem-solving. By using of a multitasking paradigm which bridging outside- in and inside-out approaches, we investigated the dynamic neural mechanisms underlying two core domains of academic performance: math and reading. We first apply partial least squares regression (PLSR) to examine static neural patterns and find that the first latent component--reflecting a generalized brain functional system--predicts math achievement but not reading. The multiple-demand system and the somato-cognitive action network (SCAN) are consistently engaged across diverse task demands. Furthermore, we use a Hidden Markov Model (HMM) to examine dynamic features of brain activity and identify distinct integrated and segregated brain states. Notably, the segregated state--characterized by heightened cortical network segregation--is associated with better math performance. Information-theoretic analyses further reveal that greater complexity in the temporal sequence of the segregated brain functional networks, along with stronger cerebrocerebellar functional coupling, correlates with higher math achievement. By means of multitasks design, these findings suggest that flexible engagement of specialized brain network and automatic information processing is crucial for math learning in children.

neuroscience↗

bioRxiv2025

Deciphering complex interactions between LTR retrotransposons and three Papaver species using LTR_Stream

Long terminal repeat retrotransposons (LTR-RTs), a major type of class I transposable elements (TEs), are the most abundant repeat element in plants. The study of the interactions between LTR-RTs and the host genome relies on high-resolution characterization of LTR-RTs. However, for non-model species, this remains a challenge. To address this, we developed LTR_Stream for sub-lineage clustering of LTR-RTs in specific or closely related species, providing higher precision than current database-based lineage-level clustering. Using LTR_Stream, we analyzed Retand LTR-RTs in three Papaver species. Our findings show that high-resolution clustering reveals complex interactions between LTR-RTs and the host genome. For instance, we found autonomous Retand elements could spread among the ancestors of different subgenomes, like retroviruses pandemics, enriching genetic diversity. Additionally, we identified that specific truncated fragments containing transcription factors motifs such as TCP and bZIP may contribute to generation of novel TAD-like boundaries. Notably, our pre- and post-allopolyploidization comparisons show that these effects diminished after allopolyploidization, suggesting that allopolyploidization may be one of the mechanisms by which Papaver species cope with LTR-RTs. We demonstrated the potential application of LTR_Stream and provided a reference case for studying the interactions between LTR-RTs and the host genome in non-model plant species.

bioinformatics↗

bioRxiv2025

Construction of Complete Telomere-to-Telomere Genome Assembly of the Rabbit Using Haploid Embryonic Stem Cells

The rabbit owns commercial importance in meat and fur production, and also has long served as a valuable animal model in biomedical research. Yet, the complete assembly of a high-quality rabbit reference genome has not been established. Here, we present a telomere-to-telomere (T2T) genome assembly of the New Zealand White (NZW) rabbit, the most complete and accurate rabbit genome to date. Using a haploid embryonic stem cells (haESCs) to overcame the challenges of homologous sequence interference and structural variation, we generate a genome with a contig N50 of 137.71 Mb, anchoring all chromosomes with only three gaps, through integrating long-read sequencing (PacBio HiFi, Oxford Nanopore), Hi-C, and Illumina data. Quality assessments reveal near-complete coverage of the BUSCO mammalian gene set, with 99.61% completeness. Notably, this complete assembly allowed for the first comprehensive annotation of the rabbit immunoglobulin loci and major histocompatibility complex region, which were previously unresolved in earlier genome versions. The NZW-T2T assembly based on haESCs not only provides a critical resource for advancing rabbit-based research, but also set a new benchmark for de novo genome assembly for other animals with complex genome.

genomics↗

bioRxiv2025

Biosynthesis and Physiological Significance of Organ-Specific Flavonol Glycosides in Solanaceae

Flavonols are subclasses of flavonoids, with hundreds of structures identified in plants. This chemical diversity primarily arises from glycosylation, where sugars are selectively added to the flavonol backbone. While flavonol profiles vary across species and organs, the evolutionary forces shaping this chemodiversity and the physiological significance of specific glycosides remain a mystery. Here, we reveal that finely tuned transcriptional regulation and the sugar selectivity of glycosyltransferases drive the formation of distinct organ specific flavonol profiles and a specific flavonol is necessary for male fertility. In Solanaceae pollen, two flavonol glycosides, K2 (kaempferol 3-O-glucosyl(1 [->] 2)galactoside) and Q2 (quercetin 3-O-glucosyl(1 [->] 2)galactoside), are exclusively accumulated. K2 is evolutionarily conserved, while Q2 was lost over time. Consistently, K2 is essential for male fertility, whereas Q2 and aglycones fail to rescue fertility defects. These findings suggest that individual flavonol glycosides have distinct physiological roles, either actively maintained or discarded through evolutionary selection.

plant biology↗

bioRxiv2025

pgxRpi: an R/Bioconductor package for user-friendly access to the Beacon v2 API

MotivationThe Beacon v2 specification, established by the Global Alliance for Genomics and Health (GA4GH), consists of a standardized framework and data models for genomic and phenotypic data discovery. By enabling secure, federated data sharing, it fosters interoperability across genomic resources. Progenetix, a reference implementation of Beacon v2, exemplifies its potential for large-scale genomic data integration, offering open access to genomic mutation data across diverse cancer types. ResultsWe present pgxRpi, an open-source R/Bioconductor package that provides a streamlined interface to the Progenetix Beacon v2 REST API, facilitating efficient and flexible genomic data retrieval. Beyond data access, pgxRpi offers integrated visualization and analysis functions, enabling users to explore, interpret, and process queried data effectively. Leveraging the flexibility of the Beacon v2 standard, pgxRpi extends beyond Progenetix, supporting interoperable data access across multiple Beacon-enabled resources, thereby enhancing data-driven discovery in genomics. Availability and ImplementationpgxRpi is freely available under the Artistic-2.0 license from Bioconductor (https://doi.org/doi:10.18129/B9.bioc.pgxRpi), with actively maintained source code on GitHub (https://github.com/progenetix/pgxRpi). Comprehensive usage instructions and example workflows are provided in the package vignettes, available at https://github.com/progenetix/pgxRpi/tree/devel/vignettes.

bioinformatics↗

bioRxiv2025

Plasma Protein Binding as an Optimizable Parameter for In Vivo Efficacy

Plasma protein is crucial for understanding pharmacokinetics, pharmacodynamics, and safety, yet it is often not recommended as a primary parameter for optimization in drug design. This study challenges this view by revisiting established pharmacokinetic models to elucidate a bidirectional strategy for clearance-dependent optimization of plasma protein binding. The analysis shows that strategically modulating plasma protein binding can enhance drug efficacy and safety, supporting its consideration as a key factor in the drug design process.

biophysics↗