Biology subjects

Zhao, J.

Publications and source records attributed to Zhao, J..

At least 145 records · Page 8Linked to original sources

bioRxiv2025

Mice and humans evaluate odor stimulus strength using common psychophysical principles

Sensory systems translate physical stimuli from the environment--such as light, sound, or chemicals--into signals that the brain can interpret. Across these systems, the amplitude of a stimulus is represented by its perceived intensity. Although previous research has extensively studied how the brain represents physical stimuli, less is known about how it represents perceptual variables such as stimulus intensity. This is primarily due to the difficulty in measuring perceptual responses in animal models, where neural recordings are more accessible. In this study, we use mouse olfaction as a model system to develop a framework for measuring perceived odor intensity. We begin by employing a two-odor concentration classification task to demonstrate that both mice and humans assess stimulus amplitude using a common perceptual scale. We then show that this scale corresponds to intensity. Finally, we apply this method to determine isointense concentrations of different odorants in mice. Our approach offers a powerful tool for testing hypotheses about the neural mechanisms underlying perceived odor intensity, potentially enhancing our understanding of olfactory processing and its neural substrates.

neuroscience↗

bioRxiv2025

UHRF1 restricts HCoV-229E infection through epigenetic silencing of the viral receptor APN

The emergence of SARS-CoV-2 has posed significant threats to global health, particularly for the older population. Similarly, common human coronaviruses, such as HCoV-229E, which typically cause mild cold-like symptoms, have also been linked to severe diseases, underscoring the need to understand virus-host interactions and identify host factors contributing to viral pathogenesis and disease progression. In this study, we performed a genome-wide CRISPR knockout screen using HCoV-229E and identified Ubiquitin-like with PHD and RING finger domain 1 (UHRF1) as a potent restriction factor. Mechanistically, UHRF1 suppressed HCoV-229E infection by downregulating the expression of its cell entry receptor, aminopeptidase N (APN), through promoter hypermethylation. Focused CRISPR activation screens of UHRF1-downregulated genes confirmed the critical role of APN in HCoV-229E infection and identified additional genes (e.g., SIGLEC1, PLAC8, and heparan sulfate biosynthesis genes) contributing to the restrictive functions of UHRF1. Transcriptomic and single-cell RNA sequencing analysis revealed that UHRF1 expression decreases with age, negatively correlating with increased APN expression. This age-related decline in UHRF1 was further validated in primary alveolar macrophages isolated from elderly individuals, which exhibited heightened susceptibility to HCoV-229E infection compared to those from younger individuals. Our findings highlight UHRF1 as a key age-related host defense factor against coronavirus infection and provide novel insights into the epigenetic regulation of viral entry receptors.

microbiology↗

bioRxiv2025

Single-Cell Super-Resolution Quantification of Oxidative DNA Damage via Aptamer-Assisted DNA-PAINT

Accurately quantifying oxidative DNA damage at the single-cell level remains a major challenge due to the limitations of conventional ensemble-based assays, which obscure cell-to-cell variability and lack molecular specificity. To address this, we developed a super-resolution imaging strategy that combines an 8-oxo-dG-specific DNA aptamer with DNA-PAINT, enabling quantitative visualization of 8-oxo-dG lesions with [~]22 nm spatial resolution in individual cells. Our approach reliably detects 8-oxo-dG clusters, distinguishes oxidative damage levels across cells, and exhibits superior specificity and labeling efficiency compared to antibody-based methods. Furthermore, it enables direct evaluation of the repair efficiency of hOGG1 and its catalytic mutants at the single-cell level, overcoming the confounding effects of variable transfection efficiency. This method also serves as a platform for assessing the effects of pharmacological modulators and antioxidants on DNA repair. Looking forward, this strategy can be extended to map other small molecular targets, such as epigenetically modified DNA or RNA bases, with single-cell precision.

biochemistry↗

bioRxiv2025

Recombinant proteasome provides new avenues for anti-malarial drug development.

The Plasmodium falciparum 20S proteasome (Pf20S) has emerged as a promising antimalarial target. Development of therapeutics to this target has previously relied on native purifications of Pf20S, which is challenging and has limited the scope of previous efforts. Here, we report an effective recombinant Pf20S platform to facilitate drug discovery. Proteasome assembly was carried out in insect cells by co-expressing all fourteen subunits along with the essential chaperone homolog, Ump1. Unexpectedly, the isolated proteins consisted of both a mature and an immature complex. Cryo-EM analysis of the immature complexes revealed structural insights detailing how Ump1 and the propeptides of the {beta}2 and {beta}5 subunits coordinate {beta}-ring assembly, which differ from human and yeast homologs. Biochemical validation confirmed that {beta}1, {beta}2, and {beta}5 subunits of the mature proteasome were catalytically active. Clinical proteasome inhibitors, bortezomib, carfilzomib and marizomib were potent but lacked Pf20S selectivity. However, the tripeptide-epoxyketone J-80 inhibited Pf20S {beta}5 with an IC50 of 22.4 nM and 90-fold selectivity over human {beta}5. Structural studies using cryo-EM elucidated the basis for the selective binding of J-80. Further evaluation of novel Pf20S-selective inhibitors such as the reversible TDI-8304 and irreversible analogs, 8304-vinyl sulfone and 8304-epoxyketone, confirmed their potency and selectivity over the human constitutive proteasome. This recombinant Pf20S platform facilitates detailed biochemical and structural studies, accelerating the development of selective antimalarial therapeutics.

biochemistry↗

bioRxiv2025

Improving polygenic risk prediction performance through integrating electronic health records by phenotype embedding

Large-scale biobanks provide comprehensive electronic health records (EHRs) that capture detailed clinical phenotypes, potentially enhancing disease risk prediction. However, traditional polygenic risk score (PRS) methods rely on simplified phenotype definitions or predefined trait sets, limiting their ability to represent the intricate phenotypic structures embedded within EHRs. To address this gap, we introduce a general framework, EEPRS, that leverages phenotype embeddings derived from EHRs to improve genetic risk prediction using only genome-wide association study (GWAS) summary statistics, enabling accurate, robust and interpretable risk prediction for a wide range of diseases. Employing embedding methods such as Word2Vec and GPT, we conducted EHR embedding-based GWAS and identified a distinct cardiovascular cluster via hierarchical clustering of genetic correlations. Across 41 clinical traits in the UK Biobank, our EEPRS framework consistently outperformed traditional single-trait PRS, particularly within this identified cluster. Validation using PRS-based phenome-wide association studies (PRS-PheWAS) further confirmed robust associations between EHR embedding-based PRS and circulatory system diseases. Furthermore, our data-adaptive method, EEPRS_optimal, employing cross-validation to select the best embedding method, leading to additional improvements in prediction. We further developed MTAG_EEPRS for multi-trait PRS, resulting in averaging 92.48% improvement in R2 for continuous traits and 24.06% in AUC for binary traits compared to single-trait PRS. Overall, EEPRS represents a robust and interpretable framework, enhancing genetic prediction accuracy through integrating EHR embeddings with single-trait and multi-trait PRS.

genetics↗

bioRxiv2025

Two mutations in the same MYC-bHLH transcription factor cause segregation of purple coloration of stolons and seed heads in Zoysia japonica x Zoysia matrella F2 and F1 populations

Anthocyanins play diverse roles in plants, including attracting pollinators and protecting cells from oxidative damage. In zoysiagrass, a warm season turfgrass, their accumulation in seed heads and stolons can decrease the aesthetic appeal. In this study, a high-density genetic map with [~]8000 single nucleotide polymorphism (SNP) markers organized into 20 linkage groups was generated in a Zoysia japonica acc. Meyer x Zoysia matrella acc. PI 231146 F2 population. Using this genetic map, a large-effect quantitative trait locus (QTL) for anthocyanin variation in stolons and seed heads was mapped to chromosome 12 (PP locus). Variant analysis of a candidate gene for PP, Zjn_sc00004.1.g07010.1.sm.mk, which encodes a MYC-bHLH transcription factor that regulates anthocyanin biosynthesis, revealed a SNP at an exon-intron boundary in Meyer that led to intron retention. Interestingly, an F1 population derived from the same parents segregated for seed head color but uniformly displayed purple stolons. Seed head color in the F1 population co-mapped with the PP locus which, combined with genotypic and yeast two-hybrid analyses, revealed that a SNP in PI 231146 leading to an Ala163Ser substitution in the MYB-interacting N-terminal domain of the same MYC-bHLH transcription factor was likely causal. The Ala163Ser substitution affected interaction of MYC-bHLH with MYB in a MYB-dependent manner. The identified mutations can be exploited to develop cultivars with green seed heads and stolons. The high-marker-density interspecific Z. japonica x Z. matrella F2 genetic map also provides a robust tool for identifying genomic regions and genes of agronomic interest that differentiate the two species.

genetics↗

bioRxiv2025

Superresolution imaging of live samples by centroid reassignment microscopy

Superresolution imaging has become one of the most important recent advances in microscopy development. However, most superresolution methods are ill-adapted for live-sample imaging because they are unacceptably slow, susceptible to artifacts, or require the use of specialized fluorophores and labeling protocols. We introduce a superresolution method called centroid reassignment microscopy (CRM) that overcomes these limitations. CRM is a simple variation on confocal microscopy wherein the single-element detector and small pinhole are replaced by a centroid detector and larger pinhole. Superresolution is obtained by reassigning the detected fluorescence to its centroid location of as a function of the scanning excitation focus location. Our method bears resemblance to the method of image scanning microscopy, which involves the use of an array detector, with the advantage that CRM provides improved resolution for the same number of detected photons while being simpler to implement and less sensitive to noise. CRM is light-efficient, fast (single-frame), robust to defocus aberrations, and requires no changes whatsoever in standard fluorescence imaging protocols, making it uniquely attractive for superresolution imaging of live, dynamic samples.

bioengineering↗

bioRxiv2025

The interplay between biomechanics and cell kinetics explains the spatial pattern in liver fibrosis

The formation of liver fibrosis patterns, characterized by excess extracellular matrix (ECM), is a complex process that is difficult to investigate experimentally. To complement experimental approaches, we developed a digital twin (DT) model to simulate the pattern formation of septal and biliary fibrosis, the two common forms of liver fibrosis. This model is based on iterative calibration with experiments from animal models treated with the hepatotoxic substance CCl4 (septal form) and Abcb4-knockout mice (biliary form). Septal fibrosis is characterized by ECM accumulation along the connective line between the central veins of neighboring liver lobules, while biliary fibrosis is marked by a scattered ECM pattern within the portal fields. This mechanistic DT model includes the components of hepatocytes (Heps{spadesuit}), hepatic stellate cells (HSCs), macrophages (Mphs), bile duct (BD) cells, collagen fibers secreted by activated HSCs, blood vessels, and cell-cell communication. It allows for the integration and simultaneous modulation of multiple hypothesized mechanisms underlying fibrotic wall formation. The model simulates the formation of liver fibrosis pattern and demonstrates that ECM distribution results from the pattern of cell death zones and biomechanical compression due to cell proliferation. "Healthy" Heps proliferate to compensate for cell loss. In septal fibrosis, where the cell death zones are several cells thick, the proliferating Heps surrounding a zone mechanically compress the deposited collagen network. After a transient phase of collagen scattered between/around Heps, the ECM eventually adopts a sharp, "wall"-like structure. Whereas, in biliary fibrosis, the pattern of cell death is more scattered, leading to a corresponding scattered ECM pattern. In this case, a pattern of scattered distributed collagen forms without transitioning to a sharp wall. Notably, the failure of fibrotic wall formation in endothelial cell-specific GATA4LSEC-KO mice, due to the disrupted pattern of CYP2E1-expressing Heps, validates our DT model. In conclusion, the DT model provided a deeper understanding of liver fibrosis pattern formation. It enabled comparison between simulated outcomes of hypothesized mechanisms and experimental data. Additionally, it guided the design of validation experiments and enabled the identification of optimal strategies for drug testing and extrapolation to humans.

systems biology↗

bioRxiv2025

CIAdex: Single-Cell FTIR Spectral Fingerprinting for Cell Identity Verification and Aging Quantification in Therapeutic Cell Manufacturing

Ensuring the identity and optimal aging state of cell products is critical for the efficacy and safety of cell therapies. Despite rapid iterations, there remains an urgent need for robust and easy-to-implement tests to characterize cell products. Here, we present CIAdex (Cell Identification and Aging Index), an analytical framework that utilizes single-cell Fourier-transform infrared (FTIR) spectral fingerprints and machine learning to achieve precise label-free cell identity assessment and aging quantification. CIAdex employs a Feature Extraction Processor to automatically extract FTIR spectral variables corresponding to distinct cellular biomolecular features, enabling reliable distinction of lineage-, donor-, and batch-specific cell populations using linear discriminant analysis. Through application of the XGBoost algorithm, a quantitative aging index (trPDL/trPN) was further generated for tracking cellular aging dynamics along culture expansion. Notably, trPDL/trPN quantitatively represent subtle age-related shifts among different batches and drug-induced senescence or rejuvenation effects which are unmeasurable by existing methods. Together, our work demonstrates that CIAdex, by simultaneous label-free identity verification and aging quantification of cell populations, offers a transformative approach to interpret single-cell FTIR spectral fingerprints and provides novel metrics for quality control in cell manufacturing with significant potential for optimization and assurance of cell therapies safety and efficacy.

cell biology↗

bioRxiv2025

Population-level super-pangenome reveals genome evolution and empowers precision breeding in watermelon

Pangenomes are increasingly critical for harnessing crop genetic diversity, yet their resolution and utility are often limited by insufficient sampling of high-quality genome assemblies. Here, we report a population-level watermelon super-pangenome constructed from 138 reference-grade assemblies, including 135 newly generated near-gapless genomes representing all seven watermelon species. The super-pangenome captures approximately one million structural variants (SVs), enabling accurate variant genotyping across ~900 watermelon accessions and substantially expanding variant discovery both across and within species. Broader sampling within the pangenome provides insights into genome evolution among watermelon species and sheds light on the origin of cultivated watermelon. SV-inclusive genome-wide association studies enhance trait mapping resolution and identify a copy number variation upstream of ClFCI1 that regulates flesh color intensity in a dosage-dependent manner. Leveraging this comprehensive variation map, we developed high-accuracy genomic prediction models for 18 agronomic traits. Together, our findings and genomic resources establish a foundational framework for dissecting complex traits and accelerating precision breeding in watermelon, while offering a valuable model for SV-resolved pangenomics in crop species.

plant biology↗

bioRxiv2025

APOE knockout attenuates vascular graft fibrosis by limiting profibrotic macrophage formation through low-density lipoprotein receptor related protein 1

Vascular graft fibrosis can cause a decrease in cellular infiltration and capillary ingrowth in vascular walls and vascular stiffening. As such, there are still no vascular grafts that can be used in blood vessels where their diameters are less than 6 mm in patients. Although various approaches have been evaluated to mitigate implant-associated fibrosis, effective treatments remain quite limited. In this study, we demonstrated that APOE was significantly increased during vascular regeneration after graft implantation in vivo. APOE knockout (KO) increased compliance of regenerated aortas and reduced extracellular matrix (ECM) deposition in adventitia of the regenerated aortas. Using single cell RNA sequencing (scRNA-seq), a subset of profibrotic macrophages was found to be involved in graft fibrosis and APOE KO limited the formation of profibrotic macrophage formation during vascular regeneration. The interaction between APOE and low-density lipoprotein receptor related protein 1 (LRP1) partially mediated fibrotic differentiation of the macrophages. Profibrotic macrophages promoted graft fibrosis mainly through secretion of insulin-like growth factor-1 (IGF-1) that could support proliferation of fibroblasts. Finally, we showed that APOE knockdown in vivo using adeno-associated virus (AAV) improved the compliance of regenerated aortas and reduced ECM deposited in the adventitial areas by limiting formation of profibrotic macrophages. Collectively, these data indicate that APOE promotes the profibrotic transition of macrophages partially through LRP1, and the profibrotic macrophages increase the proliferation of fibroblasts via IGF-1. Inhibition of APOE by AAV can alleviate graft fibrosis occurring during vascular regeneration.

bioengineering↗

bioRxiv2025

Targeting nucleolin facilitates the development of blood-tumor barrier-penetrating and glioblastoma-specific PROTACs

Targeted therapy for glioblastoma (GBM) is challenged by the blood-tumor barrier (BTB). Extracellular vesicles (EVs) from GBM cells play a role in transforming BBB into BTB, although the mechanisms are not fully understood. This study identifies nucleolin (NCL), a nucleomembrane shuttling protein, as being transferred from GBM cells to the surface of brain capillary endothelial cells, facilitating BTB formation. The aptamer AS1411, which targets NCL, is shown to cross the BTB via receptor-mediated transcytosis (RMT) and selectively recognize GBM cells in an NCL-dependent manner. Beyond its targeting capabilities, AS1411 has been repurposed to recruit the E3 ligase MDM2 in PROTACs, leveraging the intracellular interaction of NCL with MDM2. Utilizing AS1411s multifunctionality in BTB penetration, GBM cell targeting, and MDM2 recruitment, we conjugated AS1411 to VEGFR2 or EGFR ligands to create PROTAC degraders. These constructs induce NCL- and MDM2-dependent ubiquitination and degradation of VEGFR2 or EGFR, demonstrating significant anti-GBM efficacy both in vitro and in vivo, with no toxicity to normal cells. Overall, NCL from GBM-derived EVs emerges as a crucial mediator of BTB formation and serves as a receptor for AS1411-mediated RMT, paving the way for developing PROTACs that effectively traverse BTB and target GBM. GRAPHICAL ABSTRACTS O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/666084v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@79778org.highwire.dtl.DTLVardef@6984d8org.highwire.dtl.DTLVardef@1909eddorg.highwire.dtl.DTLVardef@1adf342_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Graph pangenome reveals structural variation dynamics during cucumber breeding

Structural variants (SVs) represent a significant yet underexplored component of plant genome diversity. Analyzing SV dynamics is important for understanding their contributions to phenotypic variation and their influence on genome evolution. Here, we present a graph-based pangenome for cucumber, constructed from 39 reference-level genome assemblies. The pangenome captures 171,892 high-confidence SVs and enables genotyping of these SVs across 443 cucumber accessions, representing diverse geographic origins and spanning wild progenitors, landraces, and modern cultivars. Our analyses reveal that during cucumber domestication, a substantial portion of mildly deleterious SNPs are retained, whereas SVs are consistently purged, highlighting their potentially highly deleterious nature. During the geographic expansion of cucumber, a reduction in SV burden and a younger age of SVs than SNPs were observed, suggesting stronger purifying selection acting on SVs. Additionally, notable gene flow from wild population to African and European populations was detected, resulting in an increased SV burden, potentially due to hitchhiking effects. Importantly, incorporating SV burden into genomic prediction models improved the prediction accuracy for several agronomically important traits. This study illuminates the dynamics of SVs during cucumber domestication and improvement, highlights the complex interplay between SVs and selection pressures, and underscores the extensive implications of SVs for future cucumber breeding.

plant biology↗

bioRxiv2025

Open Benchmarking for Cell-Based Multiscale Models: Lessons from a Community Initiative

The emergence of virtual human twins (VHT) in biomedical research has sparked interest in multiscale in silico modelling frameworks, particularly in their application bridging cellular to tissue levels. Among the diverse array of multiscale modelling tools, off-lattice center-based agent-based models (CBM) offer a promising approach due to their depiction of cells in 3D space, closely resembling biological reality. Despite the proliferation of CBM tools addressing various biomedical challenges, a comprehensive and systematic comparison among them has been elusive. This paper presents a community-driven benchmark initiative aimed at evaluating and comparing CBM for biomedical applications, akin to successful efforts in other scientific domains such as the Critical Assessment of Protein Structure Prediction (CASP). Enlisting developers from leading tools like BioDynaMo, Chaste, PhysiCell,TiSim, and CompuTiX, we devised a benchmark scope, defined metrics, and established reference datasets to ensure a meaningful and equitable evaluation. Unit tests targeting different solvers within these tools were designed, ranging from diffusion and mechanics to cell cycle simulations and growth scenarios. Results from these tests demonstrate varying tool implementations in handling diffusion, mechanics, and cell cycle equations, emphasising the need for standardised benchmarks and interoperability. Discussions among the community underscore the necessity for defining gold standards, fostering interoperability, and drawing lessons from analogous benchmarking experiences. The outcomes, disseminated through a public platform in collaboration with OpenEBench, aim to catalyse advancements in computational biology, offering a comprehensive resource for tool evaluation and guiding future developments in cell-level simulations. This initiative endeavours to strengthen and expand the computational biology simulation community through continued dissemination and performance-oriented benchmarking efforts to enable the use of VHT in biomedicine.

systems biology↗

bioRxiv2025

Microbial Tryptophan Metabolism Activates Host Lysosomal Activity to Facilitate Lipid Breakdown and Ameliorate Hepatic Steatosis

Lysosomes are central to lipid metabolism, yet how gut microbiota-derived metabolites regulate lysosomal function to influence host lipid homeostasis remains unknown. Here, we identify an evolutionarily conserved mechanism in which bacterial tryptophan metabolism activates lysosomal activity to promote lipid breakdown. By developing a lysosomal-responsive lipid reporter in C. elegans to screen for bacterial metabolic states that modulate host lipid storage, we discover that E. coli tryptophan catabolism via tryptophanase TnaA induces lysosomal lipid chaperone LBP-8, driving lipid mobilization. Moreover, tryptophan metabolites enhanced lysosomal acidification and degradation capacity, while genetic disruption of lysosomal regulators reversed these effects. Strikingly, bacterial tryptophan metabolism further promoted mitochondrial {beta}-oxidation through lysosomal lipase activity. This pathway was conserved in mammalian hepatocytes, where E. coli-derived tryptophan metabolites enhance lysosomal function and reduce lipid accumulation. In high-fat diet mice, restoring gut bacterial tryptophan metabolism alleviated hepatic steatosis. Our work uncovers microbiota-regulated lysosomal activation as a critical axis in lipid homeostasis, highlighting its potential as a therapeutic target for metabolic disorders linked to lysosomal dysfunction. SignificanceWe uncover a conserved mechanism by which microbial tryptophan metabolism enhances lysosomal function to maintain host lipid homeostasis. Specifically, we demonstrate that bacterial tryptophan catabolism--via the enzyme TnaA--promotes lysosomal acidification, proteolytic capacity, and structural remodeling in C. elegans, driving lipid breakdown through the lysosomal chaperone LBP-8. This activation boosts mitochondrial {beta}-oxidation and reduces lipid storage. Importantly, the same pathway operates in mammalian hepatocytes and in a high-fat diet mouse model, where restoring bacterial tryptophan metabolism markedly alleviates hepatic steatosis. Our findings bridge microbial metabolism and lysosomal dynamics, offering fresh insights into host-microbe crosstalk and metabolic regulation. HighlightsTnaA-mediated bacterial tryptophan catabolism promotes lipid mobilization via lysosomal chaperone. Bacterial tryptophan metabolites boost lysosomal function, lipid breakdown, and mitochondrial {beta}-oxidation. Conserved microbiota-lysosome-lipid axis from worms to mammalian liver. Restoring gut bacterial tryptophan metabolism alleviates hepatic steatosis in high-fat diet mice.

cell biology↗

bioRxiv2025

Accelerating Virtual Directed Evolution of Proteins via Reinforcement Learning

With the advancement of machine learning methods, the protein fitness landscape can be predicted, providing reliable guidance in the selection of advantageous mutations for the directed evolution of proteins. However, the potential multiple mutational variants derived from the simple combi-nation of a limited number of advantageous single mutations may not represent superior choices. Moreover, the exploration and selection of the astronomical number of multiple mutational variants remain a highly challenging task. In this study, we introduce a virtual directed evolution pipeline, RelaVDEP, for the rapid identification of mutational variants with explicit enhancement in the de-sired property of the target protein. By adapting and fine-tuning a pre-trained fitness predictor to improve sequence-based protein functional prediction and by designing a model-based reinforce-ment learning framework to efficiently explore the vast combinatorial space of protein mutations, this pipeline is capable of effectively accelerating the directed evolution process for a broad spec-trum of proteins with versatile functional profiles. According to a series of experimental validations, the diversified mutational variants identified by our method exhibit notable improvements in desir-able protein functional properties. In particular, by integrating RelaVDEP with active learning, we successfully optimized the sequence of a PETase, enhancing its catalytic activity through previously unknown mutations.

bioinformatics↗

bioRxiv2025

Ultra-fast and Efficient Network Embedding for Gigascale Biological Datasets

Graph/network representation learning (or graph/network embedding) is a widely used machine learning technique in industry recommending systems and has recently been applied in computational biology. Popular network representation learning algorithms include random walk and matrix factorization methods, but they do not scale well to large networks. To accommodate the fast growth of real-world network datasets, especially biological datasets, we engineered and improved several network embedding algorithms via intensive computational optimization (e.g., randomized-generalized singular value decomposition/SVD, efficient sketching via ProbMinHash including edge weights) and parallelization to allow ultra-fast and accurate embedding of large- scale networks. We present GraphEmbed, a computer program for scalable, memory-efficient network embedding. GraphEmbed can perform embedding for large-scale networks with several billion nodes in less than 2 hours on a commodity computing cluster. We benchmark it against standard datasets and demonstrate consistent speed and accuracy advantages over state-of-the- art techniques. We also propose centric AUC, a new metric for evaluating link-prediction accuracy in network embedding. It corrects the bias in conventional AUC caused by the highly skewed node degree distributions, which are typically found in real-world networks, especially biological networks. Taken together, GraphEmbed solves a major challenge in large-scale network representation learning for networks in general and biological networks in particular.

bioinformatics↗

bioRxiv2025

Smartphone-integrated RPA-CRISPR-Cas12a Detection System with Microneedle Sampling for Point-of-Care Diagnosis of Potato Late Blight in Early Stage

Potato late blight, caused by the oomycete pathogen Phytophthora infestans, is one of the most devastating diseases affecting potato crops in the history. Although conventional detection methods of plant diseases such as PCR and LAMP are highly sensitive and specific, they rely on bulky and expensive laboratory equipment and involve complex operations, making them impracticable for point-of care diagnosis in the field. Here in this study, we report a portable RPA-CRISPR based diagnosis system for plant disease, integrating smartphone for acquisition and analysis of fluorescent images. A polyvinyl alcohol (PVA) microneedle patch was employed for sample extraction on the plant leaves within one minute, the DNA extraction efficiency achieved 56 g/mg, which is [~]3 times to the traditional CTAB methods (18 g/mg). The system of RPA-CRISPR-Cas12a isothermal assay was established to specifically target P. infestans with no cross-reactivity observed against closely-related species (P. sojae, P. capsici). The system demonstrated a detection limit of 2 pg/L for P. infestans genomic DNA, offering sensitivity comparable to that of benchtop laboratory equipment. The system demonstrates the early-stage diagnosis capability by achieving a [~]80% and 100% detection rate on the third and fourth day post-inoculation respectively, before visible symptoms observed on the leaves. The smartphone-based "sample-to-result" system decouples the limitations of traditional methods that rely heavily on specialized equipment, offering a promising way for early-stage plant disease detection and control in the field.

molecular biology↗