Biology subjects

Pan, Z.

Publications and source records attributed to Pan, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

Antiviral Sulfonated Polymersomes Inhibit Corona-, Influenza-, and Alphavirus

A broad-spectrum antiviral is required to enable rapid intervention against current, newly emerging and mutating viruses. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza A virus (IAV), Sindbis virus (SINV) and chikungunya virus (CHIKV) all engage negatively charged moieties on host cells, such as heparan sulfate (HS) and sialic acid, to facilitate attachment for entry and infection. We designed HS-mimetic nanoparticles in the form of polymer vesicles (polymersomes) by blending membrane-forming and sulfonated copolymers. These biocompatible polymersomes successfully inhibited host cell infection of HS-dependent SARS-CoV-2 and HS-independent IAV in vitro with sub-nanomolar potency. Multivalent interaction of several virus ligands with a single polymersome as quantified by fluorescence correlation spectroscopy likely contributes to their potency. The sulfonated polymersomes also functioned in vivo as demonstrated by a reduction in the spread of SINV, an alphavirus model for CHIKV, in zebrafish embryos, which led to a significantly increased survival. Hence, our polymersomes could form the basis for an urgently required multifunctional broad-spectrum antiviral nanomedicine.

bioengineering↗

bioRxiv2026

Monothiol glutaredoxin DaGrxB couples iron homeostasis with redox balance to orchestrate development and virulence in the peach shoot blight fungus Diaporthe amygdali

Peach shoot blight (PSB), caused by Diaporthe amygdali, is one of the most devastating fungal diseases affecting peach production worldwide. Glutaredoxins (Grxs) are ubiquitous redox-regulatory proteins involved in antioxidant defense and iron metabolism, yet their functions in phytopathogenic fungi remain largely unknown. Here, we identified four Grxs (DaGrxA-DaGrxD) in D. amygdali. Among them, DaGrxB, a monothiol glutaredoxin, is the sole member essential for vegetative growth, asexual reproduction and virulence. DaGrxB interacts with iron-regulatory transcription factors DaHapX and DaSreA and modulates their activities via iron-dependent nucleocytoplasmic shuttling, coordinating iron acquisition and storage gene expression. During infection, D. amygdali encounters a biphasic iron environment: an inherently iron-limited host environment at early stages (1-2 dpi) followed by host-mediated iron excess (3-4 dpi), and DaGrxB is essential for navigating this transition. Consistently, deletion of either DaHAPX or DaSREA significantly reduced virulence, confirming that iron homeostasis is a critical determinant of D. amygdali virulence. Notably, deletion of DaGRXB disrupts Fe-S cluster biosynthesis, causing mitochondrial Fe2+ accumulation, impaired aconitase activity, and reduced ATP production, which triggers constitutive activation of the GSH-dependent antioxidant system, with elevated GSH/GSSG ratios, increased GR and GST activities, and reduced H2O2 levels. Exogenous GSH and H2O2 depletion both reduced virulence, suggesting that disturbed redox balance directly contributes to attenuated virulence. Additionally, the Trx domain assists the Grx domain in nucleocytoplasmic translocation through intramolecular interactions. Finally, LDH-delivered dsRNA targeting DaGRXB effectively controls PSB. Collectively, these findings reveal how a monothiol glutaredoxin integrates iron homeostasis, redox balance and virulence in a plant pathogen, providing a foundation for RNAi-based disease management.

pathology↗

bioRxiv2026

Ultrafast and Accurate Selection of High-Quality Protein Complex Models from Large-Scale Prediction

In the post-AlphaFold era, advances in protein complex structure prediction have enabled the generation of thousands of candidate models per target, making the accurate and efficient selection of high-quality models from large candidate pools a critical challenge. Existing model-selection approaches commonly employ estimated model accuracy (EMA) methods, which include single-model and consensus-based methods. The former lack cross-model comparative information, whereas the latter often incur high computational costs from pairwise structural alignments and are influenced by candidate-pool quality. Here, we present DeepUMQA-Selection, a two-stage framework combining single-model accuracy estimators for preliminary selection with Ultrafast Shape Recognition (USR)-based structural alignment for consensus selection. Preliminary selection enriches the candidate pool with high-quality models and reduces subsequent structural comparisons, while USR-based structural alignment enables efficient consensus selection without explicit structural superposition. On the retrospective CASP16 QMODE3 benchmark dataset, DeepUMQA-Selection outperformed all participating methods and predictor-derived confidence metrics, as measured by mean Top-5 weighted penalty. The advantage was particularly pronounced for heteromeric targets, with a 13.9% improvement in model-selection performance relative to the best-performing CASP16 participating method. Furthermore, a confidence-assisted strategy enabled the framework to accommodate larger protein complexes while maintaining superior model-selection performance over competing methods, supporting its scalability. Runtime benchmarking showed that the proposed USR-based structural alignment achieved a substantial 317-fold speedup over US-align for candidate pools containing 1,000 models, demonstrating its computational efficiency for large-scale model selection. Our results demonstrate that DeepUMQA-Selection enables accurate and computationally efficient selection of high-quality protein complex models from large-scale candidate pools.

bioinformatics↗

bioRxiv2026

A multi-tissue epigenomic atlas links the sheep non-coding genome to domestication and complex traits

Non-coding regulatory variation drives complex traits, domestication, and evolutionary adaptation, yet the sheep genome lacks high-resolution functional annotation. Here we present SheepEpimap, a multi-tissue regulatory atlas harmonizing 516 CUT&Tag histone modifications, ATAC-seq, and RNA-seq datasets across 43 adult tissues in sheep. We annotated 2.93 million cis-regulatory elements, yielding 557,441 enhancer-gene pairs and 145,407 variants with allele-specific effects. By training a sequence-to-function deep-learning model, we decoded the base-pair syntax of chromatin accessibility, annotated transcription factor motif instances genome-wide, and constructed 12,210 tissue-specific gene regulatory networks (GRNs). Integrating this resource with multi-tissue expression quantitative trait loci, selection sweeps, and genome-wide association studies prioritized non-coding variants driving domestication and complex traits. Finally, cross-species analysis revealed that sequence-conserved, tissue-matched enhancers were significantly enriched in the heritability of complex traits and diseases in humans. In summary, SheepEpimap (https://genome.ucsc.edu/s/mengzhu/SheepEpimap) provides an open-access foundational ecosystem for sheep functional genomics, precision breeding, and comparative biology.

genetics↗

bioRxiv2026

Extracellular vesicle associated nucleic acids and proteins as a complementary fraction for fish biodiversity monitoring and physiological signal recovery

Monitoring the biodiversity and physiological condition of aquatic organisms under environmental stress is central to aquatic ecosystem management. Environmental nucleic acid methods (eDNA and eRNA) have transformed biomonitoring but face well-documented limitations, including susceptibility to degradation, difficulty distinguishing living from legacy or transported signals, and limited capacity to report organism condition. Extracellular vesicles (EVs) released from all kinds of species, are stable and widespread vesicles in environment, showing biomarker potential. Here we evaluate whether nucleic acids and proteins recovered from EVs provide a complementary capture fraction that helps address these limitations. All four targets (eDNA, eRNA, EV-DNA, EV-RNA) shared an identical 12S rRNA metabarcoding workflow and differed only in the pre-analytical capture step, filtration for bulk environmental nucleic acids versus sequential 0.22 {micro}m filtration, tangential flow filtration, and ultracentrifugation for the EV-encapsulated fraction. In a controlled aquarium, EV-DNA recovered all seven reference species (100% sensitivity) versus 85.7% for bulk methods, including a low-abundance detection of Anguilla japonica that warrants independent confirmation. In field sampling at Xinglinwan Reservoir, EV-RNA recovered 11 of 12 expected species (91.7% sensitivity) compared with 50-58% for bulk methods, including four reference taxa detected only by the EV fraction. EV-based methods also captured more even community representation, and EV-RNA showed lower human-read contamination than eDNA. Metaproteomic analysis of reservoir EVs recovered fish-derived proteins, dominated by Cyprinus carpio, including candidate stress-associated functions (chaperones, metallothioneins, oxidoreductases), independently corroborating the EV-based detection of C. carpio. Their abundance varied with seasons, suggesting their ability to respond to environmental changes. Together, these results indicate that EV-associated nucleic acids and proteins constitute an informative complementary fraction for integrated biodiversity and physiological-signal monitoring in impacted aquatic environments. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/735187v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@ee0e6borg.highwire.dtl.DTLVardef@f4d1daorg.highwire.dtl.DTLVardef@fd5123org.highwire.dtl.DTLVardef@da0ddc_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2026

Quantifying structural vulnerabilities and resilience to research integrity risks in biomedical research networks

Retractions of scientific articles reflect a rising research integrity risk in biomedical research, and continued citation of retracted articles poses a downstream risk to research that built upon unreliable studies. Few studies have examined the structural risk factors embedded in how the research enterprise is organized. Here, we apply an analytical risk assessment framework to structural risk factors associated with retracted articles and the propagation of their citations to ten broad fields of biomedical research, collectively accounting for 14% of biomedicine. We pronounced field-specific differences in retraction rates and citations to retracted articles, and show that multiple retractions from single authors occur far more often than chance would predict. By subdividing fields into finer-grained topics with machine learning network clustering, we find that individual authors can reach large proportions of the literature within their topics through citations, and that retraction risk is positively associated with author productivity. Across these fields, 22% of the literature cites authors with at least one retracted article, and 6% cite work from authors with multiple prior retractions. Together, these factors lead to a concentration of risk within topics, which is only partially explained by the uneven distribution of authors with multiple retractions. Despite these quantifiable vulnerabilities, the risk has not yet been fully realized: most topics cite retracted work no more than baseline. Our findings thus reveal both a latent network vulnerability to the rapid dissemination of questionable results and a measurable resilience that has so far kept this possibility in check. The scientific community would particularly benefit from targeted efforts to test and strengthen reproducibility in high-risk scientific topics.

scientific communication and education↗

bioRxiv2026

Targeting Lysosomal MCOLN1/TRPML1 Ion Channels to Finely Alleviate Diabetes Mellitus via a Ca 2+ - CaMKKβ-AMPK pathway

Type 2 Diabetes mellitus (T2DM) is a metabolic syndrome characterized by hyperglycemia and various complications. Current drugs are limited by side effects and resistance, necessitating novel targets and therapies. Previous studies have shown that MK-83, a synthetic agonist of transient receptor potential mucolipin 1 (TRPML1/ MCOLN1), a lysosomal Ca2+ channel, activates adenosine 5'-monophosphate-activated protein kinase (AMPK), a key therapeutic target in diabetes. However, whether targeting TRPML1 can treat T2DM remains unclear. In this study, we found that transgenic overexpression or pharmacological activation of TRPML1 finely controls AMPK phosphorylation via a Ca2+-CaMKK{beta}-dependent mechanism. This activation promotes glucose transporter 4 (GLUT4) translocation and dramatically increases intracellular glucose uptake. Conversely, genetic inactivation or pharmacologically inhibition of TRPML1 blocks both AMPK activation and glucose uptake. More importantly, pharmacological activation of TRPML1 in vivo dramatically alleviates hyperglycemia in db/db mice (a T2DM model), as evidenced by random blood glucose levels, fasting blood glucose levels and HbA1c levels. Furthermore, other hallmark features of db/db mice-including impaired oral glucose tolerance, reduced insulin tolerance, and elevated ALT and AST levels- were all ameliorated upon TRPML1 activation. Hence, targeting lysosomal TRPML1 channel represents a promising therapeutic strategy for T2DM, with highly specific TRPML1 agonists as potential anti-diabetic agents.

Cell Biology↗

bioRxiv2026

Crossmodal plasticity effects of hearing loss and ageing in the human auditory cortex

Crossmodal plasticity refers to the phenomenon by which typical sensory areas respond to stimulation in other sensory modalities when their main input is absent or reduced. In this study, we examined the effect of age-related hearing loss (ARHL) on crossmodal plasticity in auditory brain regions. ARHL is prevalent in a large proportion of older adults, and it is associated with an increased risk of cognitive decline and dementia. Understanding how the brain adapts to ARHL is essential in aiding the development of adequate therapies and interventions for a phenomenon experienced by most adults aged 65 and over. Previous research in congenitally deaf adults has shown that crossmodal plasticity effects are stronger for conditions that require higher executive demands. We conducted an fMRI experiment in older adults to determine whether crossmodal recruitment of auditory cortical regions during higher executive demands also occurs following ARHL. In the MRI scanner, participants with and without ARHL completed a visual task-switching paradigm and a visual working memory task, each comprising high and low executive-demand conditions. Results from both groups of participants showed that the high-demand conditions reliably activated canonical frontoparietal regions involved in executive function and cognitive control. Crucially, we also observed significant recruitment of auditory regions during these visual tasks, particularly under the higher executive demands condition of the task-switching paradigm. Crossmodal activations in auditory areas occurred in both groups, with no significant effects of hearing level or age. These findings indicate that auditory regions are involved in high executive demand visual processing, and that crossmodal plasticity effects are not restricted to early sensitive periods. We propose that reduced auditory input and age-related changes in cortical processing jointly contribute to the reorganisation of the auditory cortex in later life. Understanding these mechanisms is critical for clarifying the neural consequences of ARHL and their impact on cognitive decline.

neuroscience↗

bioRxiv2026

General, orders-of-magnitude faster whole-genome analysis with genotype representation graphs

Whole-genome sequencing (WGS) of biobank-scale cohorts have generated datasets that traditional tabular genotype formats cannot efficiently store or analyze. Genotype Representation Graphs (GRGs) offer a compelling alternative: a biologically-motivated, hierarchical, graph-based representation that compactly and losslessly encodes the genotypes, and that supports computation directly on the graph rather than on a materialized genotype matrix. Here we introduce two advances that together make GRG a practical foundation for biobank-scale population and statistical genetics. First, we present GRG v2, a substantially improved format and construction algorithm that reduces construction time by 10-20x, halves the disk and RAM footprint of the resulting files, and improves load time by more than 20x. Applied to the recently phased UK Biobank WGS dataset (490,541 individuals, 706,556,181 variants), GRG v2 produces files 25 times smaller than .vcf.gz and more than 8 times smaller than PLINK2s PGEN format, while costing less than 90 GBP to construct. Second, we introduce grapp, a Python library and command-line tool that exploits the computational advantages of GRG for both routine analyses and new method development. grapp provides standard pipelines for variant and sample filtering, genome-wide association studies (GWAS) with covariates, principal component analysis (PCA), and data export, all implemented as graph-based operations. Moreover, it provides linear operators that integrate with the numpy and scipy sparse linear algebra ecosystem, enabling implicit matrix multiplication against the standardized genotype matrix, the linkage disequilibrium matrix, and the genetic relatedness matrix all via an underlying GRG. Using these operators, scipy-based PCA can be implemented in four lines of Python and runs 51-492x faster than existing methods while using less RAM. PCA on 89,988,512 variants in the UK Biobank runs in two to four hours. This scalability allows us to introduce a leave-one-chromosome-out (LOCO) approach to GWAS covariate construction that avoids LD artifacts without requiring LD pruning. Together, GRG v2 and grapp enable a level of scalability and methodological flexibility that is not achievable with traditional genotype formats.

genetics↗

bioRxiv2026

Continental-scale multi-omics reveals a distinct microbial-viral biome in sandy beach ecosystems structured by tidal zonation

Sandy beaches are dynamic coastal interfaces shaped by strong physical forcing and intense exchange between marine and terrestrial environments, yet their microbiomes remain poorly resolved at the genomic scale. Here we present a genome-resolved survey of microbial and viral communities across sandy beaches spanning a continental-scale latitudinal gradient along the Chinese coastline. By integrating cross-shore sampling, coastal geochemistry and large-scale multi-omics, we generated 978 metagenomes, 63 viromes and 72 metatranscriptomes, reconstructing 13,337 metagenome-assembled genomes and 38,255 viral populations. Sandy beach microbiomes exhibit exceptionally high genomic novelty, with more than 90% of species-level genomes representing previously undescribed taxa, suggesting that permeable coastal sediments constitute a distinct microbial and viral reservoir. Tidal zonation emerged as a dominant ecological driver structuring microbial diversity, metabolic strategies and virus-host interactions across cross-shore gradients. Genome-resolved analyses revealed systematic metabolic shifts from oxic heterotrophy in supratidal sediments toward increasingly chemolithotrophic and autotrophic pathways toward the low-intertidal and subtidal zone. Sandy beach microbiomes further encode broad potential for hydrocarbon and plastic transformation, together with diverse biosynthetic and antibiotic resistance repertoires that may mediate microbial chemical interactions. Together, these findings identify sandy beaches as a previously under-recognized microbial-viral biome shaped by tidal forcing, providing insight into microbiome evolution and coastal ecosystem resilience under increasing anthropogenic pressure.

microbiology↗

bioRxiv2026

A Pangenome Centralized NLRome Drives Lineage Specific Diversification and Functional Differentiation in Solanoideae

The Solanoideae subfamily comprises numerous economically important crops that deploy a diverse repertoire of nucleotide-binding leucine-rich repeat receptors (NLRs) to defend against pathogens. However, the evolutionary trajectory of these NLR immune receptors remains poorly understood. Here, we dissect NLR family evolution across 23 Solanoideae species by integrating genomic and transcriptomic data. We uncover a distinctive pangenome-centralized architecture wherein a mere 11.56% of soft-core orthogroups contribute 67.81% of the total NLR repertoire, with copy-number variation driven by lineage-specific expansions rather than stochastic distribution or genome size. This finding indicates that Solanoideae plants counteract biotic stress through targeted amplification of specific key families rather than undergoing broad diversification. Phylogenomic analysis resolves NLRs into four subclasses, revealing CC-NLR dominance across all species alongside extreme TIR-NLR degeneration in certain lineages, suggesting profound immune adaptive shifts. Mechanistically, recent NLR expansions are primarily fueled by proximal and tandem duplications, whereas whole-genome duplications contribute in a ploidy- and time-dependent manner, with WGD-derived genes undergoing extensive loss during diploidization. Transcriptional profiling further demonstrates duplication-type-dependent expression patterns, enabling a robust, multilayered defense network upon pathogen infection. Collectively, our study delineates the asymmetric evolutionary trajectory of the Solanoideae NLRome and establishes a framework model that provides new insights into the adaptive evolution of plant immune systems and resistance gene discovery.

genomics↗

bioRxiv2026

The enhanced multi-tissue atlas of regulatory effects in cattle

Cattle are integral to global food security, yet the molecular architecture of their complex traits remains poorly understood. Here, we present the Cattle Genotype-Tissue Expression (CattleGTEx) Phase 1 resource (https://cattlegtex.farmgtex.org/), a substantial expansion of the pilot study. By leveraging 12,422 RNA-seq profiles across 43 tissues and 82 breeds, we characterized 433,972 primary and 161,428 non-primary regulatory effects spanning seven molecular phenotypes. This high-resolution atlas resolves 75% of GWAS signals for 44 complex traits, significantly addressing the "missing regulation" in livestock. We propose a genetic regulatory model demonstrating how variants across multiple biological layers interact with specific biological contexts to shape phenotypic variation. Furthermore, CattleGTEx elucidates mechanisms underlying adaptive evolution between Bos taurus and Bos indicus, as well as artificial selection in dairy and beef breeds. Finally, by mapping evolutionary constraints on these regulatory effects, we demonstrate the translational value of this resource for prioritizing causal variants in human complex diseases. Together, Phase 1 of CattleGTEx provides a transformative framework for functional genomics, precision breeding, and comparative genetics.

genetics↗

bioRxiv2025

Evaluate and simulate the reproduction and survival of a reintroduced population in an endangered bird species Crested ibis (Nipponia nippon)

O_LIReintroduction programs are critical for restoring endangered species. However, scientific assessments and management recommendations based on comprehensive vital rates across the entire life cycle remain limited for such programs. The Crested ibis (Nipponia nippon), once nearly extinct, has been the focus of intensive conservation efforts, including reintroduction into its historical range. Here, we systematically evaluate the success of a Crested ibis reintroduction population by integrating long-term field monitoring with population dynamic modeling. C_LIO_LIWe conducted a systematic, multi-year (2023-2025) demographic study of a reintroduced Crested ibis population in Dongzhai National Nature Reserve, China. From 2013 to 2023, a total of 133 captive Crested ibises were released in seven batches within the nature reserve. By 2025, the wild population had exceeded 500. Based on the combination of field monitoring (n = 176 pairs) and GPS telemetry (n = 74 GPS tags, the longest monitoring time lasting 4 years), we quantified reproductive output and mortality rates across six life stages: nest building, incubation, offspring provisioning, fledgling (< 1 year), sub-adult (1-2 years), and adult (> 2 years). C_LIO_LIThe results show that the average clutch size of this population is 3.24 {+/-} 0.08, and each pair of parent birds can rear 1.31 {+/-} 0.12 nestlings to fledging each year. We found that high mortality during the incubation (32.5%, n = 425 eggs), offspring provisioning (34.1%, n = 287 chicks), and fledgling stages (38.6%, n = 63 birds). Key causes included unfertilized eggs, predation, starvation, and human disturbance. On the other hand, the mortalities in the nest building (9.1%, n = 176 pairs), sub-adult (9.8%, n = 28 birds) and adult stage (21.3%, n = 15 birds) were relatively low. Stage-structured matrix model shows a positive population growth of this reintroduced population (growth rate {lambda} = 1.053), with the population expected to increase 13-fold over 50 years (6,858 individuals). Sensitivity analysis indicates that adult survival had the strongest influence on population growth. Correspondingly, we predict that by 2075, its distribution range will expand 13-fold in tandem with population growth, exceeding 51,000 km{superscript 2} and covering several surrounding cities. C_LIO_LIOur comprehensive monitoring, evaluation, and simulation across the full life-cycle confirm that the reintroduced population is successfully established and self-sustaining. This finding addresses a critical research gap: the lack of a scientific and systematic framework for assessing population reintroductions in endangered species. Furthermore, we identified key conservation practices to enhance population growth and stability, including predator control, supplemental feeding, the rescue of weak chicks, and reducing human disturbance through public engagement. To ensure long-term viability, we also recommend implementing genetic management to avoid inbreeding. The resulting framework provides a practical model for improving reintroduction success and promoting species recovery in other endangered species. C_LI

ecology↗

bioRxiv2025

Variant-resolved prediction of context-specific isoform variation with a graph-based attention model

In eukaryotes, most genes produce multiple transcript isoforms that diversify the transcriptome and proteome, serving as a key mechanism of functional regulation. Genetic variation can disrupt the RNA processing signals that shape isoform structure and abundance, yet modeling these effects at full-length isoform resolution remains challenging due to the complexity of transcript regulation. Here, we introduce Otari, an attention-based graph neural network framework trained on the human genomic sequence and long-read transcriptomes across 30 tissue types and brain regions. Otari predicts tissue-specific differential isoform abundance by integrating sequence-derived epigenetic and post-transcriptional signals, enabling isoform-resolved variant effect interpretation. Applied to large-scale variant datasets including an autism cohort, Otari uncovers patterns of isoform dysregulation undetectable at the gene level, such as variant-driven perturbations in isoform abundance and microexon usage implicated in autism pathophysiology. We provide Otari as a resource for powering isoform-level analyses across tissues at scale.

bioinformatics↗

bioRxiv2025

An integrated multi-tissue atlas of epigenomic landscapes and regulatory elements in the bovine genome

Deciphering the regulatory syntax of the genome is essential to understand the genetic and molecular architecture of complex traits, as most trait-associated variants lie in non-coding regions. Yet, functional annotation of the bovine genome remains limited, hindering our ability to unravel the mechanisms underpinning complex traits of economic and ecological importance in cattle. Here, we present a comprehensive epigenetic atlas comprising 1,138 genome-wide epigenetic profiles, including chromatin accessibility, six histone modifications, CCCTC-binding factor (CTCF) transcription factor binding, DNA methylation, chromatin conformation, and transcriptomes across 53 adult tissues, five fetal tissues, and seven primary cell types. This atlas-level data enables us to annotate around 45% of the genome as putative regulatory elements exhibiting tissue- or cell-specific regulatory activity. Leveraging sequence-to-function deep learning models, we discovered 301 sequence motifs and predicted the functional impact of genetic variants through in silico mutagenesis, thereby facilitating the decoding of the regulatory syntax of the cattle genome and fine-mapping of GWAS loci for 22 complex traits. Cross-species analysis further revealed evolutionarily conserved features of regulatory architecture and provided evolutionary insights into complex traits and diseases in humans. Together, this atlas offers a foundational resource for advancing cattle functional genomics, sustainable breeding, and studies of regulatory evolution.

genomics↗

bioRxiv2025

Single-cell transcriptome sequencing for opening the blood-brain barrier through specific mode electroacupuncture stimulation

BackgroundThe blood-brain barrier (BBB) interferes with the treatment of central nervous system disorders owing to the complexity of its structure and restrictive function. Thus, it is challenging to develop central nervous system drug delivery strategies. Specific mode electroacupuncture (EA) stimulation can effectively open the BBB in rats. MethodsHere, we used single-cell RNA sequencing (scRNA-seq) to comprehensively map the cell population in the Sprague-Dawley rat cerebral cortex. ResultsWe identified 23 cell subsets and eight types of cells in the brain by cell annotation. scRNA-seq revealed transcriptional changes in the cerebral cortex under EA. ConclusionsOur findings offer valuable insights into the molecular and cellular modifications in the brain resulting from EA intervention and serve as a resource for drug delivery across healthy and diseased states. Innovative approaches to enhance BBB opening will lead to more effective therapeutic plans and enhanced drug delivery.

neuroscience↗

bioRxiv2025

NANOME: A Nextflow pipeline for haplotype-aware allele-specific consensus DNA methylation detection by nanopore long-read sequencing

Nanopore long-read sequencing has expanded the capacity of long-range, single-base, and single-molecule DNA-methylation (DNAme) detection and haplotype-aware allele-specific epigenetic phasing. Previously, we benchmarked and ranked the robustness of seven computational tools for DNAme detection using nanopore sequencing. The top performers were Megalodon, Nanopolish, DeepSignal and Guppy. However, these algorithms exhibit lower performance at regions with discordant non-singleton DNAme patterns compared to genome-wide regions. Furthermore, long-read sequencing analysis of mammalian genomes requires higher computational resources than next-generation sequencing. To address these issues, we developed a NANOpore Methylation (NANOME) a consensus DNAme predictive model using XGBoost, which integrates the output of Megalodon, Nanopolish, and Deepsignal for analyzing data obtained using Oxford Nanopore Technologies (ONT). NANOME enhanced DNAme detection precision (mean square error) at single-base resolution by 11% and improved accuracy (F1-score) at single-molecule resolution by 2.4% for human B-lymphocyte European cell lines (NA12878). The consensus model also detected [~]200,000 more CpGs than all three tools. Combing variant calling and long-read phasing, NANOME can detect haplotype-aware allele-specific DNAme in known imprinting controls in resolved and previously unresolved regions. We conducted haplotype-aware methylation detection on the T2T genome for dataset NA12878, revealing significant variations in differentially methylated region (DMR) density between gap and non-gap regions. Overall, NANOME represents a significant step forward in DNAme detection and long-range epigenetic phasing, offering a robust and accessible tool for researchers studying the epigenome.

bioinformatics↗

bioRxiv2025

DMBT1 promotes SARS-CoV-2 infection and its SRCR-derived peptide inhibits SARS-CoV-2 infection

DMBT1 is a large scavenger receptor cysteine rich (SRCR) B protein that has been reported as a tumor suppressor gene and a co-receptor for HIV-1 infection. Here we found DMBT1 is a major mucosal protein bound to SARS-CoV-2. Overexpression of DMBT1 in 293T cells enhanced infection by SARS-CoV-2 in ACE2 dependent manner. Blocking experiments using overlapping peptide library of SRCR domain of DMBT1 showed that CQGRVEVLYRGSWGTV peptide, which contains bacteria-binding VEVLXXXXW motif, could inhibit SARS-CoV-2 infection. High concentration of the peptide can significantly inhibit the replication of SARS-CoV-2 in hamsters. Single cell sequencing analysis showed the highest expression abundance of DMBT1 at airway submucosal glands. These results demonstrate that membrane DMBT1 can promote SARS-CoV-2 infection, and serve as a candidate target for antiviral development. Author SummaryWe discovered that a protein named as DMBT1, which is found in our mucous membranes, plays a significant role in SARS-CoV-2 infection. DMBT1 is a large glycoprotein that has previously reported linked to tumor suppression and HIV-1 infection. Our single-cell sequencing analysis revealed that DMBT1 is most abundant in the airway submucosal glands. In our study, we found that when DMBT1 is overexpressed in cells, it enhances SARS-CoV-2 infection. Using a peptide library from DMBT1, we pinpointed a specific peptide, CQGRVEVLYRGSWGTV, which can inhibit SARS-CoV-2 infection. At high concentrations, it significantly reduces the viruss ability to replicate in hamsters. These findings suggest that DMBT1 on the cell membrane can promote SARS-CoV-2 infection and highlight DMBT1 as a potential target for developing antiviral treatments.

microbiology↗