Biology subjects

Wang, G.

Publications and source records attributed to Wang, G..

At least 73 records · Page 4Linked to original sources

bioRxiv2026

Cost-effective hybrid long- and short-read sequencing enables accurate somatic structural variant detection

Somatic structural variant (SSV) calling typically requires matched normal data. Incorporating relatively inexpensive short-read sequencing not only provides essential germline information but can also replace a substantial portion of long-read sequencing, thereby enabling more cost-effective somatic SV detection. Here, we present SomaSV, a hybrid sequencing framework that integrates 30x tumor long-read data with matched normal data comprising 10x long-read and 30x short-read sequencing. This design achieves high-accuracy somatic SV detection while remaining cost-competitive. Comprehensive benchmarking demonstrates that SomaSV outperforms current state-of-the-art methods by more than 13% in F1 score while reducing sequencing costs by approximately 19%. Moreover, SomaSV identifies clinically relevant cancer-associated genes, including CLDN4 and ROBO2, highlighting its potential for discovering valuable biomarkers to support early cancer screening and diagnosis. The source code for SomaSV can be accessed at https://github.com/eioyuou/SomaSV.

bioinformatics↗

bioRxiv2026

A single-cell atlas and aging clock define biological age and risk-associated stem cell states in human hematopoiesis

Aging of hematopoietic stem and progenitor cells (HSPCs) impairs regenerative capacity and predisposes to hematological diseases. Here, we constructed a comprehensive single-cell transcriptomic atlas comprising 186,123 CD34+ HSPCs spanning early prenatal development (6 post-conception weeks) to late adulthood (74 years). We identified two conserved core molecular programs (MPs) of inflammaging and RNA splicing / protein homeostasis. Leveraging these programs, we developed a machine learning-based stem cell aging clock from 84 donors. Applying this clock to acute myeloid leukemia (AML), we define Transcriptional Age Deviation (TAD), a novel metric of biological age divergence. We found that a biologically "younger" state (low TAD), reflecting oncofetal reprogramming, is a powerful independent predictor of poor survival in two large AML cohorts. Low TAD was associated with high-risk genetics and therapy resistance, and critically, it re-stratified patient outcomes within each ELN 2022 risk category. Our work establishes a quantitative link between stem cell aging biology and AML prognosis, offering a robust tool to refine clinical risk assessment.

bioinformatics↗

bioRxiv2026

Differential higher-order superassembly of HECT-type UBE3 ligases controlled by calcium signals

The yeast E4 ligase Hul5 transiently associates with the proteasome during heat shock and proteotoxic stress to maintain cytosolic protein quality control. Two of its human orthologs, UBE3B and UBE3C, have specialized to safeguard mitochondria and inherit Hul5 function, respectively. We determined cryo-electron microscopy structures of full-length human UBE3B and UBE3C in complex with calmodulin at 2.9-3.5 [A] in calcium-free and calcium-saturated conditions. Calmodulin acts as an inter-protomer molecular glue clamping UBE3B into ring-shaped anti-parallel homodimers or asymmetric trimers, and remodels UBE3C into a conformation competent for proteasome association. Calcium binding to calmodulin promotes disassembly of these higher-order complexes, toggling UBE3B/C from E4 to E3 activity. These findings reveal how calmodulin regulates higher-order architectures of E3/E4 ligases to rewire the ubiquitin-proteasome system via calcium signaling.

biochemistry↗

bioRxiv2026

The aging mouse lipidome

Aging is associated with widespread metabolic changes that contribute to functional decline and disease. While prior studies have characterized age-associated changes in lipids, it still remains incompletely understood how the lipidome changes across tissues and between sexes during aging. Here, we performed targeted lipidomics across 10 organs collected from male and female mice at five ages spanning adolescence to old age. We analyzed 775 lipids across multiple lipid classes and found that aging affects the lipidome in an organ-specific manner. The thymus and quadriceps muscle had the most age-associated lipid changes, whereas lipid levels in organs such as the kidney and lung remained more stable. In quadriceps muscle, aging was associated with a decrease in specific phosphatidylcholine and phosphatidylethanolamine lipids, particularly those containing adrenic acid. We also identified sex-dependent differences in lipid composition, with the spleen showing differences throughout life. Spleens from female mice had lower levels of lysophosphatidylcholine and lysophosphatidylethanolamine compared to males. Together, these data provide a comprehensive atlas of age- and sex-associated lipid changes across mouse organs and complement existing metabolic and transcriptomic resources to support studies of mouse aging.

molecular biology↗

bioRxiv2026

SPCoral: diagonal integration of spatial multi-omics across diverse modalities and technologies

Spatial multi-omics is indispensable for decoding the comprehensive molecular landscape of biological systems. However, the integration of multi-omics remains largely unresolved due to inherent disparities in molecular features, spatial morphology, and resolution. Here we developed SPCoral for diagonal integration of spatial multiomics across adjacent slices. SPCoral extracts spatial covariation patterns via graph attention networks, followed by the use of optimal transport to identify high-confidence anchors in an unsupervised, feature-independent manner. SPCoral utilizes a crossmodality attention network to enable seamless cross-resolution feature integration alongside robust cross-omics prediction. Comprehensive benchmarking demonstrates SPCorals superior performance across different technologies, modalities and varied resolutions. The integrated multi-omics representation further improves spatial domain identification, effectively augments experimental data, enables cross-modal association analysis, and facilitates cell-cell communication. SPCoral exhibits good scalability with data size, reveals biological insights that are not attainable using a single modality. In summary, SPCoral offers a powerful framework for spatial multi-omics integration across various technologies and biological scenarios.

bioinformatics↗

bioRxiv2026

The novel variant IBDV induces inflammatory responses via VP3-mediated pyroptosis by directly targeting Caspase-9

The novel variant infectious bursal disease virus (nVarIBDV), which has been widely prevalent since 2017, threatens the poultry industry by inducing severe bursal atrophy and intense inflammatory responses. Understanding the inflammatory mechanism underlying nVarIBDV infection is critical for preventing virus-induced damage. Pyroptosis, an inflammatory type of programmed cell death, may contribute to the inflammation and bursa of Fabricius damage induced by nVarIBDV. Here, we found that the nVarIBDV infection induced severe inflammatory responses both in vivo and in vitro, which were associated with pyroptosis. Further research revealed that the viral protein VP3 drove pyroptosis induced by nVarIBDV infection by indirectly activating the Caspase-3-GSDME pathway, leading to GSDME cleavage. Mechanistically, VP3 directly interacted with and activated Caspase-9, thereby initiating Caspase-9-Caspase-3-GSDME pathway. More importantly, the Serine 33 of VP3 was identified as the key amino acid for interacting with and activating Caspase-9. Mutation of this residue significantly weakened the ability of VP3 to interact with Caspase-9 and activate the Caspase-9-mediated pyroptosis pathway, altered the binding mode of the Caspase-9-VP3 protein complex, and ultimately reduced the ability of VP3 to induce pyroptosis. In conclusion, our results elucidated a novel mechanism by which nVarIBDV infection induced inflammatory responses whereby viral protein VP3 triggered pyroptosis by targeting the Caspase-9-Caspase-3-GSDME pathway. IMPORTANCEnVarIBDV is globally prevalent and poses a significant threat to the poultry industry by causing inflammation and damage to the bursa of Fabricius. This study revealed that nVarIBDV infection induced inflammatory responses and bursa of Fabricius damage, primarily mediated by the viral protein VP3 targeting Caspase-9 to induce pyroptosis. To our knowledge, this is the first report on the interaction between inflammatory responses induced by IBDV infection and pyroptosis. In addition, serine 33 was identified as the critical amino acid required for maintaining the stability of the interaction between VP3 and Caspase-9 and activation of the Caspase-9-Caspase-3-GSDME cascade pyroptosis pathway and further inflammatory responses. Collectively, our findings not only elucidate a novel mechanism by which nVarIBDV induces pyroptosis and further leads to inflammation but also suggest that the identification of this serine site may represent a potential therapeutic target to block nVarIBDV-induced inflammation and bursa of Fabricius damage.

microbiology↗

bioRxiv2025

Aberrant medial entorhinal cortex dynamics link tau pathology to spatial memory impairment

Tau pathology in the entorhinal cortex (EC) is associated with spatial memory decline in aging and early-stage Alzheimers disease, but its impact on EC computations during learning is not well understood. We performed longitudinal two-photon calcium imaging of layer 2 excitatory neurons in the medial EC (MEC) of PS19 tauopathy mice over 10 days of an operant spatial learning task. Male PS19 mice showed marked learning impairments accompanied by dysregulated MEC activity and unstable spatial coding. Their activity also showed weakened representations in cue-poor relative to cue-rich regions, correlated with attenuated speed modulation. These changes suggest that impaired path integration destabilizes MEC spatial maps, leading to impaired spatial memory. In contrast, female PS19 mice exhibited only mild behavioral and neural deficits despite a comparable tau burden, suggesting sex-specific resilience. Among MEC cell types, pyramidal cells accumulated more phosphorylated tau than stellate cells and displayed the most severe functional disruption, linking cellular tau load to circuit dysfunction. Finally, general linear models of MEC activity reliably predicted learning performance, highlighted particularly strong contributions from non-grid and pyramidal cells, and accurately classified PS19 versus wild-type mice. These findings identify aberrant MEC dynamics as a key circuit mechanism underlying tau-related spatial memory deficits and point to early diagnostic and circuit-targeted therapeutic strategies.

neuroscience↗

bioRxiv2025

Characterization of Z-DNA dynamics across the tree of life

Z-DNA/Z-RNA is an alternative left-handed nucleic acid conformation with established and emerging roles in gene regulation, immunity, and genome instability. However, its occurrence dynamics and lineage specificity across the tree of life have not yet been fully characterized. Utilizing the recently developed and improved Z-DNA searching tool, ZSeeker, we analyzed 281,139 complete organismal genomes, including multiple Telomere-to-Telomere genome assemblies, and generated genome-wide Z-nucleic acid maps, examined their topography, and compared them to dinucleotide-preserving controls. Cellular genomes featured pervasive Z-DNA enrichment relative to expectation, with enrichments of [~]1.5 and [~]1.7-fold in Bacteria and Archaea and [~]3-fold in Eukaryota. In contrast, Viruses exhibited large differences between lineages, with modest enrichment in several DNA viral groups and pronounced depletion across RNA clades, most notably Influenza A/B strains. We built a LASSO regression model trained on non-Influenza viruses (cross-validated R{superscript 2} {approx} 0.73), which identified GC content, genome type, and host type as the leading predictors for Z-nucleic acid density, yet it significantly over-predicted Z-RNA density in Influenza A/B. More than 99% of assemblies exceeded the +2 SD threshold, and a "typical Influenza" genome was predicted at 2.76 bp/kb compared to [~]0.016 bp/kb observed (a [~]170-fold overestimation based on chance alone). Together, these results reveal domain- and lineage-specific regimes: cellular genomes are enriched for Z-DNA consistent with regulatory roles, whereas influenza viruses appear to have undergone strong, lineage-specific depletion of Z-RNA-forming sequences, likely reflecting evolutionary pressure tied to host sensing pathways.

evolutionary biology↗

bioRxiv2025

ZSeekerDB: A database of Z-nucleic acid sequences across organismal genomes

Alternative (non-B) nucleic acid structures such as Z-nucleic acids are emerging as key regulators of genome function. The density and distribution of Z-nucleic acid sequences across organismal and viral genomes can provide insights into their biological roles and evolutionary trajectory. Using our recently developed ZSeeker algorithm, we systematically analyzed over 280,000 organismal genome assemblies, identifying more than 850 million putative Z-forming loci. We also incorporated genomic coordinates, Z-score, and taxonomic metadata, enabling cross-species comparative and functional analyses. We introduce ZSeekerDB, the first large-scale, multi-taxon database cataloging Z-nucleic acid sequences, across organisms representing all major branches of life. ZSeekerDB enables interactive searches, visualizations, and downloads of Z-nucleic acid sequence data for independent analysis. ZSeekerDB is implemented as a web-portal for browsing, analyzing and downloading Z-forming loci, publicly available at https://zseeker-db.com/.

bioinformatics↗

bioRxiv2025

Polus: a Transformer-based Soft-decision Codec Enhancement Platform for DNA Storage

DNA storage offers exceptional information density and archival longevity, but is constrained by the complex, heterogeneous errors inherent to synthesis, storage, and sequencing. Conventional error-correction schemes often rely on excessive logical redundancy to mitigate these biochemical imperfections, thereby compromising storage efficiency. Here, we introduce Polus, a deep-learning-enabled platform that bridges the gap between biochemical constraints and digital reliability through soft-decision decoding. At its core is SeqFormer, a Transformer-based channel model that synergizes sequence context with quality signals to characterize platform-specific error profiles, generating calibrated per-base confidence scores. This mechanism transforms uncertain biochemical noise into informative "soft" erasures. In in silico benchmarks, Polus significantly enhances mainstream codecs: it reduces the sequencing coverage required for DNA Fountain by 38.9% --increasing effective physical density by [~]80%--and eliminates persistent indel-induced errors in the Yin-Yang codec. Furthermore, it enables a targeted resequencing strategy that achieves full recovery with 99.9% less overhead than brute-force deepening. To formalize these gains and address the lack of systematic benchmarking in the field, Polus establishes a standardized nine-metric evaluation framework that rigorously quantifies the trade-offs between reliability, density, and cost. This work provides a reproducible, quantitative foundation for next-generation, context-aware DNA storage systems.

bioinformatics↗

bioRxiv2025

A Combinatorial mRNA Therapy for Treating Rheumatoid Arthritis and Osteoarthritis by Inhibiting Inflammation and Promoting Cartilage Regeneration

Rheumatoid arthritis (RA) and osteoarthritis (OA) are debilitating joint disorders with distinct etiologies but share common pathological features of chronic inflammation and progressive cartilage damage. Current therapeutic options are largely palliative and yet fail to achieve desired effect. In this study, we developed a combinatorial mRNA therapy using a rationally engineered lipid nanoparticle (LNP) containing a novel ionizable lipid for enhanced cartilage penetration upon intra-articular administration. This LNP co-delivers mRNAs encoding two complementary therapeutic proteins: interleukin-1 receptor antagonist (IL-1Ra) for inflammation attenuation and a C-terminal truncated derivative of angiopoietin-like 3 (ANL3) for cartilage regeneration. In pre-clinical models including collagen-induced arthritis mice, TNF-transgenic mice with spontaneous RA and destabilization of medial meniscus (DMM) surgical OA model, this "one-shot" combinatorial therapy significantly ameliorated disease severity, reduced synovitis and bone erosion, and potently promoted the regeneration of hyaline-like cartilage. Mechanistically, transcriptomic profiling of joint specimens revealed that the combinatorial therapy concurrently suppressed inflammatory pathways and extracellular matrix degradation pathways, meanwhile upregulating anabolic genes facilitating chondrogenesis. Collectively, our study establishes a versatile and synergistic mRNA therapy that offers a potent disease-modifying immunomodulatory strategy capable of addressing the long-standing unmet needs in arthritis treatment.

immunology↗

bioRxiv2025

HINS: HLA-mediated immunogenic neoantigen score for robust prediction of immune checkpoint blockade response

Immune checkpoint blockade (ICB) therapy response prediction provides remarkable genomic and transcriptomic gains and has successfully understood the molecular mechanisms underlying various cancers. However, only a subset of patients with advanced tumors currently benefits from ICB therapies, which at times incur considerable side effects and costs. Developing predictive tools for ICB response has remained a serious challenge because of the complexity of the immune response and the shortage of large cohorts of ICB-treated patients that include both omics and response data. Here, based on a pooled genomic dataset of 919 patients across multiple solid tumors treated with anti-PD-(L)1 or anti-CTLA-4, we constructed human leukocyte antigen class I (HLA-I)-mediated immunogenic neoantigen score (HINS). HINS is a predictor of ICB response using neoantigen quality and HLA evolutionary divergence to integrate the factors associated with immune activation and evasion. It yielded an overall accuracy of AUC=0.853, outperforming existing predictors and capturing more than 75% true responders. Experimental analysis indicated that patients with higher HINS were more likely to undergo survival benefits following ICB therapy. Our results highlighted the transcriptional and genomic correlations between HINS-identified molecule features and ICB response, such as immunoresponsive gene enrich pathways, favorable and unfavorable genomic subgroups, and hotspot somatic events in driver genes. This study presented an interpretable, accurate deep-learning method using meaningful predictive features to predict the response to ICB, providing biological insights into the complexity of the determinants underlying immunotherapy.

bioinformatics↗

bioRxiv2025

Normative modeling of brain morphology reveals neuroanatomical heterogeneity and biological subtypes in major depressive disorder

BACKGROUNDMajor Depressive Disorder (MDD) is characterized by high neurobiological heterogeneity, which hinders precise diagnosis and treatment. Traditional group-level neuroimaging analyses fail to capture individual differences, while normative modeling offer a promising approach to quantify individual deviations from healthy brain structure patterns, facilitating the identification of biological subtypes and offering a data-driven framework to dissect this heterogeneity. METHODSUsing 1,190 healthy controls, we constructed normative developmental trajectories of gray matter volume (GMV) across 246 Brainnetome-defined regions using Bayesian linear regression. Deviation maps were derived for 398 MDD patients. k-means clustering was employed to identify GMV-based biotypes. Then, the clinical characteristics and anatomical differences among these subtypes were explored, along with the post-treatment clinical features and treatment responses of participants who completed the 8-week antidepressant treatment within each subtype. RESULTSPatients with MDD exhibited widespread yet individually variable GMV deviations. Clustering analysis revealed two subtypes: Subtype 1 displayed predominantly negative deviations in sensorimotor and occipital cortices, whereas Subtype 2 showed widespread positive deviations in temporal and posterior cingulate regions. Subtype 1 had higher extraversion and symptom-linked deviation patterns; in Subtype 2, deviation burden correlated with generalized anxiety. Longitudinally, Subtype 1s GMV deviation changes predicted symptom improvement, while Subtype 2s deviations correlated with baseline severity. CONCLUSIONSNormative modeling of GMV reveals marked neuroanatomical heterogeneity in MDD and identifies subtypes with distinct clinical and treatment-related characteristics, laying a foundation for precision psychiatry and individualized interventions.

neuroscience↗

bioRxiv2025

Image-guided alignment of consecutive multi-modal tissue slides

Multi-modal spatial data analysis often requires precise physical alignment of consecutive tissue sections, a process that can be challenging and typically relies on shared molecular markers or image recognition techniques. Here, we introduce COAST (Consecutive multi-Omics Alignment of Spatial Tissues), a method to reliably physically align consecutive tissue sections to produce a unified multi-modal molecular dataset suitable for downstream applications. COAST relies exclusively on the images associated with spatial data, eliminating the need for common molecular features or prior annotations. We demonstrate the effectiveness of COAST using spatial transcriptomics slides, where it achieves performance comparable to established uni-modal alignment tools. Applying COAST to spatial transcriptomics and metabolomics/lipidomics tissue sections from a mouse model of ischemia reperfusion injury allowed the investigation of lipid/metabolite features of transcriptionally-defined cell types. Overall, COAST offers a streamlined and integrative solution for multi-modal spatial data alignment.

bioinformatics↗

bioRxiv2025

Genome-wide CRISPR screen reveals PEX11B as a host restriction factor against ORFV through membrane fluidity regulation

Host-pathogen interactions are shaped by cellular restriction factors that direct antiviral defenses. We built the first ovine genome-wide CRISPR knockout library in sheep testis (OA3.Ts) cells, targeting all protein-coding genes. Using this platform, we identified PEX11B, a peroxisomal membrane regulatory protein, as a strong restriction factor against orf virus (ORFV) infection. Removing PEX11B increased viral susceptibility and triggered severe cytopathic effects with membrane fusion and syncytia formation. Mechanistic studies showed that PEX11B knockout harmed peroxisomal integrity and disrupted lipid metabolism. This led to greater plasma membrane fluidity, creating a proviral environment that allowed more viral entry and replication. These results reveal a new antiviral function for PEX11B in blocking viral infection and underscore the importance of peroxisomal regulation in host-virus interactions.

microbiology↗

bioRxiv2025

mfSuSiE enables multi-cell-type fine-mapping and multi-omic integration of chromatin accessibility QTLs in aging brain.

Molecular quantitative trait locus (QTL) studies increasingly profile chromatin accessibility, histone modifications, DNA methylation, RNA modifications such as N6-methyladenosine (m6A), and transcription across multiple cell types using high-throughput sequencing, generating dense base-pair-resolved measurements. The conventional approach of testing each variant against each molecular feature independently suffers from severe multiple testing burden and ignores linkage disequilibrium and spatial correlation. Existing fine-mapping methods only partially address these challenges and are sub-optimal for analyzing such datasets: multivariate approaches such as mvSuSiE jointly analyze multiple molecular contexts but are designed for a single trait value per context and cannot accommodate thousands of base-resolution measurements per context, while functional approaches such as fSuSiE model spatial structure across thousands of measurements but analyze each context separately. Here, we introduce mfSuSiE, which integrates multivariate analysis with wavelet-based functional regression to jointly fine-map thousands of base-resolution traits across multiple cell types. In simulations, mfSuSiE identified causal variants and affected molecular features more accurately than fSuSiE, while mvSuSiE cannot be applied to this type of data. Applied to single-nucleus chromatin accessibility data from six brain cell types from postmortem aging human brains, mfSuSiE substantially increased discovery and resolution, with substantial power gains for cell types with limited samples. Multi-cell-type analysis revealed extensive sharing of regulatory effects on chromatin accessibility (caQTL). Importantly, mfSuSiE produces Bayesian inference compatible with the SuSiE framework, enabling systematic multi-omic integration. Applied to Alzheimers disease loci, we integrated caQTL with expression QTLs, epigenomic QTLs, and GWAS, observing regulatory patterns suggesting complex mechanisms at loci including EARS2, CHRNE, SCIMP, and RABEP1.

genetics↗

bioRxiv2025

SuSiE 2.0: improved methods and implementations for genetic fine-mapping and phenotype prediction

Sum of Single Effects regression (SuSiE) has become widely adopted for genetic fine-mapping, yet its original implementation faces architectural limitations that hinder extensibility and performance. We present SuSiE 2.0, featuring a modular redesign for extensibility, up to 5x speed improvements for summary statistics applications, and several useful extensions including SuSiE-ash, a new method that improves calibration when strong signals coexist with moderate effects. Simulations and real data benchmarks demonstrate performance across diverse genetic architectures, highlighting improved calibration of SuSiE-ash for fine-mapping under complex polygenic backgrounds with 1.5-3x FDR reduction while maintaining power, and revealing SuSiE-based methods as effective yet underappreciated tools for TWAS prediction.

bioinformatics↗

bioRxiv2025

LARIS enables accurate and efficient ligand and receptor interaction analysis in spatial transcriptomics

Advances in spatially resolved transcriptomics provide unprecedented opportunities to characterise intercellular communication pathways. However, robust and computationally efficient incorporation of spatial information into intercellular communication inference remains challenging. Here, we present LARIS (Ligand And Receptor Interaction analysis in Spatial transcriptomics), an accurate and scalable method that identifies cell type-specific and spatially restricted ligand-receptor (LR) interactions at single-cell or bead resolution. LARIS is compatible with all spatial transcriptomic technologies and quantifies specificity, infers sender-receiver directionality, and detects how differential interactions vary across time and space. To compare LARIS with existing methods, we established a simulation framework to generate ground truth of LR interactions with defined tissue architecture and gene expression patterns. LARIS demonstrates superior performances over other methods in accuracy and scalability. We further applied LARIS to human tonsil and developing mouse cortex spatial transcriptomics datasets collected from various spatial techniques. This uncovered the signalling mechanisms shaping tissue organisation and their changes over time. LARIS reveals cell type-, niche-, and condition-specific signalling and scales to hundreds of thousands of cells in minutes. This provides an efficient and direct method for discovering the molecular interplay between apposed cells across development.

bioinformatics↗