Biology subjects

Wang, Z.

Publications and source records attributed to Wang, Z..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Deciphering the gene-centric spatial dynamics across tissue and time with GeneSPOT

Spatial omics technologies provide unprecedented opportunities to investigate how molecular programs are organized within tissues. However, existing analytical frameworks primarily focus on identifying cellular states and characterizing differentially expressed molecular features across these states. The relational spatial organization of molecular features and its dynamics across biological contexts therefore remain largely unexplored. Here, we present GeneSPOT, a gene-centric computational framework for characterizing the spatial relationships among genes and quantifying their reorganization across biological conditions. Although gene-centric in design, GeneSPOT generalizes to other molecular features. Across diverse applications, GeneSPOT resolved tissue structures, integrated paired and unpaired spatial multi-omics data, unveiled the spatial convergence of gene programs during embryonic development, and identified aging-associated spatial reorganization among genes whose expression did not change significantly with age. GeneSPOT thus complements conventional cell-centric analysis, providing a framework for deciphering how spatial relationships among molecular features are reorganized across biological conditions and over time.

bioinformatics↗

bioRxiv2026

Platform-specific count-matrix preprocessing workflows for spatial transcriptomics data analysis

Preprocessing of spatial transcriptomics (ST) count matrices is critical for removing technical variation while preserving biological signals, but optimal strategies remain unclear across diverse platforms. We systematically benchmarked 371 preprocessing workflows across 45 data spanning 11 mainstream ST platforms, evaluating their performance using specifically designed complementary metrics. To further assess preprocessing factors upstream of count-matrix generation, we additionally evaluated image preprocessing, including cell segmentation and post-segmentation transcript processing, across representative imaging-based ST platforms. We found that no universal count-matrix preprocessing workflow performed optimally across all platforms, while data from the same platforms exhibited striking consistency in optimal count-matrix preprocessing workflows. We identified the key data structure characteristics and count-matrix preprocessing steps that critically influence preprocessing performance. We developed platform-specific and data structure-guided recommendations to assist users in selecting the optimal count-matrix preprocessing workflow, validated across a broader range of downstream tasks. We demonstrated that our data-driven recommendations uncover subtle biologically meaningful spatial patterns in new data that were obscured by suboptimal count-matrix preprocessing. Our findings suggest best practices for ST data count-matrix preprocessing.

bioinformatics↗

bioRxiv2026

EpiZoo: a DNA sequence-aware foundation model for cross-species single-cell epigenomics

Large-scale single-cell epigenomic atlases characterize chromatin regulatory landscapes across diverse biological contexts, spanning cell types, tissues, individuals and species. Foundation models provide an opportunity to capture the full spectrum of cellular diversity in these atlases, yet current models remain largely confined to individual species by genomic coordinate dependence and overlook regulatory information encoded in DNA sequences. Here we introduce EpiZoo, a DNA sequence-aware foundation model for cross-species single-cell epigenomics. EpiZoo converts million-dimensional single-cell epigenomic profiles from diverse species into compact cell sentences that integrate DNA-encoded regulatory information, sequence-independent epigenomic context and accessibility-based importance. Built around a mixture-of-experts transformer and containing 2.6 billion parameters in total, EpiZoo is pretrained on our manually curated multi-species Omni-scATAC corpus of approximately 20.9 million cells to learn regulatory programs across species. On external datasets excluded from pretraining, EpiZoo achieves state-of-the-art performance in fundamental single-cell analysis tasks, including feature extraction, cell type annotation and data imputation. Its sequence-aware architecture enables extension to evolutionarily diverse species, and supports comparative analysis of regulatory conservation and divergence during primate evolution. Benefiting from this modeling design, EpiZoo enables context-aware prioritization of somatic mutations in cancer and prediction of cell-type-specific chromatin accessibility from DNA sequences across genomic regions and species.

bioinformatics↗

bioRxiv2026

Mutation Order and Selection Shape Intratumor Heterogeneity in Tumor Evolution

Cancer progression often requires the accumulation of multiple driver mutations, yet the same set of drivers may be acquired in different orders. How these alternative mutation-order pathways jointly shape tumor clonal structure remains unclear. We develop a multitype branching-process model in which malignant transformation requires two driver mutations and distinguish malignant cells both by their mutation order and by the independent transformation event that founded their clone. Under a successive exponential approximation, we establish point-process limits for the pathway-specific clone-size processes and derive a closed-form expression for the limiting expected Simpson's index of the combined malignant population. When the two mutation orders produce malignant cells with the same net growth rate, the limiting index separates into effective pathway weights, determined by mutation rates and birth-death dynamics at preceding stages, and within-pathway concentration terms, determined by intermediate-to-malignant growth-rate ratios. This decomposition shows that a driver's effect on heterogeneity depends critically on when it is acquired. A strong driver acquired early expands the intermediate lineage and increases the supply of independent malignant founders, whereas the same driver acquired at the final transition strengthens the growth and age advantage of early-founded malignant clones. Under additive fitness effects, these counteracting mechanisms can produce a non-monotone relationship between selective advantage and clonal concentration. We further show that threshold-like non-additive fitness effects can generate highly concentrated malignant populations, while order-dependent terminal fitness leads the faster-growing pathway to dominate asymptotically. Together, these results reveal how mutation order, mutational accessibility, selection, and epistasis jointly determine lineage-level intratumor heterogeneity.

cancer biology↗

bioRxiv2026

Accessing Enzyme Kinetic Data and Prediction Methods at Scale

Enzyme kinetic parameters inform metabolic models, yet experimental measurements are sparse. A growing body of work predicts them from protein and substrate features, but software fragmentation hinders adoption, so downstream tools lock into the most accessible method. We present OpenKinetics Predictor (at predictor.openkinetics.org), an open-source platform integrating thirteen methods in isolated environments behind one interface. The platform optionally reports similarity between query proteins and each method's training data to contextualise reliability. A common featurisation-prediction abstraction keeps it extensible, and independent parties, including original authors, contributed many methods. We pair it with a data portal (at data.openkinetics.org) that exposes CatLog, a curated kinetic dataset, with precomputed embeddings, predicted binding sites, and standardised splits. Both offer a web interface and an API, and the GECKO modelling toolbox already calls the predictor API. As a case study, we predict across an E. coli model and find inter-predictor agreement varies with metabolic context and data availability.

systems biology↗

bioRxiv2026

Nanoparticle Albumin-Bound Paclitaxel Targets Pulmonary Neutrophils

Nanoparticle albumin-bound paclitaxel (Abraxane) was among the first clinically approved nanomedicines and is a first-line chemotherapeutic. It enhances therapeutic efficacy and reduces side effects of paclitaxel. Despite its extensive clinical use, mechanisms of Abraxane's therapeutic effects are the subject of ongoing study. This work posits a new mechanism for Abraxane's effects in lung cancers and metastasis. Tracing the albumin component of Abraxane with radioisotopes or fluorophores, we find that Abraxane homes to the lungs. Abraxane's lung tropism is elevated by inflammation and is eliminated in complement protein C3 knockout mice, showing that complement pathway drives the uptake. Flow cytometry and histology show that neutrophils are the dominant cell type taking up Abraxane in the lungs. We demonstrate Abraxane's tropism to pulmonary neutrophils in mice and in human donor lungs. We note previously undocumented side effects associated with Abraxane's lung tropism, including pro-thrombotic and pro-inflammatory responses in acute lung inflammation. We show that Abraxane's efficacy against lung metastases is affected when lung uptake is eliminated by C3 knockout. Our results provide insight into why Abraxane is effective in lung cancer and metastases. These findings may be used to improve outcomes in patients receiving Abraxane and help design next-generation cancer nanomedicines.

bioengineering↗

bioRxiv2026

A Human Genome-wide CRISPR-Cas9 Screen Reveals Host Factors That Support Epstein-Barr Virus B cell infection

Epstein-Barr virus (EBV) infects 90% of the population worldwide and is associated with multiple malignancies, including Burkitt, Hodgkin and post-transplant lymphomas, nasopharyngeal and gastric carcinoma. EBV also triggers autoimmune diseases, including multiple sclerosis and systemic lupus erythematosus. While host factors important for EBV B-cell entry have been defined, including CD21/CR2, MHC class II and R9AP, much remains to be learned about host factors that support post-entry steps of EBV infection. To gain insight, we conducted a human genome-wide CRISPR-Cas9 screen. In addition to known EBV B cell receptors/co-receptors, we identified multiple new factors and pathways essential for EBV B cell infection. These included multiple B cell signaling pathways, actin cytoskeleton and nuclear import factors. Knockout of CD19, its tetraspanin chaperone CD81 or karyopherin subunit alpha 1 (KPNA1, also called importin-5) significantly impaired establishment of EBV infection. EBV and CD19 co-localized at early stages of EBV infection, and the CD19 cytoplasmic tail was important for EBV infection, suggesting CD19 signaling supports EBV uptake. Depletion of KPNA1 significantly impaired establishment of EBV infection, but did not impair production of infectious EBV upon reactivation of latently infected B cells. Together, these results provide insights into pathways that support EBV B cell infection and identify potential therapeutic targets.

microbiology↗

bioRxiv2026

TFAM Dependent Mitochondrial Fitness Limits CD8⁺ T Cell Immunopathology and Sustains Protective Immunity during Viral Pneumonia

During respiratory virus infection, CD8 T cells kill infected cells and establish antigen-specific memory, but mechanisms regulating these functions remain incompletely understood. Here, we identify mitochondrial transcription factor A (TFAM)-dependent mitochondrial fitness as a regulator of CD8 T cell function during influenza infection. Human CD8 T cells exhibited an age-associated decline in TFAM expression and mitochondrial function. To model this physiologically relevant decline and determine its consequences for antiviral immunity, we generated CD8 T cell-specific TFAM-haploinsufficient mice. TFAM insufficiency disrupted mitochondrial integrity and bioenergetics and increased mitochondrial DNA and oxidative stress. During influenza infection, TFAM-insufficient CD8 T cells exhibited increased cytotoxic and inflammatory activity associated with lung immunopathology without improved viral control. This early phenotype was followed by loss of effector function, diminished antigen-specific responses, reduced protection following adoptive transfer, and impaired heterosubtypic recall immunity. Thus, TFAM-dependent mitochondrial fitness is a cell-intrinsic regulator that limits immunopathology while sustaining recall immunity.

immunology↗

bioRxiv2026

Drought-Spec-Net: Early Tomato Drought Detection and Potential Yield-Impact Assessment Using Vis NIR Data

Drought stress significantly reduces tomato (Solanum lycopersicum L.) productivity, and early detection is critical to minimize yield losses through timely interventions. In this study, we developed Drought-Spec-Net, a hybrid 1D convolutional neural network that integrates local and global spectral feature extraction to detect early drought stress from visible and near infrared (Vis NIR) spectra data of tomato seedlings. The model was trained on 378 samples using an 80:20 train test split, with 20% of the training set reserved for validation. DroughtSpecNet outperformed the evaluated baseline and state of the art models, achieving 97% accuracy, 95% precision, 98% recall, and an F1 score of 97%. To improve the agronomic interpretation of the model outputs, predicted drought probabilities were converted into a literature-informed potential yield impact indicator using a maximum impact level of 60%. On the test set (76 samples), mapped potential yield-impact values ranged from 0% to 60%, with an average reduction of 12.97%. We also conducted an initial experiment using our greenhouse RGB dataset, collected daily from drought treated and well-watered tomato plants at West Virginia State University (WVSU). From this dataset, 44 images were selected for ilastik-based canopy segmentation, producing plant-level drought severity indices (DSI) with a mean of 0.28, median of 0.14, and range of 0.01 to 0.91. Additionally, we trained and fine-tuned a large language model (LLM) based on PLLaMA7BInstruct, called AgriLLaMA, for automated agronomic report generation from Drought-Spec-Net outputs. The generated reports summarize predicted stress levels, mapped potential yield impacts, and preliminary management considerations. This integrated approach not only improves early drought stress detection but also delivers quantitative and interpretable estimates of potential productivity losses, providing a complete framework connecting physiological stress detection to actionable agricultural outcomes.

plant biology↗

bioRxiv2026

A Context-Aware 14-3-3 Binding Predictor Enabled by Data Augmentation, Multi-Scale Biological Features, and Protein Language Model Embeddings

Identifying phosphorylated serine/threonine residues that mediate 14-3-3 interactions remain a major challenge in understanding phospho-dependent 14-3-3 regulation. Existing predictors largely rely on local sequence features surrounding candidate phosphosites, owing in part to the limited number of experimentally validated 14-3-3 binding sites, while broader contextual determinants of binding remain insufficiently explored. Here, we show that 14-3-3 binding sites are defined not only by local motif patterns but also by multi-scale biological context. We found that 14-3-3-binding proteins are enriched for condensation-related properties, that 14-3-3 docking sites preferentially localize to compact intrinsically disordered regions, and that protein language model embeddings provide informative representations for this task. To address both limited data availability and incomplete site representation, we developed CAMP-14-3-3 (Context-Aware Multi-scale Predictor for 14-3-3 binding), a framework that integrates multi- scale biological features with protein language model embeddings, together with a phylogeny-based data augmentation strategy and distribution-matched negative sampling. This integrated framework outperformed motif-based approaches and existing predictors on independent data. Our results support a context-aware view of 14-3-3 recognition and provide a general strategy for modeling phospho-dependent protein interactions under limited-data conditions.

bioinformatics↗

bioRxiv2026

Structural basis for differential inhibition of THIK1 by chemically related inhibitors

TWIK-related halothane-inhibited potassium channel 1 (THIK1) is a two-pore domain potassium (K2P) channel highly expressed in microglia that contributes to microglial homeostasis and neuroinflammatory responses. Pharmacological inhibition of THIK1 suppresses tonic K+ currents and attenuates NLRP3-dependent IL-1{beta} release, highlighting THIK1 as a potential therapeutic target for neuroinflammatory disorders. However, the structural basis of THIK1 inhibition remains unclear. Here we report cryo-electron microscopy (cryo-EM) structures of full-length human THIK1 in complex with C101248, a commercially available tool compound, and CVN293, a clinical-stage inhibitor that has completed Phase I evaluation. The two inhibitors occupy a similar site within the inner vestibule formed by TM2 and TM4 beneath the selectivity filter. Consistent with their closely related chemical scaffolds and shared binding site, both inhibitors remodel the Y273 inner gate but differ in their effects on the C-terminal gate. Electrophysiological analyses further demonstrate that the C-terminal gate contributes differently to inhibition by the two compounds. Together, our findings show how chemically related inhibitors exert different effects on THIK1 despite engaging a common binding site.

biophysics↗

bioRxiv2026

aaRSID, an engineered pyrrolysyl-tRNA synthetase platform for multi-probe proximity proteomics

Proximity labeling (PL) methods utilize spatially targeted chemical or enzymatic generation of a diffusible, reactive intermediate to covalently tag neighboring proteins in living systems. Unlike other tools for studying molecular interactions, PL can detect transient protein relationships with high spatial and temporal sensitivity, allowing for insight into their roles in biological processes. However, current enzymatic PL tools, such as TurboID and APEX2, are limited by their substrate structure and chemistry, which can generate significant background and/or perturb cellular physiology. To address these limitations, we have developed aminoacyl-tRNA synthetase ID (aaRSID), a PL tool that leverages an engineered pyrrolysyl tRNA synthetase (PylRS) for proximity labeling of proteins. We chose PylRS because it can catalyze promiscuous lysine labeling in the absence of its cognate tRNA and utilize a variety of non-canonical amino acids (ncAAs) as substrates. Here, we demonstrate aaRSID's intrinsic proximity labeling activity, use directed evolution to improve this activity, and apply the improved mutant (aaRSID-Ma1.3) for subcellular proteomics and multiplexed imaging. Our work establishes aminoacyl-tRNA synthetases as a new PL enzyme class and introduces a versatile chemical platform for developing ncAA-derived probes to map cellular microenvironments, greatly expanding the applications possible of PL technology.

biochemistry↗

bioRxiv2026

Expandable DNA-origami rings as a nanomechanical platform for studying disordered nucleoporins

Phe-Gly-rich nucleoporins (FG-Nups) within the nuclear pore complex (NPC) form a diffusion barrier that controls the exchange of macromolecules between the nucleus and cytoplasm. The behaviors of these intrinsically disordered FG-Nups are fundamental to the NPC function but remain difficult to probe within the dynamic, native nuclear pore. To bridge this knowledge gap, here we build expandable DNA-origami rings with tunable elasticity and study FG-Nups site-specifically tethered in such mechanically responsive NPC mimics. The elastic deformation of DNA rings, measured by single-particle TEM, reveals the type (attraction or repulsion) and strength of FG-Nup interactions that shape the collective morphology of these unstructured proteins. Analyzing homotypic interactions of five FG-Nup species finds diverse FG-Nup behaviors that can be classified as repulsive/extended (Nsp1), moderately cohesive/extended (Nup62, Nup153, and Nup214), and strongly cohesive/compact (Nup98). Furthermore, we examine how FG-Nups' primary sequence, O-glycosylation, grafting density, and binding to nuclear transport receptors (NTRs) impact their biophysical state. For example, O-GlcNAcylation reduced FG-Nup cohesiveness, switching Nup98 from a collapsed state to more extended conformations. Our work thus elucidates spatially confined Nup-Nup and Nup-NTR interactions within an NPC-like nanopore, suggests a previously underappreciated role of FG-Nups in modulating NPC dilation, and establishes a generalizable method for studying multivalent interactions among disordered proteins.

biophysics↗

bioRxiv2026

Metabolic engineering of Salmonella enterica for coupled kynurenine sensing and depletion enhances antitumor efficacy

Immunosuppressive tumor metabolic microenvironment remains a major barrier to effective cancer therapy. Tumor-targeting Salmonella enterica strains were engineered to deplete the intratumoral immunosuppressive kynurenine, triggering metabolic rewiring and fostering an active antitumor immune microenvironment. Equipping S. enterica VNP20009 with bacterial kynureninase enzyme (KynU) and a kynurenine transporter enabled efficient intratumoral kynurenine degradation. We then developed a next-generation strain, AD51, combining quorum-sensing controlled KynU expression with kynurenine-dependent growth and tumor targeting. AD51 induced robust immune activation and exhibited better anti-tumor efficacy. The addition of intratumoral exogenous IFN{gamma} to AD51 synergistically enhanced immune activation and antitumor efficacy in murine ovarian cancer and melanoma models, reducing tumor burden by 75% and 84%, respectively, compared with untreated controls (p<0.0001 for both). Our data establishes a framework for engineering bacteria to target and degrade tumor immunosuppressive metabolites while coupling bacterial growth to metabolite availability, paving the way for more precise and programmable microbial therapies.

cancer biology↗

bioRxiv2026

NSD2 deficiency disrupts the epigenetic landscape of cochlear hair cells to enhance ECM expression and to perturb cochlear hair cell function

Mammalian cochlear hair cells (HC) are crucial for hearing, and histone modifications may play an important role in HC maturation and functions. However, whether and how NSD2, a histone H3 lysine 36 (H3K36) dimethyltransferase, affects cochlear HC maturation remains unclear. Here, we established an HC-specific Nsd2 knockout mouse model and discovered that the loss of NSD2 results in severe structural defects in stereociliary bundles and profound hearing impairment. Integrated multi-omics profiling revealed that NSD2 deficiency leads to a decreased expression of H3K36me2, which in turn alters genome-wide chromatin accessibility and gene expression. Mechanistically, NSD2 deficiency triggers the aberrant upregulation of extracellular matrix (ECM) genes at the transcriptional level, causing excessive collagen accumulation and compromising tissue structure. Crucially, ECM intervention can mitigate NSD2-deficiency mediated hearing damage. Our study reveals an epigenetic mechanism by which NSD2 regulates cochlear HC maturation through inhibition of chromatin remodeling-induced ECM accumulation, and provides a potential new therapeutic direction for hearing impairment.

cell biology↗

bioRxiv2026

Spatiotemporal atlas resolves the mesenchymal-epithelial stem cell axis governing human hair follicle aging and pathology

How adult stem cell populations coordinate tissue regeneration across physiological and pathological states remains unclear. Hair follicles contain epithelial and mesenchymal stem cells that codetermine hair regeneration. Here, we integrate a multimodal spatiotemporal atlas with functional validation to define the mesenchymal-epithelial stem cell dynamics governing human hair follicle homeostasis, aging, and androgenetic alopecia. Aging is characterized by passive depletion of hDSC-derived regenerative cues, whereas AGA involves a shift of hDSC toward an inhibitory signaling hub that promotes HFSC dysfunction. We further pinpoint hDSC-secreted PTN as a key regenerative factor that promotes hair growth and counteracts androgen-induced growth suppression. This study provides a holistic framework of adult stem cell crosstalk across physiological and pathological states, offering a mechanistic roadmap to reinstate tissue regeneration.

cell biology↗

bioRxiv2026

Benchmarking Uncertainty and Improving Risk Ranking in Multi-task Bioactivity Prediction

Multi-task bioactivity prediction transfers information across assays, but experimental prioritization also requires uncertainty estimates that identify unreliable predictions. We benchmarked prediction accuracy and uncertainty calibration for five multi-task predictors on 100 ChEMBL27 test assays and 100 CHEMBL37 assays under the random split and the clustering split. Calibration used native, Deep Ensemble and MC Dropout uncertainty; risk ranking was compared for two Gaussian process (GP) backbones. Adaptive deep kernel fitting (ADKF) achieved the best mean calibration results, while deep kernel transfer (DKT) ranked errors more effectively than ADKF with native uncertainty. We also introduce Influence Calibrated Support Reconstruction (ICSR), a risk score for kernel-based predictors. ICSR combines measured support reconstruction errors with query-specific influence and stabilizes their weighted average toward the full-support mean to adjust Gaussian process uncertainty while preserving predicted activities. With DKT and ADKF, ICSR improved all three mean risk-ranking metrics over native uncertainty and an adapted neighborhood comparator in every panel and split. ICSR achieved a mean half-query MAE reduction (R50) of 14.2-19.6%, where R50 measures the percentage decrease in mean absolute error (MAE) after retaining the lowest-risk half of the queries. A retrospectively selected SARS-CoV-2 main protease case further illustrated its use for selecting more reliable predictions. The benchmark supports joint assessment of calibration and error ranking, while ICSR improves selective use of kernel-based bioactivity predictions.

bioinformatics↗

bioRxiv2026

SMORE: joint dimension reduction and cell population discovery on single-cell methylome data

Single-cell DNA methylation profiling technology captures novel epigenetic data modality but are challenging to analyze because of their heterogeneity, high dimensionality, and ultra-sparsity. Here we present SMORE (Single-cell MethylOme Reduction and Embedding), a computational method for joint dimensionality reduction and cell population discovery dedicated to single-cell DNA methylation data. SMORE operates on a Bayesian framework that converts methylation proportions into ordered methylation states and jointly infers a low-dimensional representation, cell populations and their number. By using low-rank latent Gaussian factorization and adopting a mixture-of-finite-mixtures prior on latent cell scores, SMORE infers cell assignments without requiring a prespecified cluster number, and propagates uncertainty from methylation measurements to cell assignments. Across simulations spanning varying sample sizes, population imbalance, signal strengths and model misspecification, SMORE accurately recovered latent population structure and outperformed existing methods. Applied to human single-cell methylation datasets from lung, peripheral blood and primary motor cortex, SMORE recovered biologically supported cell population structures. SMORE provides an uncertainty-aware framework for dimension reduction and population discovery for single-cell methylomes.

bioinformatics↗