Biology subjects

Wu, W.

Publications and source records attributed to Wu, W..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Language Modeling Materializes a World Model of Protein Biology

Proteins are fundamental to life. The full extent of their biology is beyond our ability to characterize with experimental approaches in the physical laboratory. Accurate digital representations could accelerate the discovery of protein biology through virtual experiments. We propose language modeling to learn unified and general representations that can be scaled to all of protein biology. Building on these representations, we develop a structure prediction model that exceeds the performance of established methods for biomolecular complex prediction across benchmarks, including for the interactions of antibodies with their targets. A simple search procedure yields high experimental success rates for the discovery of proteins with nanomolar binding affinities for both miniproteins and single-chain antibodies, a modality critical for therapeutic design. Study of the concepts in the language models representation space reveals a systematic organization aligned with the reductionist understanding of proteins developed through empirical science. Leveraging this organization, we generate a comprehensive map of protein biology encompassing over 6.8 billion sequences and 1.1 billion predicted structures, identifying connections across known and unknown biology. As a whole, this shows language modeling as a powerful substrate for representing the biology of proteins, operating across scales from the prediction and design of protein interactions at the atomic level, to identifying properties of proteins at different levels of granularity and abstraction, to the scale of mapping connections between proteins across billions of years of evolution.

bioinformatics↗

bioRxiv2026

Alignment-free phylogenetic inference via hyperbolic protein language models

Conventional phylogenetic methods rely on multiple sequence alignments which are computationally intensive and often fail for highly divergent lineages. Here, we introduce LucaPhylo, an alignment-free framework that infers evolutionary relationships directly from unaligned sequences. Through a cascaded learning strategy LucaPhylo integrates protein language models with hyperbolic geometry, a representation space naturally suited to hierarchical branching, to capture deep evolutionary constraints without explicit homology matching. Using highly divergent RNA virosphere as a test case, LucaPhylo places unaligned sequences into phylogenetic trees with an accuracy comparable to leading alignment-based tree construction tools, while retaining divergent sequences that conventional pipelines frequently discard. It further enables the integration of divergent viral lineages into phylogenetic trees, thereby expanding the evolutionary landscape of RNA viruses. Together, LucaPhylo establishes an AI-driven, alignment-free paradigm for phylogenetic inference and provides a robust computational foundation for resolving deep evolutionary relationships among RNA viruses and other biological systems.

evolutionary biology↗

bioRxiv2026

The HOG MAPK - Transcription Factor CsAtf1 - CsErg5B Regulatory Module Mediates Conidial Germination and Fludioxonil Sensitivity in Colletotrichum siamense

Colletotrichum siamense is a predominant causal agent of anthracnose in rubber tree and numerous economically important crops, causing severe yield losses worldwide. Conidial germination represents a critical early step for successful infection, while the high-osmolarity glycerol (HOG) MAPK pathway and ergosterol biosynthesis individually govern fungal development, stress adaptation and fungicide responses. However, the molecular crosstalk between these two modules remains largely elusive in phytopathogenic fungi. Here, we identified CsErg5B, a sterol C-22 desaturase homolog, as a direct target of the HOG- regulated transcription factor CsAtf1 in C. siamense. CsErg5B was indispensable for ergosterol biosynthesis, conidial germination, appressorium formation, and full virulence. The {Delta}CsErg5B mutant showed increased conidiation but severely impaired germination, and exhibited elevated resistance to fludioxonil while hypersensitivity to azole fungicides. Epistasis analysis using the {Delta}CsErg5B/{Delta}CsCyp51G1 double mutant - where CsCyp51G1 serves as another downstream target of CsAtf1 - revealed that CsErg5B functions as the predominant downstream effector of CsAtf1 in modulating conidial development and fludioxonil sensitivity. Furthermore, overexpression of CsErg5B significantly rescued the defects in conidial germination and fludioxonil sensitivity in both {Delta}CsAtf1 and {Delta}CsPbs2 mutants. Taken together, our findings uncover a HOG MAPK - CsAtf1 - CsErg5B regulatory axis that connects HOG MAPK signaling to ergosterol homeostasis, thereby governing conidial germination and fungicide sensitivity in C. siamense. This study provides novel insights into the regulatory network underlying fungal development and fungicide response, and offers promising molecular targets for the integrated management of plant anthracnose.

microbiology↗

bioRxiv2026

KAS-CUT&Tag for direct mapping of transcription bubbles

Transcription by RNA Polymerase II (Pol II) generates dynamic transcription bubbles as it moves forward, but existing methods map transcriptional activity in vivo only indirectly. We introduce KAS-CUT&Tag, a method that combines N3-kethoxal labeling of exposed guanines with CUT&Tag to directly map transcription bubbles. We find that bubble density varies across Pol II-bound genes at transcription start sites, gene bodies, and termination sites. Transcription bubbles are enriched at genes marked by the H3K36me3 histone modification and chromatin-bound splicing factor U2AF2, but the highest enrichment is at replication-coupled histone genes throughout the cell cycle. KAS-CUT&Tag also detects colocalization of the histone gene transcription factor NPAT with Pol II at the Histone Locus Body, suggesting NPAT is juxtaposed to transcription bubbles via interaction with Pol II. Together, KAS-CUT&Tag enables direct mapping of Pol II and regulatory interactions at transcription bubbles, providing a powerful tool for precise analysis of transcriptional activity.

genomics↗

bioRxiv2026

A Comprehensive Computational Model of the Human head for Designing and Optimizing Visual Brain-Machine Interfaces

Brain-machine interfaces (BMIs) for vision restoration require models that accurately simulate the anatomy and electrical properties of visual pathways. However, current models focus only on isolated structures, such as the retina or brain, and overlook surrounding tissues. Here, we present a comprehensive computational model of the human head, incorporating the entire visual pathway--including the eye, optic nerve, and brain--along with critical neighboring tissues such as the orbit, paranasal sinuses, enabling precise simulations. Validation using human and large animal data demonstrated a strong correlation between the simulated and measured electrical potentials. Component elimination analysis revealed that the optimized comprehensive model outperformed simplified versions. The models utility was demonstrated through multiple applications: (1) comparative analysis of electrical neuromodulation technologies for optic neuropathy, revealing the filed intensity limitations of noninvasive approaches and the safety concerns of invasive intraorbital approach; (2) identification of optimal stimulation site, revealing that transnasal stimulation at the optic chiasm outperformed traditional approaches; and (3) in silico design of electrode arrays for optic nerve prosthetics, demonstrating theoretical advantages in invasiveness and visual field coverage compared to existing retinal and cortical prosthetics. This validated and versatile computational resource supports the development of neuromodulation strategies and visual BMI technologies.

bioengineering↗

bioRxiv2026

Orai1 is required for Ca2+-dependent plasma membrane repair and mechanoadaptation

Ca2+-dependent repair of plasma membrane breaches is essential for animal cell viability. An initial passive influx of extracellular Ca2+ triggers the formation of a protein plug that rapidly seals breaches. However, the mechanism of extracellular Ca2+ requirement for subsequent repair remains undefined. EHD2 protein stabilizes the plasma membrane caveolae, which sustain membrane repair, and maintains high surface levels of the caveolae-resident Ca2+ channel Orai1. We establish the requirement of both Orai1 and EHD2 for repair of plasma membrane lesions induced by mechanical injury or by a model bacterial pore-forming toxin. We demonstrate rapid EHD2 recruitment and Orai1-mediated Ca2+ entry at plasma membrane sites of localized mechanical stimulus, the latter requiring EHD2 and CAV1. EHD2 and Orai1 are necessary for mechanosensitive YAP/TAZ-TEAD activation and positive feedback for CAV1 expression that promotes membrane repair. Our studies establish EHD2 and Orai1 as novel components of mammalian plasma membrane repair and mechanoadaptation.

cell biology↗

bioRxiv2026

Scaling Multiplex qPCR Primer Design to 1000-plex using the Degenerate Incomplete Multiplex Primer List Extension (DIMPLE) Algorithm

Massively multiplexed qPCR is primarily constrained by increasing primer dimer formation as the number of distinct primers in a single reaction increases. Previous multiplex primer design algorithms either fail to sufficiently suppress primer dimers at 100+ plex, or take exceedingly high amounts of computational resources to complete. Here, we present DIMPLE, a linear-runtime primer design algorithm that effectively generates 10,000+ primers to amplify thousands of potential amplicons in a single qPCR reaction. As one clinical demonstration of this algorithm, we designed an assay to detect 2,302 distinct KMT2A gene fusion subtypes using 204 primers in a single tube. In contrast to FISH and convention NGS approaches with 2% variant allele frequency (VAF) limit of detection, our DIMPLE qPCR assay was able to analytically detect gene fusions down to 0.05% VAF. We also constructed proof-of-concept multiplex qPCR panels for additional oncology gene fusions, multiplex pathogen detection, and DNA methylation markers. The scalability and low computational cost DIMPLE are complementary to new instrument platforms for massively multiplex qPCR readout for enabling rapid, point-of-care nucleic acid testing.

bioengineering↗

bioRxiv2026

Volume regulation of cancer cells during osmotic pressure variation

Osmotic pressure is a fundamental physical determinant of cellular homeostasis, and its perturbation is recognized as a critical factor in cancer progression and therapy. However, whether cancer cells and normal cells respond to osmotic pressure changes differently remains unclear. Here, we subjected cancer and normal cells to osmotic shocks and compared their volume and traction force changes. Under hypotonic conditions, cancer cells recovered their volume and forces much more slowly than normal cells, although both cell types responded similarly to hypertonic shock. We found that actin-based cortical tension controlled the recovery speed; whereas myosin-mediated contractility tuned the volume change extent. Substrate stiffness also influenced the recovery process by altering cytoskeletal constraint. A theoretical model integrating adhesion energy, ion and water transport, and surface tension was developed. The results matched the experimental observations. Our work uncovered the distinct mechanobiological behaviors of cancer cells upon hypotonic shock, providing a mechanical framework for understanding cancer mechanisms that may inform the development of cancer therapeutics.

biophysics↗

bioRxiv2026

Multi-omics and functional analysis of a bioengineered vascularized pancreatic cancer model reveal an immunosuppressive and therapy-resistant niche

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease characterized by therapy resistance and an immunosuppressive tumor microenvironment. To comprehensively characterize the complex stromal-immune cell interactions that drive PDAC aggressiveness, we applied an integrated multi-modal approach combining single-cell RNA sequencing, spatial transcriptomics, proteomics, immunofluorescence, and microfluidic-based functional assays to bioengineered spheroid models with increasing cellular complexity (up to four cell types) integrating human pancreatic cancer cells, pancreatic stellate cells, endothelial cells, and monocyte-derived macrophages. By incorporating vascularization within the OrganiX microfluidic platform, we enable studies of immune cell trafficking in vascularized tumors. Multi-omics phenotyping revealed coordinated molecular programs in our four-cell organotypic spheroid models, including enhanced hypoxic and glycolytic pathways, NF-{kappa}B activation, and ECM remodeling. Stromal and immune cells acquired tumor-associated phenotypes mirroring patient heterogeneity, including IL-1{beta}+ macrophages, inflammatory cancer-associated fibroblasts (iCAFs), and antigen-presenting CAFs (apCAFs). The four-cell model exhibited superior clinical relevance, with gene expression signatures that correlated more closely with poor-prognosis patient cohorts and cancer hallmarks that were functionally validated through microfluidic-based assays demonstrating enhanced tumor invasion, angiogenesis, and therapeutic resistance. Finally, live imaging combined with transcriptomic readouts captures dynamic interactions between neutrophils and cancer cells in the vascularized microtumor, enabling direct observation of intravascular events relevant to metastatic dissemination. This integrated analysis demonstrated the recreation of a human-relevant aggressive PDAC niche, establishing a framework that bridges in vitro cellular crosstalk studies with patient-relevant therapeutic responses, offering a powerful translational tool for therapy development in PDAC.

cancer biology↗

bioRxiv2026

CEACAM5/6+ Tumor Cells and IL-1β+ Macrophages Drive Resistance to Chemo-immunotherapy in Gastric Cancer

Chemo-immunotherapy is a first-line treatment for advanced gastric cancer, yet response rates remain limited and resistance mechanisms are poorly defined. Here we generate a single-cell atlas of 542,121 cells from 35 patients treated with anti-PD-1 plus chemotherapy, profiling pre- and post-treatment tumors linked to clinical response. Integrating spatial transcriptomics, immunohistochemistry, and bulk RNA sequencing, we identify two temporally distinct resistance programs. Intrinsic resistance in pre-treatment non-responders is marked by enrichment of CEACAM5/6 tumor cells that form immune-excluded spatial niches characterized by macrophage recruitment and CD8 T-cell exhaustion. Acquired resistance in post-treatment non-responders is driven by expansion of IL-1{beta} macrophages, which induces coordinated NF-{kappa}B activation across tumor and stromal compartments, promoting PD-L1 upregulation, epithelial-mesenchymal transition, and chronic inflammation. These findings delineate an evolutionary trajectory of resistance and nominate CEACAM5/6 and IL-1{beta} as predictive biomarkers and therapeutic targets to improve anti-PD-1-based combination strategies.

cancer biology↗

bioRxiv2026

Toward Computationally Complete Spatial Omics

Multimodal spatial omics has transformed biology by mapping molecular complexity within intact tissues, yet current technologies remain limited in the number of modalities measured simultaneously and often produce lower-quality data than single-modality assays. We present COSIE, a computational framework that generates high-resolution, multilayered molecular landscapes across tissue sections, individuals, and platforms. COSIE integrates histology, epigenome, transcriptome, proteome, and metabolome into a unified representation. Applied to 12 datasets spanning 10 spatial technologies, eight modalities, and nine tissue types, ranging from thousands of spots to millions of cells, COSIE outperforms existing methods. It resolves tissue structures, enhances noisy measurements, predicts unmeasured modalities, and captures dynamic processes. In human tumors, COSIE identifies invasive subregions linked to clinical outcomes and predicts spatial gene expression in TCGA samples using only histology images. By transforming fragmented data into comprehensive spatial maps, COSIE advances computationally complete spatial omics and the creation of digital tissue twins for biomedicine.

genomics↗

bioRxiv2026

Cell Type Architecture and Positional Gene Gradients in an Adult Animal at Subcellular Resolution

The spatial organization of cell types and gene expression underlies tissue architecture and organismal physiology. However, resolving complete cellular and molecular landscapes within a three-dimensional context remains challenging, particularly in organisms with high cellular heterogeneity and complex geometry. Here, we developed a semi-supervised workflow to reconstruct high-resolution three-dimensional spatial transcriptomic models of the planarian Schmidtea mediterranea at single-cell resolution. Planarians are basal bilaterians capable of regenerating body structures after injury. Our reconstruction maps the distribution of cell types and spatially patterned gene expression across the intact organism and identifies 119 candidate positional control genes (PCGs). These genes are expressed across muscle, neural, and epidermal populations. Functional perturbation of selected candidates, including pitx3, ptpn11, pi4ka, upf3b, and cul1, revealed roles in regeneration following injury. In addition, we identify intestinal cells as prominent components of the microenvironment surrounding adult tgs1 pluripotent stem cells (neoblasts). Together, these results demonstrate the utility of three-dimensional spatial transcriptomic reconstruction for mapping cellular architecture and positional gene regulation in complex adult organisms.

developmental biology↗

bioRxiv2026

A biofidelic Goat Model of Traumatic Optic Neuropathy with Optic Canal Fracture via Transnasal Endoscopy

Traumatic optic neuropathy (TON) is a major cause of irreversible vision loss following blunt cranial trauma, yet the absence of clinically relevant large animal models that faithfully recapitulate human TON has significantly impeded translational research. Current rodent models do not reproduce optic canal fracture, a key injury mechanism in many patients with TON. Here, we combined high-fidelity finite element analysis (FEA) with iterative engineering to establish a reproducible goat model of TON. We first built a high-resolution human-head finite element model to characterize force transmission to the optic nerve. Across clinically relevant periorbital impacts, stress preferentially concentrated in the intracanalicular segment, reaching a peak force density of approximately 500 N/m2 at 50.6 ms, about fivefold higher than in the intraorbital segment. Simulations further showed that direct optic canal impact reproduced comparable intracanalicular stress with a markedly lower input force: 195 N, compared with 3900 N for periorbital impact. Guided by these insights, we developed transnasal endoscopic impact systems capable of inducing optic canal fractures in goats. TON was confirmed within 24 hours by a characteristic relative afferent pupillary defect (RAPD), and at 1 month post-injury goats (n = 14) exhibited a 10%-20% reduction in ganglion cell complex (GCC) thickness and 40%-65% reductions in flash visual evoked potential (FVEP) and pattern electroretinogram (PERG) amplitude ratios (all P [≤] 0.001), with structural and functional preservation of the fellow eye. This study presents a robust, standardized, and clinically relevant large-animal platform for investigating TON pathophysiology.

neuroscience↗

bioRxiv2026

4D Single-Cell Spatial Transcriptomics Reveals Dynamic Morphogenetic Gradients and Regenerative Domains in Planarians

Regeneration relies on precise spatiotemporal gene expression and cellular responses to establish tissue identity and body patterning. Using high-resolution Stereo-seq (715 nm) on 353 sections from 16 whole animals at 8 regeneration timepoints, we constructed a 4D spatiotemporal transcriptomic map of planarian regeneration. Our analysis captured 36 refined cell types from 3,508,004 segmented cells, enabling genome-wide transcriptional imputation of gene expression dynamics across body axes at cellular, tissue, and organismal scales. We identified dynamic positional gradients and distinct spatially distributed cell types during regeneration, including an injury-induced Anterior Regenerative Zone (ARZ). The ARZ exhibited enriched positional signals in epidermal, muscle, and neural cells and was regulated by Mediator 8, which is crucial for polarity remodeling and blastema formation. This study provides a comprehensive spatial molecular and cellular map of regenerative processes, highlighting injury-induced spatial domains and key regulatory factors in planarian regeneration. We also provide an interactive web portal, offering a valuable resource for exploring and analyzing regeneration mechanisms in a spatiotemporal context.

developmental biology↗

bioRxiv2026

Integrative Single-Cell Epigenomic Atlas Annotates the Regulatory Genome of the Adult Mouse Brain

Histone modifications underpin the cell-type-specific gene regulatory programs that drive the remarkable cellular diversity of the mammalian brain. Here, we jointly profiled four histone modifications and the transcriptome in 2.5 million nuclei from the adult mouse brain. By integrating these data with chromatin accessibility, DNA methylation, and three-dimensional genome organization, we constructed a unified epigenomic atlas spanning over 100 brain cell subclasses, and assigned active, primed, and repressive chromatin states across 81% of the genome. Active chromatin states marked by combinatorial histone modifications better nominate candidate enhancers with functional support than chromatin accessibility alone, while H3K27me3- and H3K9me3-marked chromatin states delineate distinct modes of cell-type-specific gene repression. Finally, this multimodal resource enables deep learning models that predict cell-type-specific epigenomic features and gene expression from DNA sequence, providing a comprehensive framework for annotating the brain regulatory genome and interpreting non-coding disease risk variants. HIGHLIGHTS- A single-cell epigenome atlas of transcription and four histone modifications across 2.5M mouse brain cells - Multimodal integration maps chromatin states across [~]81% of the adult mouse brain genome - Cell-type-resolved chromatin landscapes reveal regulatory programs mediated by enhancers, Polycomb and H3K9me3 - Deep learning models predict cell-type-specific epigenomic features and gene expression from DNA sequence

genomics↗

bioRxiv2026

Detection of Somatic Point Mutations Directly from Spatial Transcriptomics Enables in vivo Spatiotemporal Lineage Tracing

Spatial transcriptomics reveals tissue organization but lacks in vivo lineage-tracing methods applicable to humans. We introduce SpaceTracer, a computational framework that accurately detects somatic single-nucleotide variants (SNVs) directly from spatial transcriptomics data. By leveraging naturally occurring somatic SNVs, SpaceTracer reconstructs cellular phylogenies within native tissue architecture, enabling the mapping of lineage spread, migration, lineage-coupled expression changes and lineage-aware local interactions. Applied to human cutaneous squamous cell carcinoma, it traced tumor initiation and progressions, uncovered widespread pre-invasive migration of dedifferentiated epithelial cells and characterized mutant B cells migrating from tertiary lymphoid structures (TLS) into the tumor boundary. The framework also reconstructed developmental lineages across multiple tissues and identified tissue-resident mutant immune cells. SpaceTracer thus provides a perturbation-free platform for high-resolution spatiotemporal lineage tracing, offering a transformative tool for elucidating complex biological systems--especially tumor-immune ecosystems--with direct implications for advancing cancer immunotherapy.

bioinformatics↗

bioRxiv2026

Functional In-Context Learning in Genomic Language Models with Nucleotide-Level Supervision and Genome Compression

AO_SCPLOWBSTRACTC_SCPLOWGenomic foundation models aim to learn general-purpose representations directly from DNA sequence, enabling sequence understanding, generation, and probabilistic reasoning across a wide range of biological tasks. Scaling such models to genomic lengths, however, remains challenging due to the tension between long-range context, nucleotide-level resolution, and practical computational efficiency. Architectural innovations have enabled increasingly long nominal inputs, but often struggle to translate additional context into meaningful performance gains, particularly in the presence of sparse functional signal along eukaryotic genomes. In this work, we revisit the design of long-context genomic foundation models from the perspective of training objective and data construction. We introduce Factorized Nucleotide Supervision (FNS), which reconciles efficient k-mer tokenization with single-nucleotide likelihoods through probability marginalization, and Genome Compression Pretraining (GCP), which reshapes the training distribution by concentrating on gene-centric and regulatory regions. Together, these techniques enable standard transformer-based models to perform functional in-context learning without sacrificing nucleotide-level fidelity or computational efficiency. Building on these ideas, we present a family of autoregressive genomic foundation models supporting contexts of up to 98k base pairs across eukaryotic and prokaryotic genomes. Across training-free evaluations and downstream fine-tuning benchmarks, our models consistently improve over prior approaches and match or exceed state-of-the-art baselines while enabling substantially more efficient inference. Together, these results demonstrate that aligning supervision and data regimes with the biological structure of genomic sequence provides a principled and effective path toward scalable and biologically faithful genomic language modeling. Models, data, and scripts for downstream analyses are publicly available at https://huggingface.co/GenerTeam.

genomics↗

bioRxiv2026

Iron Deficiency Drives Sarcopenia in the Elderly: HIF-1α-Mediated Fibro-Adipogenic Progenitor Differentiation Induces Fat Infiltration and Impairs Muscle Function

Abnormal iron metabolism is closely linked to sarcopenia; however, the specific iron metabolism features of fat-infiltrating sarcopenia remain poorly understood. Proteomic sequencing revealed that in skeletal muscle with severe fat infiltration, the expression of iron utilization-related proteins was significantly downregulated, whereas that of iron uptake and storage proteins was markedly upregulated, thereby presenting an abnormal iron deficiency (ID) phenotype. Nevertheless, the mechanism by which ID drives intramuscular fat infiltration has not been fully elucidated. Using clinical samples, aged murine ID models, and in vitro cell assays, this study is the first to demonstrate that ID stabilizes hypoxia-inducible factor-1 (HIF-1), promoting aberrant adipogenic differentiation of fibro-adipogenic progenitors (FAPs), disrupting the homeostatic balance between satellite cells (SCs) and FAPs, exacerbating skeletal muscle fat infiltration, and impairing muscle repair capacity. Notably, treatment with the HIF-1 inhibitor PX478 reversed these pathological alterations and improved muscle function. Collectively, our findings identify the ID-HIF-1-FAPs axis as a key driver of intramuscular fat infiltration, offering a novel therapeutic target for the clinical management of fat-infiltrating sarcopenia.

cell biology↗