Biology subjects

Cui, Y.

Publications and source records attributed to Cui, Y..

At least 19 recordsLinked 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

Where Tabular Foundation Models Falter on Genetic Data: Datasets That Expose and Provide a Path to Address the Gap

Tabular foundation models are used as off-the-shelf predictors for heterogeneous tabular tasks, but it remains unclear how they will perform on real genotype-to-phenotype tabular datasets, which carry unique challenges. One such challenge is ancestry-dependent non-stationarity in allele effect sizes (the effects of genetic variation on disease risk): the predictive contribution of a given genetic variant may change significantly across the ancestry spectrum. This is especially consequential for patients from ancestry groups that are underrepresented in existing datasets, because models that are unaware of this non-stationarity are more likely to perform poorly for them. The battery of synthetic tasks used to pretrain tabular foundation models does not capture this structure, and we show that it leads to systematic performance degradation. Using controlled hierarchical Gaussian-process stress tests, we demonstrate that both off-the-shelf TabICL and TabPFN are robust when ancestry dispersion in the data is low, but as dispersion grows, the latent ancestry-dependent non-stationarity of effect sizes becomes a first-order problem, and the predictive performance of both models deteriorates. We confirm the same pattern on All of Us (AoU) (a large biobank containing whole-genome sequencing and electronic health record (EHR) data spanning the ancestry spectrum) across an extensive panel of cancer phenotypes evaluated with the two leading tabular foundation models: TabICL and TabPFN. Holding the in-context training-set size, phenotype, and feature set fixed, ancestry-specific in-context tables, which are less dispersed in ancestry space, consistently outperform size-matched meta-ancestry tables, isolating ancestry dispersion as the driver of degradation. To address the failure without changing the base architecture, we construct two synthetic task families that explicitly encode ancestry-dependent effect drift and instruction-tune an off-the-shelf TabICL model on tasks sampled across both families. On held-out AoU evaluations covering cancer and respiratory disease phenotypes, the tuned model yields more stable performance across ancestry-distance bins and is especially strong in the bins farthest from the center of the in-context exemplars in ancestry space, that is, on subjects whose ancestry is most underrepresented among the provided exemplars. The paper contributes an evaluation protocol, a failure analysis, and two synthetic task families targeted at ancestry-dependent non-stationarity in precision-medicine tabular modeling.

bioinformatics↗

bioRxiv2026

Pop-Corn: Predicting Perturbation Phenotype Effects Across Single-Cell and Spatial Contexts

Genetic perturbations can reshape cell populations by altering the relative abundance of specific cell types and states within the profiled population, including increases, decreases and states that become detectable after perturbation. Pooled single-cell screens, such as Perturb-seq, measure such responses at scale. However, only a small fraction of possible perturbations can be tested experimentally. A central challenge is therefore to predict compositional shifts induced by unseen perturbations. Many perturbation-prediction methods do not directly optimize for this outcome; instead, they predict gene-expression responses and infer cell-type and cell-state composition downstream. Surprisingly, we find that even models that accurately predict perturbation-induced changes in average gene expression perform poorly at forecasting these compositional shifts. To address this gap, we present Pop-Corn, a method that directly predicts how a perturbation reshapes cell-type composition without reconstructing gene expression. In the primary T-cell benchmark, Pop-Corn predicted the overall cell-state composition of held-out perturbations more accurately than the evaluated expression-prediction pipelines, while better preserving the diversity of observed cell states. We further extend Pop-Corn to intact tissue, where it predicts perturbation-induced cell-type proportion changes in local cellular neighborhoods and uses attention patterns to generate hypotheses about context-dependent cellular interactions. Retrospective virtual screens support the use of Pop-Corn to prioritize perturbations for experimental follow-up according to their predicted effects on cell-state composition.

bioinformatics↗

bioRxiv2026

CycDelta: Multimodal Residue-Graph Learning of Permeability Changes in Cyclic Peptides

Membrane permeability remains a major challenge in cyclic-peptide drug discovery, where optimization often depends on estimating changes relative to measured reference compounds. Existing predictors mainly model absolute permeability, while heterogeneity across literature sources complicates pooled training and comparative evaluation. We present CycDelta, a multimodal residue-graph model trained directly on within-source permeability differences. CycDelta combines directed message passing with pretrained Uni-Mol monomer embeddings, physicochemical descriptors and assay information in a shared encoder for reference and query peptides. To evaluate this comparative formulation under controlled conditions, we establish CycDeltaBench, comprising in-distribution, scaffold-based out-of-distribution, external and zero-shot unseen-assay tests. On the out-of-distribution task, CycDelta achieved Pearson 0.69 versus 0.46 for the strongest baseline. It also led all evaluated methods on two external data sets and achieved zero-shot Spearman 0.70 versus 0.32 for the strongest baseline on an unseen assay. Residue-level attribution relates predictions to monomer contributions, while gradient-guided screening supports prioritization of permeability-enhancing analogue hypotheses. These results position CycDelta as a transferable and interpretable framework for comparative permeability prediction and cyclic-peptide optimization.

bioinformatics↗

bioRxiv2026

Radiation therapy synergizes with mRNA vaccination to overcome microglia-mediated suppression of dendritic cell migration and T cell priming in glioblastoma

Glioblastomas (GBMs) are uniformly fatal brain tumors resistant to immunotherapy, yet the mechanisms driving immune dysfunction remain poorly understood. Here we identify a tissue-specific mechanism of dendritic cell (DC) dysfunction contributing to GBM immune evasion. We demonstrate that while systemic immunity depends on type 1 conventional DCs (cDC1s), GBMs preferentially accumulate cDC2s with impaired antigen-presenting capacity. Further, microglia-derived GAS6 and PROS1 signaling through the AXL receptor on DCs suppresses DC activation and, critically, migration to tumor-draining lymph nodes. Pharmacologic AXL inhibition enhances DC migration and consequently anti-tumor immunity. Importantly, radiation therapy activates cDC1-mediated endogenous immune responses, which synergizes with mRNA vaccination to promote complete and durable immune-mediated tumor regression. These findings establish DC-mediated immunity as a therapeutic target in brain tumors and reveal a synergistic mechanism for combining radiation with personalized cancer vaccines.

cancer biology↗

bioRxiv2026

The RNA modification N6-methyladenosine restrains SIRPα to license myeloid efferocytosis in atherosclerosis

Background: Macrophages participate in atherosclerotic plaque development, progression and resolution, integrating lipid and inflammatory cues to balance tissue damage and repair. Despite considerable understanding of macrophage function in atherosclerosis, the underlying regulatory mechanisms governing these responses continue to evolve. Emerging evidence implicates epitranscriptomic gene regulation by the RNA modification N6-methyladenosine (m6A) in control of context specific immune responses, but its role in atherosclerosis is not well-defined. Methods: We integrated m6A quantitative trait loci (m6A QTLs) with Coronary Artery Disease (CAD) GWAS summary statistics. Using Oxford Nanopore direct RNA sequencing and MeRIP-seq, we constructed a high-resolution map of m6A modifications in macrophages. To determine the functional consequences of myeloid m6A loss in-vivo, we generated a myeloid-specific loss-of-function mouse model of m6A (Mettl14f/f LysMCre) and performed atherosclerosis studies using bone marrow transplantation into irradiated Ldlr-/- mice. Atherosclerosis lesions were analyzed using light microscopy and immunofluorescence imaging, with complementary in-vitro mechanistic studies performed in murine bone marrow derived macrophages (BMDMs). Results: m6A quantitative trait loci (m6A-QTLs) were enriched in CAD GWAS loci relevant to immune and lipid function, implicating m6A regulation in human CAD genetics. Using Oxford Nanopore direct RNA sequencing in conjunction with public MeRIP-sequencing data, we generated a single-base resolution, stoichiometric map of m6A modification in macrophages, nominating genes central to macrophage biology in atherosclerosis as high-confidence m6A targets. Myeloid specific deletion of the m6A "writer" complex subunit Mettl14 drove the development of larger, more necrotic and unstable atherosclerotic plaques on an Ldlr-/- background compared with Mettl14f/f controls, establishing an atheroprotective role for myeloid m6A. Loss of myeloid Mettl14 impaired macrophage efferocytosis in atherosclerotic plaques, in a dexamethasone-induced apoptosis in the thymus and in-vitro. Molecularly, m6A methylation of the "don't eat me" antiphagocytic receptor SIRP RNA suppressed its protein expression. Conclusions: These findings identify the myeloid m6A - SIRP axis as a novel homeostatic regulator of efferocytosis and establish epitranscriptomic remodeling as a crucial layer of macrophage regulation in atherosclerosis.

physiology↗

bioRxiv2026

A mycovirus identified from an outbreak fungal species complex reveals a parallel antiviral system

Mycoviruses are prevalent across major fungal lineages, yet none have been reported in the Cryptococcus gattii species complex (CGSC), the causative agent of the largest known fungal outbreak in immunocompetent hosts. Consequently, the occurrence of mycoviruses and the host determinants governing their infection in this important pathogenic clade remain unexplored. Through screening of 386 Cryptococcus isolates, we identified a mycovirus, designated CgTV1, in an environmental isolate of the Cryptococcus gattii species complex (CGSC). CgTV1 is a double-stranded RNA (dsRNA) mycovirus of the genus Totivirus, with a genome encoding a capsid protein and an RNA-dependent RNA polymerase. We showed that CgTV1 establishes a cryptic infection, because neither viral presence nor its clearance significantly alters host transcriptomic profiles, suggesting the existence of mechanisms that maintain virus-host homeostasis. Population genomic analyses reveal that an intact RNAi pathway constitutes a critical genetic barrier, and CgTV1 persistence strictly requires loss of function mutations in both AGO1 and AGO2. By pan-transcriptomics across 43 CGSC strains, we further uncovered an RNase III gene, CAI1, which exhibits high expression in the mycovirus positive isolate. Biochemical analysis indicates that Cai1 directly recognizes and degrades CgTV1 dsRNA, thereby constraining excessive mycoviral proliferation. We further demonstrate that Dicer and Cai1 act in parallel to maintain virus-host homeostasis: their combined ablation results in higher viral loads and more pronounced changes in host gene expression than ablation of either factor alone. Collectively, our findings provide the evidence of the occurrence of a mycovirus in the CGSC and reveal parallel mechanisms that enable cryptic mycoviral infections.

genetics↗

bioRxiv2026

BioIMA: a one-click desktop tool for standardized extraction of phenotypic traits from biological images

Standardized extraction of quantitative phenotypes from images is increasingly important across plant biology, from ecological and evolutionary studies to genetics, breeding, and functional genomics. However, as large image datasets are increasingly used for trait analysis, many biologically relevant traits, including size, shape, color, and spatial patterning, are still measured manually or using fragmented semi-automated workflows. These limitations reduce throughput, reproducibility, and accessibility, especially for researchers without computational expertise. Here, we present BioIMA, an open-source desktop tool for rapid and standardized phenotyping from biological images. BioIMA integrates foundation model-based segmentation with automated trait computation, allowing users to extract quantitative measurements from images through an intuitive graphical interface and without model training. To validate its performance, we quantified a set of knot morphological traits in two Populus species, as these measurements are typically time-consuming to perform manually. Automatic measurements showed strong agreement with manual ImageJ-based measurements (R2 > 0.95), while reducing per-image processing time by approximately 75% (from ~15 s to ~4 s). BioIMA was further applied to diverse plant datasets, including Helianthus and Rhododendron images with varying morphologies and background conditions. Although developed for plant phenotyping, BioIMA may also be extended to other biological samples where region-based size, shape, or color traits are of interest. By combining accessibility and standardization in a lightweight local application, BioIMA provides a practical community resource for image-based phenotyping in ecological and evolutionary studies.

bioinformatics↗

bioRxiv2026

AnchorR: A QuPath and R interface for collaborative exploration of spatial transcriptomics and histology

Single-cell spatial transcriptomics can connect molecular cell states with tissue morphology, but this promise depends on accurate registration to histopathology. In serial sections, however, tissue borders often differ because of sectioning artifacts, staining variability, and field-of-view acquisition, limiting conventional area-based registration. We developed AnchorR, an expert-guided workflow for coarse-grained alignment of hematoxylin and eosin (H&E) images with CosMx Spatial Molecular Imaging data. Bioinformaticians first define and color-code cell types in Seurat, and pathologists then identify corresponding internal landmarks using QuPath overlays. AnchorR combines these paired landmarks to estimate affine transformations, quantify residual error, and support visual quality control and anchor refinement. Using six oral pre-cancerous tissue sections, we identified 60 cross-modal landmarks. Fitting each section independently reduced mean landmark error from 121.5 m with a single whole-slide transformation to 14.6 m. Cross-validation further showed that increasing the number of anchors improved robustness, with nine-anchor fits achieving approximately 20 m error, or about one cell diameter. AnchorR is designed to complement automated computer-vision methods by providing reliable tissue-level alignment when border mismatch makes global registration difficult. By creating a shared workspace for pathologists and bioinformaticians, it operationalizes an expert-in-the-loop approach and makes feature-based multimodal registration accessible without specialized computer-vision expertise or high-performance computing.

bioinformatics↗

bioRxiv2026

Integrative spatial profiling of 3D genome organization and gene expression in tissue

The interplay between 3D genome architecture and transcriptional activity is fundamental to gene regulation. However, existing methodologies cannot simultaneously measure these modalities within intact tissues, limiting our understanding of how genome organization coordinates transcriptional programs across diverse cell types and spatial microenvironments. Here, we introduce Spatial Hi-C-RNA, a spatial multi-omics technology that enables the genome-wide co-mapping of chromatin conformation and transcriptome directly from the same tissue section at near- single-cell resolution. Applied to the mouse embryo and adult brains, Spatial Hi-C-RNA generated high-resolution tissue maps revealing that chromatin organization and gene expression jointly define spatially coherent domains aligned with histological structures. While concordant features were observed across modalities, distinct domain patterns also emerged, indicating that chromatin structure and transcription each contribute complementary layers of spatial regulation. We further demonstrated the robustness and biological insight of Spatial Hi-C-RNA in human melanoma, where both modalities delineated tumor boundaries and microenvironmental niches. Notably, chromatin maps revealed fine-scale tumor subdomains undetectable by transcriptomic profiling alone, highlighting the added resolution provided by spatial chromatin architecture. Integrated analysis revealed that multiscale 3D genome features, from A/B compartments and topologically associating domains to chromatin loops, are closely coupled with domain- and cell-type-specific transcriptional programs. In addition, Spatial Hi-C-RNA resolves spatiotemporal dynamics underlying embryonic lineage specification and tumor progression. Together, these capabilities extend the spatial omics landscape beyond transcriptome and epigenome profiling to the level of chromatin organization, establishing an integrative framework for understanding tissue biology across development and disease.

genomics↗

bioRxiv2026

AMPKγ2 Deacetylation Drives Nuclear Translocation and Doxorubicin-Induced Cardiomyopathy via Nucleolar Stress Signaling

Doxorubicin (Dox)-induced cardiomyopathy (DIC), characterized by cardiomyocyte apoptosis, remains a major clinical challenge in chemotherapy. The regulatory {gamma}2 subunit of AMP-activated protein kinase (AMPK{gamma}2) plays a key role in cardiovascular diseases, but its function in DIC is poorly understood. Here, we report that Dox induces isoform-specific deacetylation and nuclear accumulation of {gamma}2, triggering nucleolar stress and p53-mediated apoptosis. Mechanistically, HDAC3 and TIP60 interact with {gamma}2 and modulate the acetylation of multiple lysine residues within its nuclear localization signal (NLS), controlling its nucleocytoplasmic shuttling. Dox enhances HDAC3-mediated {gamma}2 deacetylation, thereby driving nuclear accumulation of the {gamma}2-containing AMPK ({gamma}2-AMPK) while suppressing the cytosolic AMPK activity. Nuclear {gamma}2-AMPK phosphorylates and inactivates TIF-IA, a key RNA polymerase I-specific transcription initiation factor, leading to nucleolar stress through inhibition of rRNA transcription. rRNA deficit triggers release of free ribosomal proteins (RPs), which bind to and inhibit the E3 ubiquitin ligase MDM2, resulting in p53 stabilization and activation of apoptotic signaling. Using genetically engineered cardiomyocytes and a DIC mouse model, we found that a deacetyl-mimetic {gamma}2 mutant (6KR) exacerbated DIC, whereas an acetyl-mimetic mutant (6KQ) was cardioprotective. Collectively, our findings establish acetylation-driven nuclear translocation of {gamma}2 as a critical node linking Dox-induced nucleolar stress to p53-dependent apoptosis and suggest a promising cardio-oncology strategy that combines HDAC inhibitors with Dox to mitigate DIC. Significance statementDoxorubicin is an effective cancer drug, but its use is limited by cardiomyopathy. Our study reveals that doxorubicin drives HDAC3-mediated deacetylation of AMPK{gamma}2, exposing its nuclear localization signal and redirecting {gamma}2-containing AMPK from the cytoplasm to the nucleus. Nuclear AMPK{gamma}2 phosphorylates TIF-IA, suppresses ribosomal RNA synthesis, and activates a nucleolar stress pathway that stabilizes p53 and promotes cardiomyocyte apoptosis. In mice, a deacetylation-mimetic AMPK{gamma}2 mutant worsens doxorubicin-induced cardiomyopathy, whereas an acetylation-mimetic mutant is protective. These findings uncover an acetylation-controlled spatial switch in AMPK signaling and identify the AMPK{gamma}2 deacetylation-nucleolar stress axis as a potential target for reducing chemotherapy-associated cardiac injury.

cell biology↗

bioRxiv2026

Plaque-associated oligodendrocyte proteostatic failure underlies myelin loss in Alzheimers disease

Alzheimers disease (AD) features amyloid-{beta} plaques and tau pathology, yet the mechanism underlying early and clinically significant myelin loss remains unresolved. Here, we report human iPSC-derived forebrain organoids with doxycycline-inducible expression of SOX10, OLIG2, and NKX6-2 (SON), which generate robust, mature oligodendrocytes and compact myelin in vitro and in vivo. Introducing amyloid precursor protein (APP) pathogenic mutations produces extracellular amyloid-{beta} plaques and phosphorylated tau, accompanied by reduced myelin basic protein (MBP) expression and disrupted myelin ultrastructure. Single-cell and spatial transcriptomics combined with amyloid plaque imaging reveal that oligodendrocytes in plaque-dense regions show pathological changes in calcium signaling, immune activation, lipid remodeling, and protein catabolic pathways. Notably, MBP protein is reduced despite elevated myelin-related transcription, suggesting plaque-induced degradation of myelin proteins. Consistent with this, proteasomal inhibition restores MBP protein levels and improves compact myelin ultrastructure in AD organoids. In human AD brain tissue, protein catabolic pathways are similarly upregulated with increasing plaque density. Together, these findings identify plaque-associated oligodendrocyte proteostatic failure as a candidate mechanism of myelin loss and a potential therapeutic pathway in AD.

neuroscience↗

bioRxiv2026

IgG4⁺ plasma cell enrichment and {lambda}-chain-biased BCR remodeling drive low-grade autoimmunity in chronic obstructive pulmonary disease

BackgroundThis study aimed to elucidate B cell subset pathology in COPD, a poorly characterized area, with a focus on its similarities to and differences from classical autoimmune disorders. MethodsThe single-cell RNA-sequencing (scRNA-seq) data from COPD and autoimmune diseases were obtained from Gene Expression Omnibus (GEO) for comparative analyses of B cell subsets and functions via differentially expressed genes (DEGs), KEGG, protein-protein interaction (PPI), and cell-cell communication analyses. Serum IgG4 was measured by ELISA and correlated with clinical parameters. The peripheral blood B cells were sorted by flow cytometry for single-cell B cell receptor (BCR) sequencing. A v-Abl/Bcl2 pro-B cell line was stimulated with cigarette smoke extract (CSE) to assess abnormal development in vitro. ResultsIn lung tissue, IgG4+ plasma cells were enriched and expressed BCR activation/inflammatory genes and TNF/NF-{kappa}B/MAPK pathways. Serum IgG4 concentrations correlated negatively with pre-and post-bronchodilator FEV1/FVC. B cell interacted with monocytes, macrophages, fibroblasts and endothelial cells via IL-1{beta}/IL-6, integrin and chemokine signalling, contributing to chronic inflammation and remodelling. In peripheral blood, transitional T1 B cells were increased, accompanied by {lambda}-chain enrichment and increased IGLV1-47 usage, as well as enrichment of autoimmune pathways. In the bone marrow, the numbers of pre-B I cells were increased while those of small pre-B III cells were reduced, with altered expression of BCR development genes. CSE stimulation of the pro-B cell line reduced {lambda}5 expression in a concentration-dependent manner. ConclusionsThe autoimmune abnormalities in COPD appear more restricted, although IgG4 antibody generation may contribute to immune-mediated lung damage.

immunology↗

bioRxiv2026

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets

Large-scale clinical and biomedical datasets increasingly contain both diverse subgroup attributes (e.g., demographic or clinical subgroups) and multiple prediction targets. Although various machine learning approaches can address subgroup differences or multi-target prediction, they often consider these aspects independently rather than jointly. To more effectively capture the shared and subgroup-specific information in such complex datasets, we propose the Integrative Transfer Network (ITN), a deep neural network designed to leverage data across subgroups and multiple related outcomes simultaneously. In extensive experiments, including time-to-event and classification tasks where demographic subgroups and multiple disease end-points are prevalent, ITN demonstrates consistent improvements in subgroup-specific prediction by borrowing strength from other subgroups and outcomes. We envision ITN as a unified frame-work for learning from heterogeneous datasets where subgroup-specific insights are critical.

bioinformatics↗

bioRxiv2026

Accurate de novo design of peptides from programming biophysical landscape

Peptides regulate virtually every cellular process and emerge as transformative modalities in bioengineering and therapeutics. However, the de novo design of functional peptides remains challenging, as peptide-protein interactions are governed by delicate and dynamic biophysics that are difficult to model, and experimental datasets are scarce. Here we present NeoPep, a generative deep-learning framework that encodes biophysical principles to accurately design functional peptides de novo. By integrating over 5 million peptide-protein complexes spanning experimentally determined, sequence mimics, and structure ensembles, NeoPep learns the complex biophysical landscape governing peptide function and enables its programmable control. In prospective application across 10 diverse and challenging targets, NeoPep generated potent peptide binders, agonists, and antagonists with hit rates of 12.5-66.7%, even in the absence of defined binding sites or structure information. Beyond de novo co-design, NeoPep supports standalone structure or sequence redesign, readily discriminating subtle context differences. In the structure redesign mode, it generates highly selective peptides with atomic-level conformational accuracy (C RMSD < 2.0 [A] to our solved cryo-EM structure). This structural precision circumvents the need for experimental structure determination, further accelerating iterative sequence redesign to yield a 43.3-fold improvement in potency. These findings establish a general framework for translating biophysical principles into actionable peptide functions, with broad implications for basic research, bioengineering, and medicine.

molecular biology↗

bioRxiv2026

Nucleoporin1 maintains male germ unit organization and transport in Arabidopsis pollen tubes, likely through shaping nuclear morphology

The male germ unit (MGU) in Arabidopsis pollen is comprised of one vegetative nucleus (VN) and two sperm nuclei (SN). It is evolutionarily specialized to deliver immotile sperm nuclei to an ovule for fertilization. Despite some progress in research on MGU, its organization and transport remain only partially understood. Here, we identified Nucleoporin1/136 as a new player in the structural organization and positioning of MGU in pollen tubes. We and others have previously reported the reduced fertility of nup1-1 plants; however, the mechanism remains unknown. In this work, we further examined the role of NUP1 in fertility using two mutant alleles, nup1-1 and nup1-2-/+. The reciprocal crosses between the nup1 mutants and the Col-0 wild type indicate that the nup1 mutant pollen is defective. To study the effect of a complete NUP1 knockout on pollen, we generated a transgenic line that produces pollen with and without NUP1 expression. This work led to the surprising discovery that the NUP1 protein is inherited from the pollen mother cell to the daughter cell during microgametophyte development. Subsequent in vitro experiments showed that NUP1 is required for pollen germination and pollen tube elongation. Further microscopic studies demonstrated that NUP1 is highly expressed in VN and essential for maintaining nuclear shape and size. We also demonstrated that NUP1 is required for proper MGU organization and transport, likely through maintaining VN morphology. Notably, our finding of nuclear morphology-mediated regulation of MGU may also explain the mechanistic details underlying the defective MGU movement in previously reported mutants such as kaku4, wit, and wip, which have abnormal nuclear morphology.

plant biology↗

bioRxiv2026

Equitable Health Intelligence: An Open Benchmark of Multi-Population Machine Learning for Omics-Based Cancer Prognosis

PurposeMachine learning (ML) models for omics-based cancer prognosis are often trained on data from predominantly European-ancestry populations, producing biased predictions for other populations and undermining equitable genomic medicine. Existing fairness benchmarks mainly focus on outcome parity rather than predictive performance parity across populations. Public benchmark resources are needed for systematically detecting and mitigating such performance disparities in multi-population cancer prognosis. MethodsWe developed Equitable Health Intelligence (EHI, https://ehiportal.org), an open-source benchmark of multi-population ML for omics-based cancer prognosis. EHI contains 1,475 ML tasks across 40 cancer/pan-cancer types, 4 omics feature sets, 4 clinical endpoints, 5 event-time thresholds, and 3 data-disadvantaged population (DDP) groups relative to a majority European Ancestry population group. Deep neural network models are trained under three multi-population ML schemes (Mixture, Independent, and Transfer Learning), with Naive Transfer included as a no-adaptation control, comprising a total of 10,325 ML experiments. ResultsThe EHI platform provides an interactive environment with visualization and exploratory tools for users to inspect predictive performance disparities between the majority European-ancestry group and data-disadvantaged populations, evaluate the extent to which transfer learning mitigates these disparities, and examine the impact of feature engineering methods across cancer types, omics features, and clinical endpoints. ConclusionEHI is an open, interactive, and extensible benchmark for identifying and addressing performance disparities in multi-population ML for omics-based cancer prognosis. It provides a foundation for a growing ecosystem of methods targeting ML performance disparities arising from biomedical data inequality and population-level distribution shifts, thereby advancing equitable AI in precision oncology.

bioinformatics↗

bioRxiv2026

Bridging Ancestry Gaps in Genomic Risk Prediction with Tabular Foundation Models

MotivationModels deployed for genomic prediction of diseases perform unevenly across populations, limiting clinical utility. Two factors drive this limitation: large imbalances in sample availability across ancestry groups and non-stationarity of genotype-phenotype effect sizes across the ancestry continuum. While tabular foundation models with in-context learning (ICL) have shown strong sample efficiency in other domains, their effectiveness for genotype-to-phenotype prediction and their robustness to ancestry-driven effect heterogeneity remain unclear. ResultsUsing large, ancestrally diverse biobank data, we show that ICL-capable tabular foundation models reduce performance degradation in under-sampled ancestry groups compared to conventional supervised approaches. However, we find that prevailing models trained on existing synthetic tabular tasks fail when allele effect sizes vary across ancestry space. Treating genetic ancestry as a continuous variable, we introduce an instruction-tuning framework that exposes models to synthetic tasks with ancestry-dependent non-stationary effects. Instruction-tuned models achieve improved and more stable predictive performance across the genetic ancestry continuum, including for individuals distant from in-context exemplars in ancestry space. Availability and ImplementationAll code for instruction-tuning models, synthetic task generation, data wrangling, and model evaluation, is publicly available at https://github.com/ai4pm/Bridging-Ancestry-Gaps-in-Genomic-Risk-Prediction-with-Tabular-Foundation-Models. The final instruction-tuned model (ICL-NS-G2P-proto) is also released in this repository. Detailed documentation is provided, including environment setup instructions and guidelines for running various parts. The instruction-tuning task datasets are available at https://zenodo.org/records/18309187. Contactadas23@uthsc.edu, ycui2@uthsc.edu Supplementary InformationSupplementary data are available online.

bioinformatics↗