Biology subjects

Wu, Q.

Publications and source records attributed to Wu, Q..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

MetaUmbra: Statistically Controlled Genome-Level Presence Inference from Metaproteomic Peptides

Taxonomic interpretation of metaproteomic peptides remains difficult because many peptide sequences are present in proteins from different organisms, reducing taxonomic specificity. Current peptide-centric workflows can report taxonomic summaries or taxon level confidence scores, but they do not provide formal statistical evidence that a taxon is present in the microbiome. Here we present MetaUmbra, a tool that derives genome-level statistical significance values from identified peptides. MetaUmbra builds theoretical peptide lists by in silico digestion of the taxon specific proteins and matches observed peptides against these references. It then combines a conservative significance estimate from unique peptides with a Monte Carlo based p-value for shared peptide evidence estimated under an empirical null model. In the defined community benchmark SIHUMIx, MetaUmbra identified the expected genomes without introducing false-positive genomes after embedding the SIHUMIx genomes in a large gut reference background. In the single strain benchmark Mix24X, all expected genomes were identified with the best statistical significances even after near neighbor and full background expansion. In a hamster gut genome panel, MetaUmbra further preserved an interpretable ranking of candidate genomes in a dense real-data setting. Together, these results show that MetaUmbra can statistically identify the presence of specific microbes in a complex microbiome while maintaining low false-positive calls. MetaUmbra therefore provides a practical framework for converting peptide evidence into genome-level statistical inference in metaproteomics.

bioinformatics↗

bioRxiv2026

DIANNE: Segmentation-Free Localization of Histology Differential Attributes

Pathologist-guided distinctions within histology and spatial omic images provide insights into health and disease, with digital pathology leveraging artificial intelligence to automate such assessments. To train computational models, current digital pathology methods rely on upfront manual annotations, which are time-consuming to generate. Pre-annotation is poorly suited to investigating novel spatial behaviors--a major need driven by advances in spatial profiling--for which annotation criteria and data needs will be uncertain. To address these challenges, we present DIANNE, a digital pathology annotation support and discovery approach for rapid training and inference of spatial differential attributes, enabled by train-time Positive Class Mixup Augmentation. DIANNE can compute foundation model-derived segmentation-free localization of differential classifiers across whole slide H&E images within seconds on a workstation, enabling interactive investigation of spatial niches. Predictive models can be re-trained in real-time in response to patch or regional annotation changes, clarifying determinative biological attributes across slides from only a few dozen annotated patches. We demonstrate the effectiveness of DIANNE for tumor detection, artifact identification, and exploration of pancreatic, fetal membranes and kidney tissue structures. DIANNE also provides analogous capabilities for IHC, multiplex immunofluorescence, and registered spatial transcriptomic+H&E images. DIANNE is implemented in a Jupyter toolkit, enabling rapid development of high-resolution classifiers from weakly-supervised training. DIANNE provides a practical system to quantitatively understand known and novel spatial phenotypes.

pathology↗

bioRxiv2026

Glutamate transporter xCT is important for cGAS-dependent interferon responses to DNA and to HSV-1

Metabolic reprogramming is a key component of antiviral immunity, yet how metabolite transport regulates innate immune signaling remains incompletely understood. Here, we show that infection with herpes simplex virus 1 (HSV-1) and stimulation with cytosolic DNA induce the cellular export of glutamate via the xCT (SLC7A11) transporter and that inhibition of xCT reduces cellular resistance to viral replication. Mechanistically, xCT inhibition impaired cGAS-STING signaling by reducing DNA-induced cGAMP production, thereby diminishing type I interferon (IFN/{beta}) responses and downstream induction of interferon-stimulated genes. Interestingly, modulating intracellular glutamate levels through inhibition of other glutamate pathways, e.g., glutaminolysis or glutamate import, also affected cellular IFN responses, suggesting that glutamate is a central control knob for DNA sensing. Finally, we demonstrate that HSV-1 suppresses xCT expression via a mechanism dependent on the immediate early viral protein ICP27, thereby promoting viral replication by limiting cGAS-dependent IFN induction. Together, these findings identify xCT-dependent glutamate transport as a critical metabolic regulator of cGAS-STING-mediated antiviral immunity.

immunology↗

bioRxiv2026

Integrated metabolomics and proteomics from voxelated cortical hemispheres of adult rhesus monkeys

The spatial organization of molecular networks across cortex likely contributes to differences in local circuit vulnerability in aging and Alzheimers disease; yet many existing molecular datasets sacrifice spatial structure, sampling only a handful of regions per brain. Here, we present a framework for generating spatially registered, paired metabolomic and proteomic maps across an entire cortical hemisphere of an adult rhesus monkey, at millimeter resolution. One hemisphere each from two animals was harvested under controlled conditions, approximately flattened, and hand dissected at different sampling resolutions (roughly 2.5 and 4 mm/side) into tissue voxels. Each voxel was split after homogenization and extraction to provide matched aliquots for targeted metabolomics and deep untargeted proteomics. To handle these high dimensional data, we developed PChclust, a principal component guided feature clustering algorithm. For cross omic integration, we developed a spatially regularized sparse canonical correlation analysis (sr-sCCA), which incorporates spatial neighborhood structure via graph Laplacian smoothing. We recover meaningful biology: Molecular similarity between neighboring voxels decayed with distance in both modalities, confirming that voxelation captures spatially organized biological variance. The sr-sCCA identified joint proteome-metabolome components with coherent cortical gradients that were conserved across animals. Pathway enrichment analysis recovered brain relevant ontologies and reconstructed complete metabolic circuits from single voxels.

neuroscience↗

bioRxiv2026

WAVE2 and REST/NRSF Regulate Clustered Gene Expression by Maintaining Heterochromatin Organization

The actin polymerization machinery, comprising the ARP2/3 complex and its activators, the WASP family proteins, has been implicated in regulating a broad spectrum of nuclear processes, such as transcriptional regulation and nuclear organization. Here, using clustered protocadherin (cPcdh) and {beta}-globin genes as model systems, we showed that WAVE2, a member of the WASP family, regulates chromatin organization by maintaining heterochromatin dynamics. Specifically, by CRISPR DNA-fragment editing, in conjunction with integrated analyses of ChIP-seq, MeDIP-seq, ATAC-seq, 4C-seq, and RNA-seq, we showed that deposition of H3K9me3, a key heterochromatin mark, is significantly decreased at the cPcdh locus upon WAVE2 deletion, concurrent with aberrant accumulation of CTCF/cohesin complex at promoter regions and spatial reorganization of chromatin architecture around nucleolus. In addition, REST/NRSF exerts a similar heterochromatindependent effect on the cPcdh locus. Finally, genetic and genomic data showed that WAVE2 regulates {beta}-globin gene expression by maintaining heterochromatin status. Together our data suggested that WAVE2 and REST/NRSF regulate clustered gene expression in a heterochromatin-dependent manner.

molecular biology↗

bioRxiv2026

Ocrelizumab Modulates Both B and T Cell Immune Capacities in Multiple Sclerosis

To understand the molecular and cellular mechanisms beyond B-cell depletion with the anti-CD20 monoclonal antibody ocrelizumab, we used comprehensive muti-modal flow cytometry and functional assays in a prospective longitudinal multiple sclerosis (MS) cohort. Ocrelizumab depleted the vast majority of B cells and showed selective effects on different B cells subsets. Analysis of residual/replenished B cells revealed relative enrichment of regulatory B cells like CD27+CD43+ B1 and CD24hiCD38hi transitional B cells, and reduction of CD27+ memory B cell subsets and CD19+IgD+CD27-naive B cells at early time points (1-3 month) and before subsequent infusions at 4-7 months, 11-14 months, and >18 months. CD20+ T cells and peripheral helper T-cells (Tph) were also reduced. RNA sequencing analysis showed B1 cells have significantly higher expression of LGALS1, KCNN4, ITGB1, and IL2RB. Compared to transitional B cells, B1 cells also displayed significantly higher expression of tissue homing molecules ITGAX (CD11c), S100A4, ITGB1, and CXCR3. IL10 signaling pathway is increased in these B cells. Ex vivo B cell functional assays indicated the residual/replenishing B cells were anergic following ocrelizumab, with increased IL10/TNF and IL10/IL6 ratios under BCR stimulation. Ocrelizumab treatment may create a self-reinforcing regulatory circuit: the reduction of Tph cells could alleviate suppression of regulatory B cells, which subsequently expand and further promote regulatory T cell networks via IL2RB, LGALS1, and an increased IL-10 signaling pathway.

immunology↗

bioRxiv2026

Programming Biomolecular Interactions with All-Atom Generative Model

Biomolecular interactions lie at the core of cellular life, spanning diverse molecular modalities from small molecules to nucleic acids and proteins. Nevertheless, design strategies remain separated despite shared physicochemical principles of molecular recognition. Here we present AnewOmni, a unified generative framework trained on more than 5 million biomolecular complexes, that enables transferable molecular design across molecular scales by assembling chemically meaningful building blocks at atomic resolution. We further introduce programmable graph prompts to support user-defined chemical, topological, and geometric steering during generation, exploring hybrid and unconventional chemistries beyond canonical structures. We demonstrate that transferable learning of interaction patterns and physical constraints across molecular modalities is possible, via an atom-to-block latent space capturing both atomic details and structural priors. The framework successfully designed small molecules, peptides, and nanobodies targeting the challenging KRAS G12D switch II pocket, as well as orthosteric peptides and allosteric small-molecule inhibitors for PCSK9 in the absence of known binding site, achieving 23%-75% success with only low-throughput validation, bypassing modality-specific high-throughput screening. AnewOmni is the first to succeed in functional molecular design across all scales, from small chemical entities to large biologics, and represents a stepstone towards general molecular reasoning engines, advocating a generative foundation model for biomolecular interactions to enter regimes where data and human intuition remain limited.

bioengineering↗

bioRxiv2026

Learning the All-Atom Equilibrium Distribution of Biomolecular Interactions at Scale

Biomolecular functions are governed by dynamic conformational ensembles rather than static structures. While models like AlphaFold have revolutionized static structure prediction, accurately capturing the equilibrium distribution of all-atom biomolecular interactions remains a significant challenge due to the high computational cost of molecular dynamics (MD). We present AnewSampling, a transferable generative foundation framework designed for the high-fidelity sampling of all-atom equilibrium distributions, which is the first model to faithfully reproduce MD at the all-atom level. It uses a quotient-space generative framework to ensure mathematical consistency and leverages the largest self-curated database of protein-ligand trajectories to date, with over 15 million conformations. Statistically, AnewSampling consistently outperforms all prior generative methods on the ATLAS monomer benchmark, and the all-atom capabilities of AnewSampling enable close statistical alignment with ground-truth MD for evaluating atomic biomolecular interactions in protein-ligand dynamics. Furthermore, AnewSampling successfully recovers coupled ligand and side-chain motions in CDK2 systems, overcoming a major sampling hurdle inherent to conventional MD. AnewSampling enables rapid exploration of conformational landscapes prior to intensive simulations, elucidating fundamental biophysical mechanisms and accelerating the broader design of functional biomolecules.

bioinformatics↗

bioRxiv2026

Instantaneous Beta Frequency Regulates Self-Generated Timing in Humans

Self-generated timing requires the brain to determine when to terminate an ongoing action without reference to external cues. Although beta-band oscillations have been implicated in temporal control, how their intrinsic dynamics shape moment-to-moment variability in internally generated time remains unclear. Here, combining scalp EEG with intracranial recordings in humans, we show that the instantaneous frequency of beta oscillations (13-30 Hz) provides a dynamic control signal for self-generated timing. During a self-paced time production task, higher instantaneous beta frequency reliably predicted longer produced durations, both within individuals on a trial-by-trial basis and across individuals. This relationship was beta-specific, frontoparietally distributed, robust to motor and spectral confounds, and validated by convergent intracranial recordings. These findings run counter to classical pacemaker-accumulator models and instead establish beta-frequency dynamics as a mechanism that delays action termination by increasing the density of intrinsic stabilizing control updates during self-generated timing.

neuroscience↗

bioRxiv2026

Diffusion-ACP39: A Decoder-Adaptive Latent Diffusion Framework for Generative Anticancer Peptide Discovery

Cancer remains a major global health threat, with its incidence and mortality rates consistently rising in recent years. Anticancer peptides (ACPs) are short amino acid chains that can inhibit the growth or spread of cancer cells. Compared to traditional treatments, ACPs are a promising class of potential cancer therapies due to their multiple mechanisms, potential for combination cancer therapy, enhanced immune function, lower toxicity to normal tissues, fewer side effects, and less drug resistance. Although it is necessary to explore novel ACPs, traditional wet-lab methods for selecting them are labor-intensive, time-consuming, and expensive. To accelerate the discovery of novel ACPs, we proposed Diffusion-ACP39, a latent diffusion-based generative model with synchronized seed autoencoder for anticancer peptide design, capable of generating novel peptides with lengths ranging from 5 to 39 amino acids. Furthermore, we developed RF-ACP39, a random forest classifier model to assess the generative power of Diffusion-ACP39. Finally, Diffusion-ACP39 achieved an accuracy of 94.5% when generating 10,000 peptides with RF-ACP39. We also qualitatively analyzed the differences among true ACPs, random sequences, random peptides, and generated ACPs, demonstrating that the generated ACPs are most similar to true ACPs.

bioinformatics↗

bioRxiv2026

Wiz regulates clustered protocadherin genes by restricting CTCF/cohesin loop extrusion in a genomic-distance biased manner

Zinc finger (ZF) proteins (ZFPs) constitute the largest family of transcription factors in mammals; however, their regulatory mechanism remains largely elusive. Here we propose COP (C2H2-ZFP occupancy predictor), a deep learning framework that integrates DNA sequence information with protein primary and secondary features to systematically predict ZFP genomic enrichments. Applying COP to the mouse clustered protocadherin (cPcdh) gene locus, we identified dozens of C2H2-ZFPs potentially involved in CTCF-mediated gene regulation with Wiz having the highest number of 12 ZFs. We confirmed Wiz enrichments at all of the CBS (CTCF-binding site) elements across the Pcdh clusters by Myc-tagging the endogenous Wiz gene. Genetic experiments revealed significant increases of expression levels of the cPcdh genes upon Wiz deletion in both neuronal cells in vitro and in mouse brain in vivo. Finally, integrated ChIP-seq, RNA-seq, and 4C-seq analyses demonstrated that Wiz regulates CTCF/cohesin occupancy and long-range enhancer-promoter contacts in a genomic-distance biased manner. Together, these findings have interesting implications regarding molecular mechanism of C2H2-ZFPs in cPcdh gene regulation.

cell biology↗

bioRxiv2026

ALKBH5 Promotes Hepatic Gluconeogenesis via m6A-mediated Stabilization of Ogt mRNA

Excessive hepatic glucose production is a hallmark of fasting hyperglycemia in type 2 diabetes mellitus, yet the epitranscriptomic mechanisms that sustain this dysregulation remain incompletely understood. Here, we show that dexamethasone/forskolin-induced gluconeogenic activation triggers widespread remodeling of RNA m6A methylation in mouse primary hepatocytes. Among the m6A regulators examined, the demethylase ALKBH5 is robustly induced both in vitro and in fasted livers. This induction is mediated by the glucocorticoid receptor, which binds to the Alkbh5 promoter and activates its transcription. Functionally, ALKBH5 overexpression enhances gluconeogenic gene expression and glucose production, whereas both global and hepatocyte-specific Alkbh5 knockout mice exhibit suppressed hepatic gluconeogenesis. Mechanistically, we identify Ogt as a primary downstream target, defining an ALKBH5-OGT epitranscriptomic axis in hepatocytes. ALKBH5 demethylates and stabilizes Ogt mRNA, thereby elevating OGT expression and promoting gluconeogenic gene expression. Consistently, the pro-gluconeogenic effects of ALKBH5 are largely abrogated by OGT knockdown. Finally, pharmacological inhibition of ALKBH5 with 18l effectively improves glucose tolerance and suppresses hepatic gluconeogenesis in both wild-type and db/db mice. Together, these findings reveal a glucocorticoid-responsive epitranscriptomic mechanism whereby ALKBH5 stabilizes Ogt mRNA to drive gluconeogenesis and highlight ALKBH5 as a promising therapeutic target for type 2 diabetes mellitus.

molecular biology↗

bioRxiv2026

A novel proliferative candidate genes panel for idiopathic pulmonary fibrosis: insights from integrated bulk and single-cell RNA sequencing

BackgroundIdiopathic pulmonary fibrosis (IPF) remains a fatal interstitial lung disease with limited diagnostic specificity and therapeutic options. This study integrates bulk and single-cell RNA sequencing (RNA-seq) to identify novel biomarkers and elucidate molecular mechanisms underlying IPF pathogenesis. MethodsWe prospectively enrolled 14 treatment-naive IPF patients and 6 controls. Bulk RNA-seq was performed on bronchoalveolar lavage fluid (BALF), while single-cell RNA-seq analyzed lung tissues from 4 IPF patients and 3 controls. Differentially expressed genes (DEGs) were identified (|log2FC| >1, FDR <0.05), followed by functional enrichment, protein-protein interaction (PPI) network analysis, and cell-type-specific expression profiling. Results1. DEG Identification: Bulk RNA-seq revealed 108 DEGs (24 upregulated, 84 downregulated). KEGG enrichment analysis of DEGs revealed that upregulated genes were mainly enriched in inflammation and immune pathways (such as NF-{kappa}B signaling pathway, Fc epsilon RI signaling pathway, B cell receptor signaling pathway, phagosome, Fc gamma R-mediated phagocytosis), pyrimidine metabolism, cell cycle, and PI3K-Akt signaling pathway. 2. PPI Network: Module analysis identified a proliferative gene module 1 (NUF2, CEP55, ANLN, TTK, TK1, MYBL2, CCNA2, RRM2, CDT1) linked to cell division and cycle regulation. 3. Single-Cell Insights: scRNA-seq of 30,477 cells delineated 11 populations. Module 1 genes exhibited predominant expression in proliferating cells, Module 1 signature score of proliferating cells was significantly higher in IPF than in control group. 4. Pathogenic Links: Key genes (e.g., CEP55, TTK) were associated with PI3K/AKT signaling, epithelial-mesenchymal transition (EMT), and anti-apoptotic pathways, mirroring oncogenic mechanisms. ConclusionThis multi-omics approach uncovers a proliferation-centric gene module in IPF, revealing shared molecular pathways with tumorigenesis. Our findings highlight novel diagnostic biomarkers and suggest repurposing cell cycle inhibitors as potential therapies. Future studies should validate these targets in preclinical models to advance precision medicine for IPF.

genetics↗

bioRxiv2026

STING safeguards epithelial genome integrity and protects from carcinogenesis via mitotic checkpoint control

STING is canonically known for mediating interferon responses to cytosolic DNA, yet its cell-intrinsic role in genome maintenance beyond the immune context is unknown. Here we show that epithelial STING functions as a type I interferon-independent genome-integrity checkpoint. STING loss impairs homologous recombination repair, attenuates ATM-associated damage signaling, elevates CDK1 activity, and causes chromosomal instability revealed by single-cell Strand-seq, culminating in spontaneous intestinal adenocarcinoma. These defects arise before tumor formation and confer selective vulnerability to CDK inhibition in tumor organoids and human colorectal cancer cells. Our findings identify STING as a cell-autonomous guardian of epithelial genome stability that restrains chromosomal instability-driven tumor evolution beyond its canonical immune function.

cancer biology↗

bioRxiv2026

SLECA: a single-cell atlas of systemic lupus erythematosus enabling rare cell discovery using graph transformer

Systemic lupus erythematosus (SLE) is a highly heterogeneous autoimmune disease with complex immune and molecular dysregulation. While rare immune cell populations are increasingly recognized as critical drivers of disease pathogenesis and progression, the lack of sufficiently powered, comprehensive single-cell atlases has limited their systematic identification and characterization. To address this gap, we present SLECA, the first large-scale single-cell atlas of SLE, enabled by a novel graph-transformer framework for the interpretable discovery and analysis of disease-relevant rare cell populations. SLECA integrates 366 single-cell samples with standardized clinical and biological metadata, providing a comprehensive and analytically unified atlas of systemic lupus erythematosus. By enabling scalable integration and interpretable analysis, SLECA resolves 54 distinct cell types, including rare populations with critical disease relevance. Notably, we identify double-negative T cells (DNTs) as a disease-expanded population whose abundance correlates with clinical severity. Through in silico perturbation, we demonstrate that key transcription factors, specifically JUN and EGR1, can reprogram DNT cells toward conventional T-cell phenotypes, highlighting actionable regulatory vulnerabilities in SLE.

bioinformatics↗

bioRxiv2026

TREM2 agonist antibody rebuilds the resident synovial macrophage lining barrier in rheumatoid arthritis

Rheumatoid arthritis (RA) can be viewed as a disease of barrier failure, in which CX3CR1+/TREM2+ synovial tissue-resident macrophages that form the lining barrier over cartilage and bone become fragmented and disorganized. However, how to therapeutically rebuild this barrier--and how macrophage states transition during repair--remain unclear. We engineered TR-Ab19, a mouse-selective agonistic antibody against TREM2, as a precision tool to initiate and interrogate barrier repair in vivo. TR-Ab19 engages TREM2-linked downstream signaling and redirects synovial macrophages from Clec4d+ inflammatory/proliferative programs toward TREM2+CX3CR1+Aqp1+ barrier-like states, thereby rebuilding the lining barrier. Across collagen-induced arthritis (CIA) and serum-transfer arthritis (STA) models, TR-Ab19 reduces synovitis, preserves cartilage and bone microarchitecture, limits osteoclastogenesis, and attenuates systemic cytokines and B-cell abnormalities. Single-cell RNA-seq with trajectory and cell-cell communication analyses reveal a TREM2-dependent shift toward a barrier-dominant macrophage ecosystem. Together, these findings establish antibody-mediated reprogramming of resident synovial macrophages as a barrier-centered strategy for RA and provide a framework for instructing macrophage niches in chronic inflammation.

bioengineering↗

bioRxiv2026

MetaTree: an interactive web platform for hierarchical data visualization and multi-group comparison

BackgroundHierarchical quantitative profiles are widely used in microbiome studies and other domains. However, comparing multiple samples and experimental groups while preserving hierarchical structure remains challenging. Many existing workflows require extensive manual figure assembly or do not support aligned comparisons across conditions on a shared hierarchy. ResultsWe developed MetaTree, an open-source platform that runs in a web browser for interactive visualization and comparative analysis of hierarchical quantitative data. MetaTree anchors samples, groups, and contrasts between groups to a shared reference hierarchy, preserving one-to-one node correspondence so that the same clade is compared in the same position across views. In addition to visualization, MetaTree integrates statistical testing for comparisons between two groups with false discovery rate (FDR) control, enabling users to identify clades with consistent differences between conditions and interpret them in hierarchical context. MetaTree also provides user configurable controls for visual encoding, filtering thresholds, label density, and layout, allowing figures to be adapted to different datasets and reporting needs. The interface remains usable for large hierarchies through interactive navigation, adaptive label handling, and branch collapsing. ConclusionsMetaTree is an installation-free web platform (https://byemaxx.github.io/MetaTree) for topology-consistent visualization and comparison of hierarchical profiles, supporting coordinated multi-panel exploration and automated comparison matrices to enable rapid generation of publication-ready figures for microbiome and other hierarchical datasets.

bioinformatics↗

bioRxiv2026

A mediterranean-mimicking diet harnesses gut microbiota-derived 3-IAA to rejuvenate T cell

Diet profoundly shapes the tumor microenvironment through metabolite modulation, yet the mechanisms linking diet, gut microbiota, and antitumor immunity remain unclear. Here, we demonstrate that a mediterranean-mimicking diet (MedDiet) suppresses tumor growth by orchestrating a microbiota-metabolite-immune axis. MedDiet selectively enriched Bacteroides thetaiotaomicron (B. thetaiotaomicron) in the gut, whose administration recapitulated tumor suppression. Both MedDiet and B. thetaiotaomicron elevated plasma levels of the tryptophan metabolite indole-3-acetic acid (3-IAA). Mechanistically, 3-IAA enhanced CD8+ T cell cytotoxicity and alleviated exhaustion by inhibiting the integrated stress response, sustaining antitumor immunity in vivo. Importantly, 3-IAA synergized with anti-PD-1 therapy to further restrain tumor progression. Finally, we developed a MedDiet Score to predict patient survival and response to immunotherapy. These findings reveal that dietary modulation of gut microbiota can reshape systemic metabolites to potentiate T cell-mediated antitumor responses. Our study identifies 3-IAA as a metabolite-based adjuvant for immune checkpoint therapy and highlights the translational potential of diet-driven immunomodulation in cancer treatment.

cancer biology↗