Biology subjects

Du, G.

Publications and source records attributed to Du, G..

At least 19 recordsLinked to original sources

bioRxiv2026

Autonomic and neurotrophic signaling in alcohol-associated liver disease

Alcohol-associated liver disease (ALD) is a leading cause of liver-related morbidity and mortality, yet its pathogenesis remains incompletely understood. While neural regulation is critical for hepatic homeostasis, how autonomic and neurotrophic signaling is regulated in the liver during ALD progression remains largely unexplored. Here, we performed an integrative analysis of bulk and single-cell transcriptomic datasets of human livers across the spectrum of ALD. We identified stage-dependent remodeling and coordinated expression patterns of autonomic and neurotrophic genes. Autonomic signaling exhibited a consistent decline, with reduced expression of sympathetic (DBH, TH) and the parasympathetic (ChAT) markers. In contrast, pan-neuronal and neuroplasticity-associated genes (UCHL1, TUBB3, STMN2) were upregulated, indicating active neural remodeling. In parallel, factors such as MANF, GDNF, and NRG2, with the receptor NTRK1, were upregulated and positively correlated with clinical indicators of disease severity, inflammation and fibrosis. In contrast, trophic factors including NTF3 and CDNF, and receptors such as GFRA1 and GFRA3, were downregulated and inversely associated with these parameters. Single-cell analyses revealed that neurotrophic factors are predominantly expressed by hepatic stellate cells, supporting their role as resident neuroglia-like cells in the liver. In another independent cohort, we observed a coordinated downregulation of NTF3 and sympathetic nerve markers in alcohol-associated hepatitis (AH), suggesting impaired NTF3-mediated neurotrophic support may contribute to sympathetic dysfunction in AH. Collectively, these findings uncover previously unrecognized neuro-regulatory dimensions in ALD and suggest that dysregulated autonomic and neurotrophic signaling may contribute to disease progression. Conflict of interestThe authors have declared that no conflict of interest exists.

bioinformatics↗

bioRxiv2026

NACraft: Programmatic nucleic-acid aptamer design via all-atom structure-model feedback

Protein-nucleic-acid interactions underpin diverse biological processes and provide a basis for molecular sensing, regulation and therapeutic intervention. However, the coupled dependence of aptamer function on nucleotide sequence, three-dimensional folding and target binding makes rational RNA and DNA binder design challenging. Here we present NACraft, a training-free and programmatic framework for all-atom nucleic-acid aptamer design based on backpropagation through structure-model feedback. By composing binding, sequence-similarity and anti-binding constraints, NACraft supports de novo generation, similarity-guided sampling and target-selective design within a unified optimization framework, without task-specific training or fine-tuning. Computational experiments showed that NACraft generated high-confidence candidates de novo across diverse protein targets, with further improvements achieved through similarity-guided design for both RNA and DNA complexes. Its target-selective design capability was further validated in silico, with 69.44% of paired candidates generated to favour the positive target EGFR over the off-target HER2. Under matched independent AlphaFold3 evaluation, NACraft achieved better performance than ODesign in 10 of 11 NA-12 targets and 17 of 20 protein target-length settings. Together, these results demonstrate the effectiveness and versatility of NACraft and extend structure-model hallucination toward programmatic nucleic-acid aptamer design. Codehttps://github.com/OTEAM-AI4S/NACraft

bioinformatics↗

bioRxiv2026

Levodopa increases substantia nigra iron: implications for Parkinson's disease

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSExcessive or unregulated iron in the brain can lead to toxicity via ferroptosis and related mechanisms. Iron accumulation in the substantia nigra (SN) occurs with Parkinsons disease (PD) progression and has been hypothesized to be an etiological mechanism. ObjectiveBased on emerging clinical observations, we tested the hypothesis that iron accumulation in the SN is a consequence of levodopa administration and is treatment-related rather than an intrinsic etiological mechanism. MethodsWe used both unilaterally lesioned 6-OHDA and unlesioned rats. We administered levodopa to rats at doses that were allometrically calculated to be similar to those used in mid-stages of PD. Iron-sensitive MRI (R2*) was used to quantify iron in the brain. Both group and intra-subject analyses were done using paired t-tests and linear mixed models. ResultsExperiment 1 used the unilateral 6-OHDA model to take advantage of the almost complete lack of dopamine neurons on the lesioned side. This permitted testing if levodopa-induced iron accumulation occurred in and/or depended on dopamine neurons. Fifteen days of levodopa treatment caused a marked increase in Fe in both the lesioned (p = 0.042) and unlesioned sides (p = 0.005), showing that iron accumulation does not depend on the presence of dopamine neurons. Based on these data, in experiment 2 unlesioned rats were administered levodopa daily for four months, and iron (R2*) values were assessed at baseline, 1, 2, and 4 months. In these normal rats, the levodopa-treated group had significantly increased Fe (R2*) in the substantia nigra compared to the vehicle group (p = 0.013). Interestingly, these effects were limited to the striatum, with no increases seen in the striatum, ventral tegmental area, or frontal cortex ConclusionLevodopa triggers processes that increase iron deposition in the substantia nigra, but this process may not depend on dopamine neurons. The underlying mechanisms and the effect on PD progression are important to elucidate and may transform how we understand PD and related neurodegenerative disorders

neuroscience↗

bioRxiv2026

SpatialClaw: A Memory-Augmented Autonomous Ecosystem for Spatial Omics Analysis

While the expansion of spatial omics has revolutionized our ability to dissect tissue architecture, the accumulation of incompatible computational methods has heavily fragmented end-to-end analysis, rendering complex workflows irreproducible. Generic conversational agents lack the domain-specific precision necessary to navigate the intricate biological pipelines. To overcome this, we present SpatialClaw, a memory-augmented autonomous ecosystem to unify spatial omics analysis under a single natural-language interaction. SpatialClaw integrates 30 specialized skills, spanning raw data preprocessing, spatial domain identification, deconvolution, spatially variable gene detection, cell-cell communication analysis, multi-sample and cross-modality integration. Distinct from existing agents, SpatialClaw introduces a graph-based persistent memory architecture that stores dataset metadata, analysis lineage, biological insights, and user preferences as versioned nodes and edges across three hierarchical layers (Session, Episodic, and Semantic), governed by a deterministic promotion policy. A Memory-Augmented Reasoning (MAR) Operator bridges the memory store and the main agent, synthesizing retrieved experiences into task-specific guidance for each query. In rigorous benchmarking spanning three memory-sensitive scenarios across 10 spatialomics skills, SpatialClaw outperforms both a standard large language model and the memory-only configuration. Furthermore, we demonstrate its robust biological utility by dissecting the complex tumor microenvironment of a 15-section human triple-negative breast cancer cohort. In merely three conversational turns and with zero direct scripting, SpatialClaw executes a comprehensive end-to-end workflow, yielding standardized output bundles. Ultimately, by synergizing comprehensive analytical tools with structured persistent memory, SpatialClaw elevates spatial omics from disjointed computational stitching to a fully traceable, reproducible, and self-improving discovery ecosystem. SpatialClaw is ready to use at https://github.com/ShangBioLab/SpatialClaw.

bioinformatics↗

bioRxiv2026

Cell Type-informed Characterization of Spatial Niches from Spatial Multimodal and Multi-omics Data

Cell niches play critical roles in tissue organization and orchestrate homeostasis, development, and disease progression. Advances in spatial omics technologies now allow diverse molecular and image-derived data to be jointly captured while preserving spatial context, but deciphering cell niches from such spatial multimodal and multi-omics data remains challenging. Existing computational methods are still limited in their flexibility across variable combinations of spatial modalities and omics data. Here we introduce SpaNECT, a unified and flexible framework designed to accommodate spatial multimodal and multi-omics data for cell niche characterization. SpaNECT further incorporates reference-informed cell-type information to support biologically interpretable niche analysis. Systematic evaluations across diverse tissues, disease conditions, and developmental stages showed that SpaNECT consistently outperformed representative methods in resolving cell niches. In mouse brain spatial multi-omics data, SpaNECT uncovered niche-associated molecular and regulatory programs; in developing chick heart, it tracked cross-stage niche reorganization and progressive remodeling of ventricular-associated cell states during maturation. Overall, SpaNECT establishes a general and robust framework for characterizing cell niches across spatial multimodal and multi-omics data.

bioinformatics↗

bioRxiv2026

Pathway-informed Universal Domain Adaptation for Single-cell RNA-seq Data

The rapid accumulation of single-cell atlases has yielded datasets of unprecedented scale, encompassing samples across diverse platforms, locations and laboratories. This multidimensional complexity drives an urgent need for universal domain adaptation methods capable of achieving precise cell-type annotation. However, existing methods lack computational scalability and fail to integrate biological priors. Here, we develop scPathOT, a pathway-informed universal domain adaptation framework that leverages pathway activation transformations to harmonize single-cell datasets across disparate conditions. We demonstrate the versatility of scPathOT across diverse technological platforms, tissues, disease contexts, cellular senescence and treatment conditions. Crucially, this pathway-informed alignment not only accurately resolves cellular identities but also uncovers functional mechanism. In pancreatic islets, scPathOT delineates a shared stress-repair axis traversed by {beta}-cells prior to their divergence into type 1 and type 2 diabetes-specific states. In aging bone marrow, scPathOT disentangles lineage-specific senescence modules that unexpectedly converge onto unified inflammatory and oxidative-stress programs. Furthermore, application to an in-house pancreatic ductal adenocarcinoma cohort uncovers the mechanistic basis underlying the neoadjuvant chemotherapy-induced reorganization of stromal-immune crosstalk. By coupling biological priors with universal domain adaptation, scPathOT provides a scalable, mechanistically interpretable framework to accelerate biological discovery from atlas-level single-cell data.

bioinformatics↗

bioRxiv2026

Rheb membrane orientation dynamics and functional consequences elucidated by molecular simulations, single-molecule-FRET and signaling assays

Membrane binding and dynamics of prenylated Ras family small GTPases are being increasingly recognized as key regulators of their cellular signaling. However, previous studies have focused primarily on the plasma membrane-associated Ras proteins. The Ras homolog enriched in brain (Rheb) is predominantly found in endo-membranes, such as the endoplasmic reticulum and lysosomes where it directly activates mTORC1 to control cell growth and proliferation. Little is known about the molecular details and functional consequences of Rheb membrane engagement, interaction with lipids, and orientational dynamics. In this work, we combined molecular dynamics simulations, hidden Markov modeling and single-molecule FRET measurements to map the modes of membrane binding and orientation landscape of Rheb. We also assessed the functional consequence of these processes on mTORC1 activation through simulation-guided mutagenesis and cell signaling assays. We found that Rheb interconverts between two dominant orientation states through two intermediates, and that mutationally destabilizing some of these orientation states modulates mTORC1 activity. Together, these results establish membrane orientation dynamics as a functionally relevant phenomenon in the endo-membrane localized Rheb and suggest a broader role of orientational dynamics for the regulation of functional in lipid-modified small GTPases.

biophysics↗

bioRxiv2026

KRT17 stabilizes EPN1 via inhibiting SMURF1-mediated ubiquitination to modulate Wnt/β-catenin signaling output and stem-like traits in ovarian cancer

BackgroundOvarian cancer (OC) progression and chemoresistance are closely linked to dysregulated oncogenic signaling, including Wnt/{beta}-catenin pathways that contribute to cancer stem-like traits. However, the upstream mechanisms connecting cytoskeletal regulation to Wn/{beta}-catenin signaling in OC remain incompletely understood. Keratin 17 (KRT17), a type I intermediate filament protein, has been implicated in tumor progression, but its mechanistic role in OC requires clarification. MethodsGene Expression Omnibus (GEO) datasets and clinical specimens were analyzed to assess KRT17 and EPN1 expression and prognostic significance. Functional assays, xenograft models, co-immunoprecipitation, ubiquitination analyses, and rescue experiments with wild-type and ubiquitination-resistant EPN1 mutants were performed to investigate molecular mechanisms. ResultsKRT17 expression was elevated in OC tissues and correlated with poor patient survival. KRT17 depletion suppressed proliferation, migration, stem-like properties, tumor growth, and cisplatin resistance. Mechanistically, KRT17 interacted with EPN1 and weakened its association with the E3 ligase SMURF1, reducing SMURF1-mediated ubiquitination at lysine 107 and preventing proteasomal degradation. Stabilized EPN1 was associated with increased {beta}-catenin abundance and stemness-associated markers, and enhanced self-renewal capacity. ConclusionsThese findings identify a KRT17-EPN1 axis that links intermediate filament dynamics to ubiquitin-dependent regulation of EPN1 stability and Wnt/{beta}-catenin signaling outputs in ovarian cancer.

cancer biology↗

bioRxiv2026

A druggable redox switch on SHP1 controls macrophage inflammation

Immunological proteins are major disease targets, yet most remain undrugged. Post-translational redox modification of cysteine residues has emerged as an important mode of immune cell regulation, particularly in macrophage cytokine responses. Here, we develop a strategy for systematic discovery and small-molecule functionalization of redox-regulated cysteines on immunological proteins. Using deep redox proteomics, we annotate 788 in vivo redox-regulated cysteines across diverse immune-relevant protein domains. We demonstrate how these sites enable cysteine-directed pharmacology through discovery of a novel cysteine activation site on the immune regulator SHP1. Targeting Cys102, we develop a highly selective covalent agonist, SCA, which binds the N-SH2 domain to relieve autoinhibition and activate SHP1. In mouse and human macrophages, SCA selectively engages SHP1 Cys102, antagonizing IRAK signaling and LPS-induced pro-inflammatory cytokine production. Together, this work identifies a druggable cysteine redox switch controlling macrophage cytokine responses and provides a compendium of redox-regulated sites for therapeutic development.

biochemistry↗

bioRxiv2026

Orion Microbiome Database: mapping constellations in the microbiome universe

BackgroundPublicly available microbiome sequencing data have grown rapidly in scale and diversity, but secondary use remains limited by fragmentation across repositories, inconsistent annotations, heterogeneous processing pipelines, and incomplete metadata. These barriers complicate cross-study comparisons and restrict the reproducibility of large-scale microbiome research. ObjectiveThe Orion Microbiome Database was developed to overcome these challenges by providing a standardized, accessible, and reproducible resource for the integration and analysis of whole-genome shotgun (WGS) microbiome datasets. MethodsOrion aggregates raw metagenomic datasets from public repositories and harmonizes them into computed microbial profiles using automated and transparent analysis pipelines. Metadata are curated to ensure consistency and enable systematic cross-cohort comparisons. The platform provides interactive tools for data exploration, visualization, and downstream analysis, reducing redundancies in data preprocessing and lowering the barrier for both novice and expert users. Beyond research applications, Orion also serves as an educational resource, enabling microbiome data exploration in classroom settings. ConclusionBy unifying fragmented sequencing resources into a reproducible framework, Orion accelerates microbiome discovery, supports scalable cross-study investigations, and fosters integration of microbiome science into teaching and training. This database has the potential to serve as a model for future large-scale, community-driven platforms in microbiome research.

microbiology↗

bioRxiv2025

Organization of mouse prefrontal cortex subnetwork revealed by spatial single-cell multi-omic analysis of SPIDER-Seq

Deciphering the connectome, anatomy, transcriptome and spatial-omics integrated multi-modal brain atlas and the underlying organization principles remains a great challenge. We developed a Single-cell Projectome-transcriptome In situ Deciphering Sequencing (SPIDER-Seq) technique by combining viral barcoding tracing with single-cell sequencing and spatial-omics. This empowers us to delineate a integrated single-cell spatial molecular, cellular, anatomic and projectomic atlas of mouse prefrontal cortex (PFC). The projectomic and transcriptomic cell clusters display distinct modular organization principles, but are coordinately configured in the PFC. The projection neurons gradiently occupied different territories in the PFC aligning with their wiring patterns. Importantly, they show higher co-projection probability to the downstream nuclei with reciprocal circuit connections. Moreover, we integrated projectomic atlas with their distinct spectrum of neurotransmitter/neuropeptide and the receptors-related gene profiles and depicted PFC neural signal transmission network. By which, we uncovered potential mechanisms underlying the complexity and specificity of neural transmission. Finally, leveraging machine learning, we predicted neuron projections with high accuracy by combining gene profiles and spatial information. As a proof of concept, we used this model to predict projections of fear recall engram neurons. This study facilitates our understanding of brain multi-modal network and neural computation.

neuroscience↗

bioRxiv2025

Peptide-based ligand antagonists block a Vibrio cholerae adhesin

Vibrio cholerae, the causative agent of cholera, uses surface proteins such as the repeats-in-toxin (RTX) adhesin FrhA to colonize hosts and initiate infection. Blocking bacterial adhesion represents a promising therapeutic strategy to treat infections without promoting drug resistance. FrhA contains a peptide-binding domain (PBD) that is key for hemagglutination, human epithelial cell binding, and V. cholerae biofilm formation. Previous studies identified a lead pentapeptide ligand with the sequence Ala-Gly-Tyr-Thr-Asp (AGYTD) that blocks V. cholerae colonization of the mouse small intestine at high micromolar concentrations. A structure-guided approach has now identified a minimal D-amino acid-containing tripeptide motif with higher affinity for the FrhA-PBD and predicted metabolic stability. Our results contribute to the development of anti-adhesion strategies to combat infections.

microbiology↗

bioRxiv2025

Spatial geometry-aware deep learning for deciphering tissue structure from spatially resolved transcriptomics

Recent advances in spatially resolved transcriptomics have enabled high-throughput gene expression profiling while preserving spatial context, thereby facilitating the investigation of spatial heterogeneity within tissues. Here, we present SpatialGEO, a spatial geometry-aware deep learning framework designed to decipher tissue organizational structures through dual-encoder feature extraction and geometric graph learning. Experimental results demonstrate the superior accuracy of SpatialGEO in identifying spatial regions across datasets encompassing diverse biological contexts and spatial resolutions. Moreover, SpatialGEO uncovers key mechanisms of immune evasion and regulation within the tumor microenvironment and provides novel biological insights into mouse embryonic development.

bioinformatics↗

bioRxiv2025

A Multimodal Graph Learning Framework for Versatile Spatial Transcriptomics Analysis with SpatialModal

The development of spatial transcriptomics(ST) technologies enables the exploration of tissue structure and cellular function within a spatial context. However, mainstream analytical methods primarily focus on the gene expression modality itself and struggle to fully leverage auxiliary information from histological images, which limits precise dissection of complex biological structures. To address this, we propose SpatialModal, a multimodal graph learning framework that effectively integrates gene expression data and histological images from spatial transcriptomics to construct unified spatial representations. SpatialModal effectively captures synergistic interactions between modalities by jointly modeling intra- and inter-modal complementary features while incorporating spatial adjacency information. Furthermore, it employs a dual contrastive learning strategy to enhance the discriminative power of representations, thereby enabling efficient and robust analysis of tissue structures. We validate the effectiveness of this approach on multiple public datasets covering diverse tissue types, species, and resolutions. Experiments demonstrate that SpatialModal exhibits significant advantages in downstream tasks, including spatial domain identification, gene expression reconstruction, and pseudotime inference, and accurately elucidates the hierarchical structure of the mouse cortex as well as the metabolic-immune dynamic boundaries in the breast cancer microenvironment. Additionally, SpatialModal shows exceptional robustness in single-cell resolution and multi-slice integration tasks, providing an efficient and broadly applicable analytical tool for spatial transcriptomics research.

bioinformatics↗

bioRxiv2024

Characterization of Autophagy Cargo Profiles during Starvation and Exercise with a Novel Autophagosome Isolation Method

Autophagy is a cellular process to clear unwanted and dysfunctional cellular cargoes, which are sequestered in autophagosomes before their delivery to lysosomes for degradation. Autophagy cargo selection, mediated by cargo receptors, varies across cell types and conditions. Understanding the cargo features is essential for elucidating autophagys function in specific physiological or pathological contexts. Here we present a simple and rapid method for isolating LC3B-positive autophagosomes from the tissues of GFP-LC3 transgenic mice, a widely used autophagy reporter model, without relying on the complex ultracentrifugation steps required by traditional methods. When combined with quantitative proteomics, this approach enables efficient in vivo characterization of autophagy cargoes. We applied this method to establish autophagy cargo profiles in skeletal muscle during starvation and exercise, two physiological conditions that activate autophagy, and identified distinct cargo selection patterns, with significantly higher levels of ER-phagy and ribophagy observed during starvation. We further revealed the ER-phagy receptors TEX264 and RETREG1/FAM134B as potential mediators of the elevated ER-phagy under starvation. In summary, we report an efficient workflow for in vivo autophagy cargo characterization and provide detailed analysis and comparison of cargo profiles under starvation and exercise conditions.

cell biology↗

bioRxiv2024

Discovery of Potent Degraders of the Dengue Virus Envelope Protein

Targeted protein degradation has been widely adopted as a new approach to eliminate both established and previously recalcitrant therapeutic targets. Here we report the development of small molecule degraders of the envelope (E) protein of dengue virus. We developed two classes of bivalent E-degraders, linking two previously reported E-binding small molecules, GNF-2 and CVM-2-12-2, to a glutarimide-based recruiter of the CRL4CRBN ligase to effect proteosome-mediated degradation of the E protein. ZXH-2-107 (based on GNF-2) is an E degrader with ABL inhibition while ZXH-8-004 (based on CVM-2-12-2) is a selective and potent E-degrader. These two compounds provide proof-of-concept that difficult-to-drug targets such as a viral envelope protein can be effectively eliminated using a bivalent degrader and provide starting points for the future development of a new class antiviral drugs.

biochemistry↗

bioRxiv2024

Circular RNA HMGCS1 sponges miR-4521 to aggravate type 2 diabetes-induced vascular endothelial dysfunction

Noncoding RNA plays a pivotal role as novel regulators of endothelial cell function. Type 2 diabetes, acknowledged as a primary contributor to cardiovascular diseases, plays a vital role in vascular endothelial cell dysfunction due to induced abnormalities of glucolipid metabolism and oxidative stress. In this study, aberrant expression levels of circHMGCS1 and MIR4521 were observed in diabetes-induced human umbilical vein endothelial cell dysfunction. Persistent inhibition of MIR4521 accelerated development and exacerbated vascular endothelial dysfunction in diabetic mice. Mechanistically, circHMGCS1 upregulated arginase 1 by sponging MIR4521, leading to decrease in vascular nitric oxide secretion and inhibition of endothelial nitric oxide synthase activity, and an increase in the expression of adhesion molecules and generation of cellular reactive oxygen species, reduced vasodilation and accelerated the impairment of vascular endothelial function. Collectively, these findings illuminate the physiological role and interacting mechanisms of circHMGCS1 and MIR4521 in diabetes-induced cardiovascular diseases, suggesting that modulating the expression of circHMGCS1 and MIR4521 could serve as a potential strategy to prevent diabetes-associated cardiovascular diseases. Furthermore, our findings provide a novel technical avenue for unraveling ncRNAs regulatory roles of ncRNAs in diabetes and its associated complications.

cell biology↗

bioRxiv2023

uniLIVER: a Human Liver Cell Atlas for Data-Driven Cellular State Mapping

The liver performs several vital functions such as metabolism, toxin removal and glucose storage through the coordination of various cell types. The cell type compositions and cellular states undergo significant changes in abnormal conditions such as fatty liver, cirrhosis and liver cancer. As the recent breakthrough of the single-cell/single-nucleus RNA-seq (sc/snRNA-seq) techniques, there is a great opportunity to establish a reference cell map of liver at single cell resolution with transcriptome-wise features. In this study, we build a unified liver cell atlas uniLIVER by integrative analyzing a large-scale sc/snRNA-seq data collection of normal human liver with 331,125 cells and 79 samples from 6 datasets. Besides the hierarchical cell type annotations, uniLIVER also proposed a novel data-driven strategy to map any query dataset to the normal reference map by developing a machine learning based framework named LiverCT. Applying LiverCT on the datasets from multiple abnormal conditions (1,867,641 cells and 439 samples from 12 datasets), the alterations of cell type compositions and cellular states were systematically investigated in liver cancer.

bioinformatics↗