Biology subjects

Wang, C.

Publications and source records attributed to Wang, C..

At least 343 records · Page 19Linked to original sources

bioRxiv2025

RBMX2: A Pivotal Regulator Linking Mycobacterium bovis Infection to Epithelial-Mesenchymal Transition and Lung Cancer Progression

Tuberculosis (TB) is a complex disease that arises from the dynamic interplay among the pathogen, host, and environmental factors. In 2022, TB affected approximately 10.6 million people worldwide, resulting in 1.3 million deaths. In regions with a high burden of zoonotic TB, Mycobacterium bovis (M. bovis) accounts for nearly 10% of human TB cases. The immune evasion strategies and latent nature of Mycobacterium tuberculosis (M. tb) pose significant challenges to understanding the host immune response to TB infection. In this study, we identify RNA-binding motif protein X-linked 2 (RBMX2) as a novel host factor that may facilitate M. bovis infection. However, the molecular mechanisms and functional roles of RBMX2 during M. bovis infection remain poorly understood. Our findings show that RBMX2 is significantly upregulated across multiple cell types following infection, including embryonic bovine lung (EBL) cells, bovine macrophage (BoMac) cells, bovine pulmonary alveolar primary cells, and human alveolar epithelial cells (A549). Using a combination of global transcriptomic sequencing, proteomic profiling, cell adhesion assays, ChIP-PCR, and Western blotting, we demonstrate that RBMX2 suppresses cell adhesion and tight junction formation in EBL cells while promoting M. bovis adhesion and invasion through activation of the p65 signaling pathway. Importantly, integrated analyses of transcriptomic, proteomic, and metabolomic data reveal that RBMX2 plays a key role in regulating epithelial-mesenchymal transition (EMT), a cellular process closely associated with cancer progression. To further explore the potential link between RBMX2 and cancer, we analyzed the TIMER2.0 database and found elevated RBMX2 expression in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) tissues compared to normal lung tissues. These findings were corroborated by immunofluorescence staining. Using an M. bovis-infected BoMac-induced EBL-EMT model, we show that RBMX2 promotes EMT through activation of the p65/MMP-9 pathway following infection. Additionally, we validated the EMT process in human lung epithelial cancer cells (H1299), further supporting the involvement of RBMX2 in this transition. Collectively, our study identifies RBMX2 as a novel host factor that may enhance M. bovis infection and drive infection-induced EMT. These findings provide new insights into the molecular mechanisms underlying TB pathogenesis and suggest that RBMX2 could serve as a potential target for the development of TB vaccines and therapeutic strategies against TB-associated lung cancer.

microbiology↗

bioRxiv2025

stPipe: A flexible and streamlined R/Bioconductor pipeline for preprocessing sequencing-based spatial transcriptomics data

Spatial transcriptomics technology has developed rapidly in recent years, with various sequencing-based platforms such as 10x Visium, Slide-seq and Stereo-seq becoming widely used by researchers. Each platform brings its own set of protocols and customised data analysis pipelines which presents challenges when the goal is to obtain uniformly preprocessed data that is conveniently formatted for downstream analysis. To address the need for simpler, open-source solutions that deal with sequencing-based spatial transcriptomics (sST) data from different platforms, we present stPipe, a comprehensive and modular pipeline for analysing sST data. stPipe handles various analysis steps including (i) data processing from raw paired end FASTQ files to create a spatially resolved gene count matrix; (ii) the collation of relevant quality control metrics during preprocessing to ensure unwanted artefacts can be filtered from further analysis; and (iii) the adoption of standardised data storage containers to allow results to be easily passed on to a wide range of downstream analysis packages tailored to different goals (such as clustering, cell-cell communication analysis and differential expression analysis). stPipe is implemented as an R/Bioconductor package that builds upon functionality in the scPipe software, and offers a flexible preprocessing pipeline that can manage data from all current main-stream sST plaforms. A key use case for stPipe is in methods benchmarking, and we demonstrate how the uniform processing of sST data collected on reference tissue samples from the cadasSTre and SpatialBenchVisium projects is made easier, allowing comparisons between different technology platforms and downstream analysis tools. Our framework thus aims to advance the standardization and optimization of spatial transcriptomics analyses, fostering collaboration and innovation within the research community.

bioinformatics↗

bioRxiv2025

Spatial Transcriptomics Using Archived Formalin-Fixed Paraffin-Embedded Core Needle Biopsy Tissues Revealed Unique Transcriptomic Signatures in Kidney Transplant Rejections

Clinical core needle biopsies present challenges for transcriptomic analysis due to limited tissue volume. In this study, we determined the feasibility of using spatial transcriptomics to evaluate rejection on formalin-fixed paraffin-embedded (FFPE) core needle biopsies from human kidney allografts. We demonstrated that non-rejection, active antibody mediated rejection (AMR), acute cell mediated rejection (TCMR) and chronic active AMR have distinct transcriptomic signatures. Subclusters of monocytes/macrophages with high Fc gamma receptor IIIA (FCGR3A) expression were identified in C4d-positive active AMR and acute TCMR, and the spatial distribution of these cells corresponded to the characteristic histopathological features. Key markers related to monocyte/macrophage activation and innate alloantigen recognition were upregulated, along with metabolic pathways associated with trained immunity in AMR and TCMR. The discovery of unique transcriptomic signatures associated with AMR and TCMR facilitates the differentiation of acute kidney allograft rejections using spatial transcriptomic data derived from FFPE core needle biopsies.

immunology↗

bioRxiv2025

Dissecting organoid-bacteria interaction highlights decreased contractile force as a key factor for heart infection

Bacterial endocarditis is a fatal cardiovascular disease exacerbated by weakened heart contraction, yet the direct impact of cardiac contractility on bacterial adhesion remains elusive. Here, we present a novel quantitative physics model integrating finite element analysis and live-cell imaging to uncover their strong correlation. Using this model, we quantified the real-time force magnitude generated by organoid-type cardiac microtissue derived from healthy donors and dilated cardiomyopathy patients - mimicking normal and suppressed heart contractility, respectively - to the approaching bacteria in a real fluidic system. The data revealed that weakened cardiac contractility facilitated bacterial invasion of the myocardium. Verifying this finding in a mouse transverse aortic constriction model demonstrated that increasing heart contraction efficiently mitigated bacterial invasion, with a 25% increase in heart contractility reducing endocarditis risk by 80%. Our findings demonstrate that patient-derived cardiac organoids provide a physiologically relevant platform for studying bacterial infections in vitro, offering high clinical fidelity. This platform establishes a valuable tool for drug screening and the development of novel therapeutic strategies.

bioengineering↗

bioRxiv2025

Exploring Population Differences in the Human Gut Microbiome: from Microbial Abundance to Single Nucleotide Polymorphisms

The human gut microbiota exhibits significant diversity across populations, influenced by factors such as geography, diet, and lifestyle, particularly between the Han Chinese and non-Chinese populations. While previous studies have predominantly focused on the taxonomic abundance of the gut microbiome, the impact of single nucleotide polymorphisms (SNPs) in driving population-specific differences remains largely underexplored. In this study, we systematically investigated gut microbial differences between the Han Chinese and non-Chinese populations using the Human Gut Microbiome Reference Genome Catalog (HGMRGC). HGMRGC includes 271,480 non-redundant genomes from 5,785 prokaryotic species, which was generated by metagenomic sequencing data from 9,320 publicly available samples across 22 countries and 3,584 newly sequenced samples from Han Chinese individuals across 29 provinces and regions in China. We observed geography was the primary driver of microbial variation of abundance and SNPs. We identified 625 novel population-specific genome clusters from HGMRGC with functional differences in carbohydrate utilization and 126 species exhibiting distinct prevalence related to vitamin biosynthesis, antibiotic resistance, and carbohydrate metabolism. Beta diversity analysis highlighted significant inter-population differences in both microbial abundance and SNPs, while alpha diversity analysis revealed that non-Chinese populations exhibited higher diversity in microbial abundance, and Han Chinese populations displayed greater diversity in SNPs. These results provide valuable insights into population-specific microbial diversity, laying the groundwork for future research on its functional and health implications.

microbiology↗

bioRxiv2025

Regulation of airway fumarate by host and pathogen promotes S. aureus pneumonia

Staphylococcus aureus is a leading cause of healthcare-associated pneumonia, contributing significantly to morbidity and mortality worldwide. As a ubiquitous colonizer of the upper respiratory tract, S. aureus must undergo substantial metabolic adaptation to achieve persistent infection in the distinctive microenvironment of the lung. We observed that fumC, which encodes the enzyme that converts fumarate to malate, is highly conserved with low mutation rates in S. aureus isolates from chronic lung infections. Fumarate, a pro-inflammatory metabolite produced by macrophages during infection, is regulated by the host fumarate hydratase (FH) to limit inflammation. Here, we demonstrate that fumarate, which accumulates in the chronically infected lung, is detrimental to S. aureus, blocking primary metabolic pathways such as glycolysis and oxidative phosphorylation (OXPHOS). This creates a metabolic bottleneck that drives staphylococcal FH (FumC) activity for airway adaptation. FumC not only degrades fumarate but also directs its utilization into critical pathways including the tricarboxylic acid (TCA) cycle, gluconeogenesis and hexosamine synthesis to maintain metabolic fitness and form a protective biofilm. Itaconate, another abundant immunometabolite in the infected airway enhances FumC activity, in synergy with fumarate. In a mouse model of pneumonia, a {Delta}fumC mutant displays significant attenuation compared to its parent and complemented strains, particularly in fumarate- and itaconate-replete conditions. Our findings underscore the pivotal role of immunometabolites in promoting S. aureus pulmonary adaptation.

microbiology↗

bioRxiv2025

SpatialAgent: An Autonomous AI Agent for Spatial Biology

Advances in AI are transforming scientific discovery, yet spatial biology, a field that deciphers the molecular organization within tissues, remains constrained by labor-intensive workflows. Here, we present SpatialAgent, an autonomous AI agent for spatial biology research. SpatialAgent couples large language models with a Plan-Act-Conclude architecture, dynamic tool and skill retrieval, multimodal interpretation, and verification modules that audit generated claims. It supports the full discovery loop, from gene-panel design and multimodal annotation to trajectory inference, cell-cell communication analysis, imputation, and hypothesis generation. Across human and mouse brain, heart, tonsil, colon, and prostate datasets, SpatialAgent outperformed established computational baselines and matched or surpassed expert scientists in key tasks. In open-ended case studies, it recovered known tissue organization and generated spatially grounded hypotheses. In a prospective mouse prostate cancer Xenium study, it designed a compact 100-gene add-on panel that profiled 4.2 million cells across 21 samples, improved cell-type and malignant-state prediction, and captured spatially structured tumor and microenvironment programs. SpatialAgent establishes a framework for autonomous and collaborative discovery in spatial biology.

bioinformatics↗

bioRxiv2025

BABA: Naturalistic fMRI and MEG recordings during viewing of a reality TV show

Prior neuroimaging datasets using naturalistic listening paradigms have predominantly focused on single-talker scenarios. While these studies have been invaluable for advancing our understanding of speech and language processing in the brain, they do not capture the complexities of real-world multi-talker environments. Here, we introduce the "Le Petit Prince (LPP) Multi-talker Dataset", a high-quality, naturalistic neuroimaging dataset featuring 40 minutes of electroencephalogram (EEG) and 7T functional magnetic resonance imaging (fMRI) recordings from 26 native Mandarin Chinese speakers as they listened to both single-talker and multi-talker speech streams. Validation analyses conducted on both EEG and fMRI data demonstrate the datasets high quality and robustness. Additionally, the dataset includes detailed transcriptions and prosodic and linguistic annotations of the speech stimuli, enabling fine-grained analyses of neural responses to specific linguistic and acoustic features. The LPP Multi-talker Dataset is well-suited for addressing a wide range of research questions in cognitive neuroscience, including selective attention, auditory stream segregation, and working memory processes in naturalistic listening contexts.

neuroscience↗

bioRxiv2025

Learning interpretable representation for context-specific transcription regulatory networks using a foundation model

Gene expression is shaped by transcription regulatory networks (TRNs), where transcription regulators interact within regulatory elements in a context-specific manner. Despite significant efforts, understanding the intricate interactions of transcription regulators across different genomic regions and cell types remains a major challenge, largely due to data sparsity. Here, we introduce ChromBERT, a foundation model pre-trained on large-scale human ChIP-seq datasets. ChromBERT effectively captures the interaction syntax of approximately one thousand transcription regulators across diverse genomic contexts, generating interpretable representations of context-specific TRNs and their constituent regulators. Fine-tuned for various transcription regulation tasks, ChromBERT demonstrates superior performance in imputing previously unseen cistromes and modeling regulatory effects and dynamics in specific cell types. Notably, ChromBERT adapts its TRN representations for cell-type-specific tasks, providing deep insights into the roles of transcription regulators underlying observed regulatory effects or dynamics without requiring cell-type-specific genomic data for each regulator. By overcoming the limitations of sparse transcription regulator data, ChromBERT significantly enhances our ability to model, interpret, and predict transcriptional regulation across diverse biological contexts.

bioinformatics↗

bioRxiv2025

Deciphering complex interactions between LTR retrotransposons and three Papaver species using LTR_Stream

Long terminal repeat retrotransposons (LTR-RTs), a major type of class I transposable elements (TEs), are the most abundant repeat element in plants. The study of the interactions between LTR-RTs and the host genome relies on high-resolution characterization of LTR-RTs. However, for non-model species, this remains a challenge. To address this, we developed LTR_Stream for sub-lineage clustering of LTR-RTs in specific or closely related species, providing higher precision than current database-based lineage-level clustering. Using LTR_Stream, we analyzed Retand LTR-RTs in three Papaver species. Our findings show that high-resolution clustering reveals complex interactions between LTR-RTs and the host genome. For instance, we found autonomous Retand elements could spread among the ancestors of different subgenomes, like retroviruses pandemics, enriching genetic diversity. Additionally, we identified that specific truncated fragments containing transcription factors motifs such as TCP and bZIP may contribute to generation of novel TAD-like boundaries. Notably, our pre- and post-allopolyploidization comparisons show that these effects diminished after allopolyploidization, suggesting that allopolyploidization may be one of the mechanisms by which Papaver species cope with LTR-RTs. We demonstrated the potential application of LTR_Stream and provided a reference case for studying the interactions between LTR-RTs and the host genome in non-model plant species.

bioinformatics↗

bioRxiv2025

DIFS: Discriminative Feature Selection for Cell Clustering based on Single-Cell RNA Sequencing Data

A key challenge in single-cell RNA sequencing (scRNA-seq) analysis is clustering cells based on their expression profiles. Effective clustering requires selecting the most informative gene features whose varying expression levels in different cell types can be used to discriminate between different cell types. This study introduces DIFS, a novel statistical framework designed to enhance discriminative feature selection for scRNA-seq-based cell clustering. DIFS operates in two stages. In the first stage, a modified dip test identifies genes with significant multimodal expression patterns, as these are likely to have different expression levels in different cell types. In the second stage, cells are clustered based on the selected features from stage one, and additional cluster-specific features are identified, capturing genes that may be expressed in only one cell cluster. Through real data analysis, we demonstrate that DIFS improves the accuracy of cell type classification and enhances the understanding of cellular heterogeneity in single-cell studies.

bioinformatics↗

bioRxiv2025

A Rapid and Highly Sensitive CRISPR Assay Utilizing Cas12a Orthologs for the Detection of Novel Duck Reovirus

The novel duck reovirus (NDRV) disease presents a significant threat to the poultry industry due to the absence of effective therapeutic measures. As a result, there is an urgent need to develop innovative rapid diagnostic methods for early virus detection. In this study, we developed a Rapid Visual CRISPR Assay to detect the NDRV S3 gene using novel Cas12a orthologs. Specifically, we compared the performance of two candidates, Gs12-16 and Gs12-18, in detecting the NDRV S3 gene to identify a highly sensitive and efficient CRISPR-based diagnostic method. Our results demonstrated that both Gs12-16 and Gs12-18 exhibited strong cis- and trans-cleavage activities for classical "TTTV" protospacer adjacent motif (PAM)-containing targets in vitro, although they required different reaction temperatures. Notably, Gs12-18 showed relatively higher activity for dsDNA targets compared to Gs12-16, indicating that Gs12-18 is more suitable for CRISPR-based nucleic acid detection applications. To leverage these properties, we integrated Gs12-18 with loop-mediated isothermal amplification (LAMP) technology to establish a LAMP-CRISPR/Gs12-18-mediated method for detecting the NDRV S3 gene. This approach enables highly sensitive and visually detectable on-site identification of the NDRV S3 gene, achieving a sensitivity of 38 copies per reaction. Our LAMP-CRISPR/Gs12-18-based method can be utilized for highly sensitive detection of NDRV nucleic acids.

synthetic biology↗

bioRxiv2025

Phase separation of Polycomb-like (PCL) proteins drive PRC2 complex condensates to regulate gene expression

Polycomb group (PcG) proteins function as two major multicomponent protein complexes: Polycomb repressive complex 1 (PRC1) and 2 (PRC2) to repress key developmental genes and maintain epigenetic memory of cell identity during development. Phase-separation has long been implicated in PRC1 mediated gene silencing, but it is still unclear whether PRC2 also utilize a similar mechanism to regulate gene expression. Here we report that Polycomb-like (PCL) proteins, PRC2 accessory factors, can phase-separate in vitro and generate dynamic puncta in vivo. Biochemical and cellular analyses reveal that PCL proteins (hereafter referred to as PCLs) have intramolecular interaction between N- and C-terminal domains, which make PCLs into more compact conformations. The intramolecular interaction not only controls the size of the PCLs phase separation droplets, but also affects the chromatin association of PRC2. Finally, we show that CpG islands, key DNA regulatory elements in mammalian promoters, disrupt the N-C intramolecular interaction of PCLs to expose their middle intrinsically disordered regions (IDRs), which in turn trigger PCLs driven PRC2 condensates formation. Together, this study provides a new perspective on the regulation of PRC2 by PCLs, implicating PCLs read CpG islands to fine-tune their oligomerization to overcome the threshold for PRC2 recruitment to corresponding genomic loci via multivalent interaction with CpG islands chromatin.

biochemistry↗

bioRxiv2025

Characterization of deep-sea viruses reveals their unexpected diversity and roles in facilitating host metabolism of complex organic matter

Viruses exert a pervasive influence on biogeochemical cycles in deep-sea ecosystems. By integrating multi-omics and viral isolation data from four distinct deep-sea sediment sites, we uncover the diversity of DNA and RNA viral communities, which vary significantly across geographically separated cold seep and seamount environments. Host range predictions reveal both prokaryotic and eukaryotic communities are susceptible to viral infection. Metatranscriptomic analyses suggest viral functional genes are actively expressed, potentially enhancing host metabolism of complex organic matter (COM). Indeed, purified viruses from deep-sea sediments were shown to facilitate COM metabolism in certain deep-sea microorganisms, enabling the isolation of previously uncultured bacterial taxa. Notably, in these newly isolated bacteria, polysaccharides induced chronic virus release without cell lysis, a process that may assist host metabolism of polysaccharides. Our findings illuminate the diversity and roles of viral communities in distinct deep-sea sediments, underscoring their significant contribution to COM degradation and the deep-sea carbon cycle.

microbiology↗

bioRxiv2025

DeorphaNN: Virtual screening of GPCR peptide agonists using AlphaFold-predicted active state complexes and deep learning embeddings

Peptide-activated G protein-coupled receptors (GPCRs) regulate critical physiological processes such as metabolism, neural signalling, and endocrine function through their interaction with neuropeptides and peptide hormones. Despite their importance, identifying endogenous peptide agonists for GPCRs remains challenging, particularly for orphan receptors without known ligands. Recent advances in deep learning-based protein structure prediction, exemplified by AlphaFold (AF), have shown application beyond structural modelling, including protein-protein interaction prediction. Given that GPCR-peptide agonist interactions represent a specialized form of protein-protein interaction, we leveraged a dataset of experimentally validated agonist and non-agonist GPCR-peptide interactions from Caenorhabditis elegans to evaluate AF-Multimers ability to distinguish agonist-bound complexes. When modelling GPCR-peptide complexes, AF-Multimer confidence metrics partially discriminate agonist from non-agonist interactions, with improved discrimination achieved by utilizing AF-Multistate-derived active-state templates. We further investigated whether embeddings from the hidden layer of AF-Multimers neural network could distinguish agonist from non-agonist complexes. Feature performance analysis reveals that AF- Multimers pair representations outperform single representations, with distinct subregions of the pair representation providing complementary predictive signals. Building on these insights, we developed DeorphaNN, a graph neural network integrating active-state GPCR-peptide structural predictions, interatomic interactions, and deep learning embeddings to prioritize GPCR-peptide agonist interactions. DeorphaNN generalizes across datasets derived from different species, including annelids and humans, and successfully uncovered peptide agonists for two orphan GPCRs. DeorphaNN offers a novel computational resource to accelerate deorphanization by prioritizing GPCR-peptide agonist candidates for AI-guided experimental validation.

bioinformatics↗

bioRxiv2025

Quantile-specific confounding: correction for subtle population stratification via quantile regression

Subtle population structure remains a significant concern in genome-wide association studies. Using human height as an example, we show how quantile regression, a natural extension of linear regression, can better correct for subtle population structure due to its inherent ability to adjust for quantile-specific effects of covariates such as principal components. We utilize data from the UK biobank and the SardiNIA/ProgeNIA project for demonstration.

genetics↗

bioRxiv2025

A tissue-scale strategy for sensing threats in barrier organs

Barrier organs rely on a limited set of pattern recognition receptors (PRRs) to detect diverse immunogenic challenges. How organs assess threats and adjust immune responses to balance host protection with collateral tissue damage remains unclear. Here, by analyzing influenza infection in the lung using single-molecule imaging and spatial transcriptomics, we discovered a tiered threat-sensing strategy at the tissue scale, where the probability of detecting and responding to infection is lowest in the outermost epithelia and highest in the inner stroma. This strategy emerges from spatially graded PRR expression that results in cell-type-specific probabilities of threat-sensing across the tissue, a design broadly adopted by barrier organs. Selectively increasing PRR expression in lung epithelia in vivo exacerbated tissue damage upon inflammatory challenge. These results reveal a spatially tiered strategy to tolerate threats restricted within the epithelia, and yet enable progressively potent immune responses as threats invade deeper into the tissue.

immunology↗

bioRxiv2025

Collagen-binding IL-12 expressing STEAP1 CAR-T cells reduce toxicity and eradicate mouse prostate cancer in combination with checkpoint inhibitors

Immunosuppressive microenvironments, the lack of immune infiltration, and antigen heterogeneity pose significant challenges for chimeric antigen receptor (CAR)-T cell therapies to tackle solid tumors. CAR-T cells were armed with immunostimulatory payloads, such as interleukin-12 (IL-12), to overcome this issue, but faced intolerable toxicity during clinical development. Here, we show that collagen-binding domain-fused IL-12 (CBD-IL-12) was retained within syngeneic murine prostate tumors, after secretion from CAR-T cells targeting human six transmembrane epithelial antigen of the prostate 1 (STEAP1). This led to equivalently high intratumoral interferon-{gamma} levels without hepatotoxicity and infiltration of T cells into non-target organs, compared with unmodified IL-12. Both innate and adaptive immune compartments were dramatically activated and recognized diverse tumor antigens after CBD-IL-12 CAR-T cell treatment. Combination immunotherapy of CBD-IL-12 CAR-T cells and immune checkpoint inhibitors eradicated large tumors in an established prostate cancer model, without pre-conditioning chemotherapy. The therapy generated anti-tumor immunological memory. CBD-fusion to potent yet toxic payloads of CAR-T therapy may remove obstacles to their clinical translation towards elimination of solid tumors.

bioengineering↗