Biology subjects

Zhou, G.

Publications and source records attributed to Zhou, G..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Indomethacin exerts both cyclooxygenase inhibition-dependent and independent mechanisms to enhance chemo-immunotherapy in mice

Nonsteroidal anti-inflammatory drugs (NSAIDs) primarily act by inhibiting cyclooxygenases (COX1 and COX2), thereby reducing production of the proinflammatory mediator prostaglandin E2 (PGE2). Because PGE2 is a critical driver of cancer progression and tumor immune evasion, this has motivated interest in combining NSAIDs with chemotherapy or immunotherapy for cancer treatment. However, since COX and PGE2 levels vary across tumor types, it remains unclear whether tumor PGE2 abundance solely dictates tumor response to NSAID-based therapies. Here, we investigated the therapeutic potential of indomethacin (Indo), a prototypical NSAID, in combination with cyclophosphamide (CTX), a widely used chemotherapeutic agent with immunostimulatory properties. Metronomic administration of Indo significantly enhanced the antitumor efficacy of CTX in multiple murine tumor models exhibiting variable COX2 and PGE2 levels, including CT26, MC38, 4T1 and A20. The antitumor effects of CTX+Indo required CD8 T cells and T-cell trafficking from tumor-draining lymph nodes and were further potentiated by anti-PD-1 blockade. Single-cell RNA sequencing (scRNA-seq) revealed that responsive CT26 tumors exhibited a reprogrammed tumor immune microenvironment (TIME), marked by increased effector CD8 T-cell infiltration, reduced immunosuppressive myeloid populations, and enhanced interferon signaling in tumor cells. Importantly, Indo retained therapeutic benefit following CTX even in tumors incapable of producing PGE2, demonstrating a critical contribution of COX-independent mechanisms, particularly inhibition of tumor-intrinsic oncogenic RAS signaling, to the enhanced efficacy of the CTX+Indo combination. Collectively, our results provide strong preclinical rationale for leveraging the COX/PGE2 and RAS dual inhibitory capacities of NSAIDs to enhance the efficacy of chemotherapy and immunotherapy.

immunology↗

bioRxiv2025

Design and synthesis of mirror image fluorescent nucleotide reversible terminator, (L)-3'-O-azidomethyl-dGTP-N3-fluorophore, and (D)-9°N mutant DNA polymerase for mirror image (L)-DNA sequencing by synthesis

Functional DNA molecules such as aptamers, catalytic DNAzymes, and dynamic nanostructures have many promising applications in diagnostics and therapeutics yet suffer from limited utility due to immunogenicity and unwanted interactions with other biomolecules, leading to nuclease degradation and unintended sequence driven binding to native sample DNA. Bio-orthogonal, (L)-form or mirror image DNA overcome these challenges yet suffers from the inability to sequence these molecules in a high-throughput manner, a necessary step in the rapid creation of functional (L)-DNA. Here, we report for the first time, design, synthesis, and functional demonstration of (L)-DNA sequencing by synthesis (SBS) using a fluorescent nucleotide reversible terminator, (L)-3'-O-azidomethyl-dGTP-N3-fluorophore paired with a (D)-9{degrees}N mutant DNA polymerase, both enantiomers (i.e., mirror image) of their natural biologically active forms. TABLE OF CONTENTS (GRAPHICAL ABSTRACT) O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/692211v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@160650forg.highwire.dtl.DTLVardef@18add51org.highwire.dtl.DTLVardef@1c80104org.highwire.dtl.DTLVardef@1bad99b_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2025

Adapting Epigenetic Clocks for Cell-Free DNA High-Throughput Sequencing Data

Background.Circulating cell-free DNA (cfDNA or ccfDNA) methylation sequencing holds promise for developing epigenetic aging clocks in minimally invasive aging assessment applications. However, current clocks--primarily trained on array-based data--do not readily generalize to methylation profiles of cfDNA, due to stochasticity and uncertainty arising from the limited input amounts of cfDNA and technical characteristics of high-throughput sequencing (HTS) platform. Due to lack of training to correct this uncertainty, direct application of legacy clocks to HTS data will inevitably produce unreliable estimates, introduce predictive discordance and undermine the trustworthiness of epigenetic biomarkers. Despite this urgent challenge, a systematic, model-agnostic framework for adapting existing epigenetic clocks to HTS-based cfDNA data remains lacking. Methods.Here, we generated a dedicated benchmark dataset comprising paired cfDNA and genomic DNA (gDNA), which was replicated and profiled across two methylation arrays and two targeted HTS platforms. We evaluated 53 epigenetic clocks for CpG coverage, reproducibility, cross-platform consistency, and age prediction accuracy. Further, we systematically explored and tested multiple adaptation strategies including depth filtering, beta-value imputation, and transfer learning via model distillation aiming at improving performance and clinical concordance of legacy epigenetic clocks on cfDNA HTS datasets. Results.Our results show that inherent technical noise of HTS platforms compromises diagnostic precision, presenting as a significant burden for applying legacy clocks on cfDNA HTS data. However, this technical limitation can be systematically neutralized. By enforcing stringent sequencing depth protocols ([≥]10x ideally 20x), employing robust algorithmic stabilization (L2-heavy clocks and imputation) and transfer learning, we can effectively isolate genuine physiological aging signals from technical artifacts. Ultimately, an adaptation pipeline incorporating all these methods effectively improved age prediction accuracy and, closed the gap between epigenetically induced aging status and clinically assessed realities. Besides, the pipeline demonstrated improved diagnostic sensitivity for neurodegenerative conditions such as amyotrophic lateral sclerosis, establishing a reliable foundation for non-invasive clinical monitoring. Conclusions.This study provided a comprehensive framework and practical guidelines for adapting epigenetic clocks to HTS-based cfDNA data, paved the path for more general application of cfDNA-based aging assessment and liquid biopsies.

bioinformatics↗

bioRxiv2025

PhysioMotion Artifact: A Task-Driven EEG Dataset with Channel-Level Motion Artifact Annotations

Physiological artifacts pose persistent challenges in electroencephalo-gram (EEG) data acquisition, often compromising interpretation and post-analysis of EEG signals across research and clinical applications. To address such limitations, including various artifact types, insufficient annotations, and low spatial resolutions, we present PhysioMotion Artifact, a large-scale, task-driven EEG dataset with channel-level artifact annotations. EEG data was acquired from 30 healthy participants performing 16 systematically designed single-type and multi-type movement tasks, inducing 14 distinct types of physiological artifacts. To demonstrate the utility of the dataset, we implemented a Convolutional Neural Networks-Transformer hybrid model for artifact detection and classification, achieving 98% accuracy in binary classification and 85% in 14-class classification tasks.

neuroscience↗

bioRxiv2025

Uncertainty-Aware Tau Detection in Progressive Supranuclear Palsy Using Object Detection Models

Abnormal tau accumulation is a hallmark of neurodegenerative tauopathies such as Progressive Supranuclear Palsy (PSP). Traditional post-mortem assessments rely on manual lesion annotation, which is time-consuming and subjective. Existing machine learning methods typically involve multi-stage, feature-based pipelines, resulting in limited scalability and reliance on handcrafted features. This work evaluates deep learning-based object detection (OD) as a unified, end-to-end alternative for automating tau lesion identification and classification. We fine-tuned YOLO and Faster R-CNN models on a dataset of 29,852 tau lesions from 16 PSP brain slides, achieving mAP@50 of 0.702. To address the limitations of conventional OD models, which assume overconfident spatial predictions, we integrated Monte Carlo Dropout into both architectures to estimate spatial and label uncertainty. We evaluated detection uncertainty using Probability-based Detection Quality. Our results demonstrate that incorporating uncertainty offers a more comprehensive evaluation of detection reliability, complementing traditional metrics like mAP. For clinical application, we integrate a non-stochastic YOLO model into QuPath for whole-slide inference. Downstream clinical validation was conducted, including SHAP-based saliency analysis and correlation between model-predicted tau densities and PSP Rating Scale scores, a clinical disease severity measure. The alignment of detection outputs with pathological features and clinical severity underscores the clinical utility of OD models. Overall, this work highlights the complementary strengths of deep object detectors and uncertainty quantification, offering a path toward scalable, interpretable, and clinically meaningful tau pathology analysis. Code is available at https://github.com/Pidongg/dl_histopathology.

bioinformatics↗

bioRxiv2025

The Critical and Unexpected Role of a Methyl Group in Interleukin-17A Inhibitors

Interleukin-17 (IL-17) is a pro-inflammatory cytokine primarily secreted by Th17 cells. It plays a crucial role in the bodys immune defense against fungal and bacterial pathogens. However, an imbalance in IL-17 production can contribute to the development of autoimmune and inflammatory disorders. Therapeutic strategies targeting IL-17, such as blocking antibodies like secukinumab (Cosentyx), have been successfully developed. These antibodies are currently employed in the treatment of various conditions, including psoriasis, psoriatic arthritis, and ankylosing spondylitis. More recently, a small molecule inhibitor of IL-17, LY3509754, progressed to clinical trials but was halted during Phase 1 due to unfavorable hepatotoxicity. Two derivatives, compounds 7 and 8, did not advance to clinical trials due to safety concerns. These three compounds (7, 8, and the original lead compound, presumably implied) share a common difluoro substituent, which was hypothesized to be the cause of the observed safety issues. In subsequent structure-activity relationship (SAR) studies, replacing the difluoro substituent with a single methyl group (resulting in compound 9) unexpectedly led to a significant improvement in cellular activity. Furthermore, compound 9 exhibited a very low unbound fraction and reduced liver distribution, ultimately translating to high in vivo efficacy with a sufficient safety margin. This seemingly minor methyl substitution transformed the compound into a highly promising preclinical candidate (compound 9), now slated for further development. Co-development inquiries are welcome. Please contact us at enan1@dcpc.com. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/680113v2_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@3f31adorg.highwire.dtl.DTLVardef@d6873forg.highwire.dtl.DTLVardef@494af7org.highwire.dtl.DTLVardef@1d8b7eb_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2025

Quantitative Prevalence, Chemical Speciation, and ECM-Embedded Networks of Biogenic Silicon in Animal Tissues Revealed by Refined Analytical Methods

Silicon, abundant in Earths biosphere, plays crucial roles in many organisms. However, its biological functions in animals remain inadequately characterized, primarily constrained by methodological limitations in quantifying, identifying, and visualizing biogenic silicon in tissues. Here, we refined the molybdenum blue colorimetric method (MB) for more accurate silicon quantification, developed integrated mass spectrometry techniques to delineate chemical natures of biogenic silicon, and optimized micro-X-ray fluorescence (micro-XRF) imaging for spatial mapping in animals. Employing these methodologies, we demonstrated that silicon abundance in animals substantially exceeds prior estimates--surpassing essential elements like iron. We also revealed the chemical architectures of diverse silicon compounds and visualized tissue-specific distributions in mice and humans, indicating its predominant localization within extracellular matrix (ECM) and suggesting the formation of silicified networks. These findings establish silicon as a structurally and quantitatively significant element with profound biological implications, warranting its recognition as an essential factor in human physiology and pathology.

biochemistry↗

bioRxiv2025

ChemGenXplore: An Interactive Tool for Exploring and Analysing Chemical Genomic Data

MotivationChemical genomics is a useful high-throughput approach to systematically link phenotypes to genotypes. However, the vast datasets generated remain challenging to explore due to the lack of integrated, interactive tools for visualisation and analysis. Existing workflows often require multiple independent software tools, limiting data accessibility and collaboration. Therefore, we created a user-friendly platform that enable efficient exploration and sharing of chemical genomics data. ResultsWe developed ChemGenXplore, a web-based Shiny application designed to streamline the visualisation and analysis of chemical genomic screens. It offers two primary functionalities: one for exploring pre-implemented datasets and another for analysing user-uploaded datasets. ChemGenXplore enables users to visualise phenotypic profiles, assess gene-gene and condition-condition correlations, perform GO and KEGG enrichment analysis, and generate customisable, interactive heatmaps. By consolidating these features into an interactive and accessible tool, ChemGenXplore facilitates data sharing, enhances reproducibility, and promotes collaboration within the research community. Availability and ImplementationChemGenXplore is freely accessible as a web application at https://chemgenxplore.kaust.edu.sa/ and is also available for local deployment via GitHub at https://github.com/Hudaahmadd/ChemGenXplore.

bioinformatics↗

bioRxiv2025

Loss of NRMT1 allows expression of multiple differentiation pathways and alters transcription of secreted proteins in C2C12 myoblasts

Muscle stem cells (satellite cells) retain their identity and function through expression of the paired homeobox transcription factor PAX7. PAX7 is able to both stimulate satellite cell proliferation and activate target genes involved in establishing myogenic identity, including myogenic factor 5 (MYF5) and the other myogenic regulatory factors (MRFs). Upregulation of the MRFs promotes commitment to the muscle lineage by initiating withdrawal from the cell cycle, upregulating expression of muscle-specific transcripts, and directing myoblast fusion. We have previously shown that knockout of the N-terminal methyltransferase NRMT1 in C2C12 mouse myoblasts results in significantly decreased Pax7 expression, an inability of the cells to differentiate into myotubes, and abnormal upregulation of osteogenic markers. Here, we use RNA-sequencing to more comprehensively determine how loss of NRMT1 affects the transcriptional profile of proliferating and differentiating C2C12 myoblasts. We see that upon inducing differentiation, NRMT1 knockout cells can downregulate cell cycle, DNA replication, and histone gene expression. Though they also have significantly downregulated Pax7 and Myf5 expression, other muscle-specific transcripts are significantly increased over wild type, indicating the muscle transcriptional program is not completely inhibited. However, signaling pathways involved in the differentiation of other types of mesenchymal and hematopoietic lineages are also increased with NRMT1 loss and expression of chemotactic genes is downregulated. Together, these data indicate that NRMT1 knockout cells can upregulate genes needed for cell cycle withdrawal and muscle specification but fail to suppress markers of other lineages and activate normal chemotactic signaling, which may lead to the observed differentiation phenotypes.

genomics↗

bioRxiv2025

An Unexpected Role of Molecular Chaperones in Regulating the Excited States and Activities of Folded Enzymes

Molecular chaperones are recognized for assisting protein folding. Emerging evidence suggests that chaperones also interact with natively folded proteins. Yet their functional impact on folded proteins remains unclear. Here, we show that chaperones can directly modulate the conformational dynamics and catalytic efficiency of enzymes in their native states. Spy and Hsp70 increased lysozyme activity by altering conformational exchanges, and this effect extended across other chaperones and enzymes. Hsp70, Spy, Hsp104, and Hsp20 enhanced the catalytic activities of multiple folded enzymes, including alkaline phosphatase, DNA polymerase Pfu, endonucleases Cas12a/Cas13a, and xylanase. These findings uncover an unanticipated function of chaperones in regulating enzyme function expanding their mechanistic scope beyond folding assistance and suggests opportunities in enzyme engineering, diagnostics, and cellular regulation.

molecular biology↗

bioRxiv2025

Systemic Inflammation Modulates Clearance and drives Extra-Hepatic Distribution of Extracellular Vesicles

Extracellular vesicles (EVs) are promising vehicles for targeted therapeutic delivery, capable of encapsulating and transporting biomolecules to specific cells and tissues. Given that inflammation is central to many acute and chronic diseases, understanding EV biodistribution under inflammatory conditions is essential for therapeutic optimization. This study examines how acute systemic inflammation influences EV biodistribution, clearance, and plasma half-life, with a focus on the role of macrophages and their polarization states. Using a lipopolysaccharide (LPS)-induced inflammation model in wild-type mice and bioluminescent and fluorescent labelling of EVs, we observed that inflammation extends the plasma half-life of EVs by over 600-fold within 2 hours and 900-fold at 24 hours post-administration, leading to significant enrichment in inflamed organs, particularly the liver and spleen. Enhanced accumulation in specific tissues translated to increased targeting of immune- and epithelial cells within those organs, with notable uptake by hepatocytes in the liver. This prolonged half-life was attributed to the altered EV protein corona in inflamed tissues, which facilitates cellular association. These findings underscore the complex dynamics between EVs and immune cells under inflammatory conditions and provide critical insights for advancing EV-based therapies in chronic inflammatory diseases.

bioengineering↗

bioRxiv2025

Almost Free Enhancement of Multi-Population PRS: From Data-Fission to Pseudo-GWAS Subsampling

Many multi-population polygenic risk score (PRS) methods have been proposed to improve prediction accuracy in underrepresented populations; however, no single method outperforms other methods across all data scenarios. Although integrating PRS results across multiple methods and populations may lead to more accurate predictions, this approach may be limited by the availability of individual-level tuning data to calculate combination weights. In this manuscript, we introduce MIXPRS, a robust PRS integration framework based on data fission principles, to effectively combine multiple multi-population PRS methods using only genome-wide association study (GWAS) summary statistics from multiple populations. Specifically, MIXPRS employs SNP pruning to mitigate linkage disequilibrium (LD) mismatch between the training GWAS summary statistics and LD reference panels, and utilizes non-negative least squares regression to robustly estimate PRS combination weights. Extensive simulations and real-data analyses involving 22 continuous traits and four binary traits across five populations from the UK Biobank and All of Us datasets demonstrate that MIXPRS consistently outperforms the existing methods in prediction accuracy. Because MIXPRS relies solely on GWAS summary statistics, it enjoys broad accessibility, robustness, and generalizability for underrepresented populations.

genetics↗

bioRxiv2025

Exploring C1 substrate cofeeding in Eubacterium limosum with AneVO, a low-cost anaerobic parallel bioreactor platform

Acetogenic bacteria have emerged as attractive biocatalysts for renewable biochemical production, using the highly efficient Wood-Ljungdahl pathway to convert a range of sustainable single-carbon (C1) feedstocks. The major challenge is their energy-constrained anaerobic lifestyle, which results in slow growth and limits the product spectrum. To overcome this limitation, here we investigate substrate co-metabolism in the acetogen Eubacterium limosum, cofeeding either carbon monoxide (CO) or glucose alongside the primary C1 substrate (methanol or formate). To increase experimental throughput, we developed AneVO, a parallel mini bioreactor system based on eVOLVER that enables benchtop anaerobic batch and fed-batch cultivation, along with continuous delivery of anaerobic gas blends. With all substrate pairs tested, E. limosum grew faster and reached 52-254% higher cell densities with cofeeding, while maintaining or improving the volumetric uptake rate of the main C1 substrate. Product formation also improved, with an increase in volumetric acetate productivity from glucose cofeeding of 2.2-fold with formate and 2.4-fold with methanol, and 3-fold from CO cofeeding with methanol. Together these results validate AneVO as a low-cost platform for convenient benchtop cultivation of strict anaerobic microbes in multiple growth modes, and present a strategy for enhancing C1 bioconversion rates in E. limosum, an emerging model acetogen.

microbiology↗

bioRxiv2025

Joint Modeling of Effect Sizes for Two Correlated Traits: Characterizing Trait Properties to Enhance Polygenic Risk Prediction

Recent years have witnessed a surge in the development of innovative polygenic score (PGS) methods, driving their extensive application in disease prevention, monitoring, and treatment. However, the accuracy of genetic risk prediction remains moderate for most traits. Currently, most PGSs were built based on the summary statistics from the target trait, while many traits exhibit varied degrees of shared genetic architecture or pleiotropy. Appropriate leveraging of pleiotropy from correlated traits can potentially improve the performance of PGS of the target trait. In this study, we present PleioSDPR, a novel method that jointly models the genetic effects of complex traits to characterize conditions under which considering pleiotropy enhances polygenic risk prediction. PleioSDPR models the joint distribution of effect sizes across traits, allowing SNPs to be null for both traits, causal for only one trait, or causal for both traits, while accommodating region-specific genetic correlations and unequal heritability between traits. Through extensive simulations and real trait applications, we demonstrate that PleioSDPR improves prediction performance compared with several univariant and multivariate PGS methods, especially when there is no validation dataset. For example, by incorporating information from schizophrenia or leg fat-free mass, PleioSDPR effectively improves the prediction accuracy of bipolar disease (14.2% accuracy gain) and hip circumstance (20.65% accuracy gain), respectively. Moreover, our findings demonstrate that traits exhibiting high genetic correlations and heritability, and low overlapping sample sizes contribute more to the improvement of prediction accuracy of the target trait. Overall, our study highlights the potential of PleioSDPR to enhance the accuracy of genetic risk prediction by leveraging pleiotropy and considering a broader spectrum of traits and diseases. These findings contribute to the understanding of polygenic risk prediction and underscore the importance of incorporating pleiotropic information for improved utilization in disease prevention and treatment strategies.

genetics↗

bioRxiv2025

DCAF16-Based Covalent Degradative Handles for the Modular Design of Degraders

Targeted protein degradation (TPD) is a powerful strategy for targeting and eliminating disease-causing proteins. While heterobifunctional Proteolysis-Targeting Chimeras (PROTACs) are more modular, the rational design of monovalent or molecular glue degraders remains challenging. In this study, we generated a small library of BET-domain inhibitor JQ1 analogs bearing elaborated electrophilic handles to identify permissive covalent degradative handles and E3 ligase pairs. We identified an elaborated fumaramide handle that, when appended onto JQ1, led to the proteasome-dependent degradation of BRD4. Further characterization revealed that the E3 ubiquitin ligase CUL4DCAF16--a common E3 ligase target of electrophilic degraders--was responsible for BRD4 loss by covalently targeting C173 on DCAF16. While this original fumaramide handle, when appended onto other protein-targeting ligands, did not accommodate the degradation of other neo-substrates, a truncated version of this handle attached to JQ1 was still capable of degrading BRD4, now through targeting both C173 and C178. This truncated fumaramide handle, when appended on various protein targeting ligands, and was also more permissive in degrading other neo-substrates, including CDK4/6, SMARCA2/4, and the androgen receptor (AR). We further demonstrated that this optimized truncated fumaramide handle, when transplanted onto an AR DNA binding domain-targeting ligand, could degrade both AR and the undruggable truncation variant of AR, AR-V7, in androgen-independent prostate cancer cells in a DCAF16-dependent manner. Overall, we have identified a unique DCAF16-targeting covalent degradative handle that can be transplanted across several protein-targeting ligands to induce the degradation of their respective targets for the modular design of monovalent or bifunctional degraders.

biochemistry↗

bioRxiv2025

INHBA promotes the progression of gastric cancer by activating MAPK signaling pathway via targeting ITGA6

Gastric cancer (GC) is one of the most common malignancies, ranking as the fifth most common cancer and the fourth leading cause among cancer related deaths worldwide. Upregulated INHBA expression in gastric cancer tissues compared to adjacent non-cancerous tissues was confirmed by immunohistochemistry and qRT-PCR analysis. This increased expression of INHBA was found to be significantly associated with the incidence of tumor lesion, lymph node metastasis and the progression to more advanced TNM stages in patients. Functional experiments showed that INHBA could promote the proliferation of GC cells, enhance migration and invasion in vitro, while simultaneously facilitating the inhibition of apoptosis. To test whether INHBA regulated the tumor growth in vivo, the animal studies were performed. The indicated that INHBA over-expression promoted the tumor growth, including weight and volume. Moreover, this study demonstrated through a series of experiments including RNA-seq, Co-IP, Co-IF, Western blot, and Rescue studies that INHBA promotes the progression of GC by targeting ITGA6 to regulate the MAPK signaling pathway. In summary, understanding the role of INHBA/ITGA6/MAPK in tumourigenesis could provide new insights into gastric cancer therapy and targeted inhibition of INHBA might be a potential therapeutic approach for GC treatment.

cancer biology↗

bioRxiv2025

A distinctive subset of microglia positioned at the paraventricular zone are dedicated to cleaning the cerebrospinal fluid

The interconnected ventricles in the central nervous system are filled with cerebrospinal fluid (CSF) which serves multifaceted roles including bring away metabolic wastes. However, whether there is an inherent "dislodging" mechanism, in addition to CSF drainage and reabsorption, at play for the removal of microparticles in the CSF is unclear. In this study, we identified a subset of microglia distributed in close proximity to the ependymal wall of ventricles. They differed from parenchymal microglia in morphology, behaviors and transcriptomes. In particular, the paraventricular microglia extended transependymal dendrites into the lumen of ventricles and were proficient in sequestering and phagocytosing intraventricular exogenous particles when present. A specific removal of the paraventricular microglia led to an acute ventriculomegaly due to an increased colloid osmotic pressure in the CSF resulting from protein accumulation. Thus, we identified the paraventricular microglia as specific guardians in monitoring and cleaning the CSF.

immunology↗

bioRxiv2025

Integrative Analysis of Single-cell and Bulk RNA Sequencing Reveals Macrophage Heterogeneity in Lyme Arthritis

IntroductionLyme arthritis (LA) is a common late-stage manifestation of Lyme disease caused by Borrelia burgdorferi (Bb) infection. Macrophages play a crucial role in LA pathogenesis, yet their heterogeneity and functional states within the arthritic microenvironment remain poorly understood. MethodsWe integrated single-cell RNA sequencing data from ankle joint tissues of Bb-infected C57BL/6 mice with bulk RNA sequencing data from both infected ankle joints and bone marrow-derived macrophages. We developed a LA mouse model and validated key findings through histopathological examination and RT-qPCR analysis. ResultsWe identified nine distinct cell types in the joint microenvironment with significant compositional shifts during disease progression. Analysis of 16,617 Macrophages/Monocytes revealed seven functionally distinct subpopulations with differential responses to infection. Pseudo-time trajectory analysis demonstrated three principal differentiation pathways from monocyte-derived macrophages toward anti-inflammatory, TNF+, and IL1b+ inflammatory phenotypes. Cell-cell communication analysis revealed significantly altered TGF-{beta} signaling networks in LA, with the Tgfb1-(Tglbr1+TgIbr2) axis playing a critical role. GSVA and pathway analyses showed metabolic reprogramming from oxidative phosphorylation toward glycolysis in macrophages during infection. Through integrated analysis and LASSO regression, we identified four characteristic genes (SIRPB1C, FABP5, MMP14, and EGR1) as potential LA biomarkers and developed a diagnostic nomogram with high predictive accuracy. ConclusionOur integrative analysis provides comprehensive insights into macrophage heterogeneity and functional plasticity in LA, identifying characteristic biomarkers and potential therapeutic targets. The metabolic reprogramming and altered TGF-{beta} signaling networks we identified may contribute to disease pathogenesis and offer new avenues for intervention strategies.

genomics↗