Single-cell transcriptomic analysis identifies VAV3 as a critical regulator of macrophage function in gouty arthritis

Single-cell transcriptomic analysis identifies VAV3 as a critical regulator of macrophage function in gouty arthritis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Single-cell transcriptomic analysis identifies VAV3 as a critical regulator of macrophage function in gouty arthritis Fang Liu, Weizhen Weng, Zuoyu Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6054213/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Gouty arthritis is a common inflammatory disease triggered by the deposition of monosodium urate (MSU)crystals in the joints, leading to both acute and chronic inflammation. While macrophages have long been implicated in the pathogenesis of gouty arthritis, the exact mechanisms, differentiation conditions, and key molecules involved remain unclear. Methods: Gene Set Enrichment Analysis (GSEA) was used to determine the primary functions of macrophages. High-dimensional weighted gene co-expression network analysis (hdWGCNA), transcription factor activity analysis, and pseudotemporal trajectory analysis were applied to identify VAV3 as a key gene regulating macrophage differentiation. The correlation between VAV3 expression and relevant biological processes was further validated through Gene Set Variation Analysis (GSVA) and by examining VAV3 expression in related bulk RNA sequencing datasets from the GEO database, confirming its association with gouty arthritis. Results: Our analysis indicates that macrophages are a crucial cell type in the synovial fluid microenvironment of gouty arthritis, where their differentiation is influenced by various factors. VAV3 is a key gene regulating macrophage differentiation and function, and its expression is positively correlated with several phenotypic features of disease progression, including angiogenesis and inflammation. The differential expression of VAV3 is validated across multiple RNA sequencing datasets from the GEO database. Conclusion: Our findings underscore the critical role of macrophages in gouty arthritis and identify VAV3 as a novel biomarker and potential therapeutic target. These results deepen our understanding of the inflammatory microenvironment in gouty arthritis and suggest that VAV3 could have broader implications in other gout-related conditions, such as gouty nephropathy. Gouty arthritis Macrophages Single-cell RNA sequencing Immunology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Gout is a common inflammatory disease characterized by the deposition of monosodium urate (MSU) crystals in both joint and non-articular tissues 1 . When uric acid levels are elevated, coupled with favorable conditions such as specific temperature and pH, MSU crystals accumulate in local tissues, triggering both acute and chronic inflammatory responses. These responses can severely impair a patient's quality of life and complicate therapeutic interventions 2 . However, the specific cell types and key molecules involved in the inflammatory microenvironment induced by MSU deposition remain insufficiently characterized. Mononuclear phagocytes (MNPs) represent a diverse family of cells, including progenitor cells, blood monocytes, dendritic cells (DCs), and resident tissue macrophages 3 . Macrophages exhibit remarkable plasticity, enabling them to perform a wide range of functions depending on the local microenvironment. This plasticity and diversity are pivotal to the pathophysiology of numerous diseases 4 . In synovial tissues, macrophages are capable of secreting pro-inflammatory cytokines and chemokines 5 , thereby exacerbating synovial inflammation 6 . Moreover, they can disrupt the balance between extracellular matrix metabolism and biosynthesis through the secretion of matrix metalloproteinases 7 . Outside the synovium, macrophages in peripheral blood engage in diverse biological processes, such as pro-inflammatory responses, antigen presentation, tissue remodeling, and anti-inflammatory functions 8 . In the past, macrophages have been classified into two subtypes, "M1" and "M2," based on the expression of specific markers identified by flow cytometry 9 . However, with the advent of single-cell sequencing technologies, our understanding of macrophage differentiation has advanced to the subpopulation level, providing deeper insights into disease mechanisms. This approach facilitates detailed analyses of cell-to-cell interactions, the identification of key transcription factors driving cellular behavior, and the simulation of dynamic changes in cellular composition during disease progression 10 , 11 .Previous studies have investigated the transition from gout flare to remission at the single-cell level, focusing on changes in immune cell composition and inflammatory markers in human peripheral blood mononuclear cells (PBMCs). These studies also examined the relationship between molecules such as HLA-DQA1 in PBMCs and systemic inflammation 12 , 13 . Nevertheless, the dynamic roles and phenotypic evolution of macrophages in gouty arthritis, particularly at the single-cell resolution, remain poorly understood. In this study, we utilized single-cell sequencing datasets to integrate and analyze RNA sequencing data from the peripheral blood of gout patients and synovial fluid of patients with gouty arthritis. Our analysis revealed that macrophages play a pivotal role in the onset of gouty arthritis. Through various single-cell analysis techniques, including biological function enrichment analysis, high-dimensional weighted gene co-expression network analysis (hdWGCNA), transcription factor analysis, and pseudo-time trajectory analysis, we elucidated the roles and differentiation conditions of macrophages in the pathogenesis of gouty arthritis. Moreover, we identified VAV3 as a key molecule implicated in macrophage function during gouty arthritis. This discovery enhances our understanding of the pathological mechanisms underlying gouty arthritis and may provide potential targets for its diagnosis and therapeutic intervention. Materials and methods Single-Cell Data Acquisition and Processing The single-cell RNA sequencing datasets for peripheral blood mononuclear cells (PBMCs) from gout patients (GSE217561) and synovial fluid from gout patients (PRJNA861849) were retrieved from the NCBI Gene Expression Omnibus (GEO) and NCBI BioProject databases, respectively. The CellRanger software (version 4.6) was used to generate single-cell gene expression matrices for subsequent analysis. The data underwent quality control, normalization, and integration procedures. Batch effects were corrected using the R package "Harmony". To visualize the data in two dimensions, we employed Uniform Manifold Approximation and Projection (UMAP), which allows for an unsupervised display of distinct cell subpopulations. Clustering results were visualized using the DimPlot function in the Seurat package. Gene Set Enrichment Analysis (GSEA) Gene Set Enrichment Analysis (GSEA) is a computational method employed to assess the distribution patterns of genes within predefined gene sets, based on a ranking of genes according to their association with specific phenotypic traits. This method facilitates the identification of gene sets that are significantly enriched in relation to the phenotype of interest, providing insights into their potential functional roles. In this study, differentially expressed genes associated with macrophage subpopulations were identified using the R package FindAllMarkers. Subsequently, GSEA was utilized to examine the functional enrichment of these differentially expressed genes, enabling the annotation of biological functions associated with each cell type. hdWGCNA Analysis and GO Analysis High-dimensional weighted gene co-expression network analysis (hdWGCNA) is an advanced framework for analyzing co-expression networks within high-dimensional transcriptomic data, including single-cell and spatial RNA sequencing data. This method facilitates the identification of disease-associated co-expression network modules within distinct cell populations, offering valuable insights into the molecular mechanisms underlying disease 14,15 . In this study, hdWGCNA was performed to identify potential key genes in mononuclear phagocytes (MNPs) associated with the severity of gout. MNP subpopulations were extracted from the single-cell RNA sequencing data to construct a gene expression correlation matrix. This was followed by the construction of a weighted gene co-expression network and module detection. The clusterProfiler R package was employed for Gene Ontology (GO) functional enrichment analysis of the key genes identified in the significant modules. This analysis enabled the characterization of the biological functions of the core genes and provided insight into their roles in gout pathogenesis and the functional characteristics of the key modules. Transcription factor activity analysis pySCENIC is an updated and Python-based reimplementation of the SCENIC tool, which is widely used for analyzing single-cell RNA sequencing data 16 . SCENIC leverages transcription factors and cis-regulatory sequences to define distinct cell states. This approach allows for the identification of transcription factor combinations that regulate cell type-specific transcriptomes, providing deep insights into the regulatory mechanisms underlying cellular heterogeneity 17,18 .To predict the relationship between transcription factors and their target genes, the GENIE3 algorithm was utilized to train a random forest model, which quantifies the influence of individual transcription factors on gene expression regulation. The resulting transcription factor–target gene (TF-TG) co-expression network was further validated using RcisTarget, which integrates transcription factor binding site data to provide an additional layer of reliability. The transcriptional activity of individual cells was quantified using the AUCell score, which enabled the identification of cells with significantly elevated transcriptional activity. Finally, the results of the SCENIC analysis were visualized using various graphical tools, allowing for the presentation of transcriptional regulatory networks and the comparison of regulatory patterns across different cell populations or groups. Trajectory Inference of Single-Cell Differentiation CytoTRACE and Monocle3 were employed to simulate the differentiation of monocytes into macrophages. CytoTRACE is a computational framework designed to predict cellular differentiation states from single-cell RNA sequencing (scRNA-seq) data 19 . After identifying the differentiation state of each cell using appropriate computational algorithms, Monocle3 applies an unsupervised framework to map cells onto trajectories that accurately reflect the biological differentiation process. This method enables the reconstruction of dynamic cellular states along the differentiation pathway, offering valuable insights into the temporal progression and regulatory mechanisms underlying cell fate determination 20 . Statistical analysis All statistical analyses were conducted using R software (version 4.3.2) for data processing. To compare quantitative data across different groups, either a two-tailed unpaired Student's t-test or one-way analysis of variance (ANOVA) with Tukey’s post hoc multiple comparison test was applied, depending on the experimental design. A p-value of <0.05 was considered statistically significant. Results 3.1 Identification of Macrophages in gouty arthritis To investigate the cellular characteristics associated with gouty arthritis, we analyzed the single-cell dataset GSE217561 from the GEO database, which contains PBMC sequencing data from patients in different stages of gout (gout remission, GR, n=3; late gout remission, GS, n=3) and healthy controls (HC, n=3). Additionally, we examined the PRJNA861849 dataset from the NCBI BioProject database, which includes synovial fluid sequencing data from gout patients (SF, n=3). After data integration and quality control, we obtained sequencing data from 117,405 cells, including 14,833 mononuclear phagocytes (MNPs). Compared to PBMCs, synovial fluid exhibited greater cellular heterogeneity, with a significant increase in the proportion of MNPs (Fig. 1A-D). We further categorized MNPs into four subpopulations: CD14+ monocytes, CD16+ monocytes, dendritic cells (DCs), and macrophages. Notably, the proportion of macrophages was significantly elevated in gout patients, especially in the synovial fluid, compared to the healthy control group. This suggests that macrophages play a crucial role in the pathogenesis of gouty arthritis within the joint microenvironment (Fig. 1E-G). Additionally, our analysis revealed that macrophages are central in regulating fibroblast proliferation, angiogenesis, endothelial cell proliferation, chemotaxis, and tissue remodeling—biological processes that are integral to the progression of arthritis (Fig. 1H) 21-23 . 3.2 Identification of module genes in MNPs by hdWGCNA To identify key gene co-expression modules and biomarkers associated with gouty arthritis progression, we conducted high-dimensional weighted gene co-expression network analysis (hdWGCNA). A soft threshold of 6 was selected to construct an unweighted primary macrophage network for optimal connectivity (Fig. 2A). A total of 11 gene modules were detected (Fig. 2B and C). Among them, Modules 5 and 6 exhibited the strongest relevance to gout-related biological activities (Fig. 2E-H). Moreover, macrophages were identified as the predominant cell subset involved in these modules (Fig. 2D). Based on these findings, we selected the top 50 genes with the highest k-Module Membership (kME) values from Modules 5 and 6 for further investigation. 3.3 Reconstruction of transcriptional modules related to cell states during gouty arthritis To identify the master regulators of gouty arthritis, we constructed transcriptional regulatory networks with transcriptional regulators and their target genes by applying SCENIC (single‐cell regulatory network inference and clustering) analysis. The active transcription factors in synovial fluid are significantly different from those in blood.(Fig 3A) We screened the most active transcription factors in different groups for visualization.(Fig 3B) The top 50 genes corresponding to the top 5 transcription factors in synovial fluid were used to intersect with the top 50 hub genes screened in hdWGCNA, and finally two key molecules were obtained: PPARG and VAV3.(Fig 3D) Since PPARG is a well-established marker for gout 24,25 , while VAV3 has not been reported to have similar functions, we further examined VAV3 expression in MNPs and found it to be highly expressed in macrophages.(Fig 3C) 3.4 Pseudotemporal analysis confirmed the trajectory of macrophage differentiation We subsetted monocytes and macrophages from the MNP data and used CytoTRACE to assess the differentiation potential of monocytes and macrophages in different groups, as well as their individual differentiation trajectories. The CytoTRACE score, which ranges from 0 to 1, reflects stemness, with higher scores indicating lower differentiation potential. Our analysis revealed that the differentiation potential of both monocytes and macrophages was significantly lower in the GS and SF groups compared to the control and GR groups (Fig. 4A). Furthermore, macrophages exhibited markedly lower differentiation potential than monocytes (Fig. 4B). To build upon these findings, we employed Monocle3 to model the differentiation process from monocytes to macrophages and performed Gene Set Enrichment Analysis (GSEA) on the key driver genes involved (Fig. 4C-E). Our results suggest that the stimulation of cytokines such as IL-1β, TNF, and VEGF contributes to the recruitment of monocytes to the local joint microenvironment, where they differentiate into macrophages, driving the pathogenesis of gouty arthritis. Notably, the expression of VAV3 showed an increasing trend along the pseudo-time trajectory (Fig. 4F). 3.5 VAV3 Is Implicated in Multiple Biological Functions Associated with Gouty Arthritis To assess the biological functions of macrophages in gouty arthritis, we applied Gene Set Variation Analysis (GSVA) to correlate VAV3 expression with various function scores. The results revealed a positive correlation between VAV3 expression and several arthritis-related biological functions, including limb joint morphogenesis, apical junction, angiogenesis, inflammation, endothelial cell proliferation, and endothelial cell migration (Fig. 5A-F) 26-29 . To validate these findings, we examined VAV3 expression across multiple distinct bulk RNA-seq datasets. Our results showed that VAV3 was upregulated in the ankle RNA sequencing data from a mouse model of MSU-induced gouty arthritis (Fig. 5G). Additionally, VAV3 was upregulated in MSU-stimulated bone marrow-derived macrophages (BMDMs) (Fig. 5H). Interestingly, VAV3 was also upregulated in macrophage sequencing data from hyperuricemia nephropathy (Fig. 5I). These findings suggest that VAV3 may not only serve as an expression marker for gouty arthritis but also as a potential marker for gouty nephropathy. Discussion Gout is a common and painful condition that often results in debilitating complications, including arthritis and kidney disease 1 . Both the serum environment, which influences uric acid levels, and the synovial fluid environment, which affects the formation of urate crystals, are crucial in the progression of gouty arthritis 30 . Given this, we selected single-cell sequencing data from PBMCs and synovial fluid for our investigation into the cellular and molecular mechanisms underlying gouty arthritis. The advent of single-cell sequencing technology has opened up new avenues for understanding the intricate biological processes that govern disease initiation and progression. By integrating and comparing single-cell RNA sequencing data from peripheral blood and synovial fluid, we identified macrophages as the predominant cell type involved in the pathogenesis of gouty arthritis. This finding highlights the central role of macrophages in the inflammatory response characteristic of gout. Functional enrichment analysis revealed that macrophages are primarily involved in the activation of matrix cells, including mesenchymal and endothelial cells. These processes are pivotal in the pathogenesis of gouty arthritis, as they contribute to tissue remodeling, fibrosis, and inflammation. Interestingly, the cartilage matrix and fibers may provide a conducive environment for the crystallization of monosodium urate (MSU) crystals, which further exacerbates the progression of arthritis 26 . Additionally, by leveraging advanced computational tools such as hdWGCNA and SCENIC, we systematically identified VAV3 as a key molecule potentially involved in the pathological progression of gouty arthritis. To validate this, we examined VAV3 expression across multiple distinct bulk RNA-seq datasets. VAV3 has been implicated in various biological processes, such as angiogenesis, cell migration, and cytoskeletal remodeling 31 – 33 . Through hdWGCNA, we found that the gene expression module associated with VAV3 was significantly enriched for GO terms related to leukocyte proliferation, leukocyte activation, and the positive regulation of fibrous tissue formation. Collagen released by stromal cells alters the morphology of MSU crystals, promoting their endocytosis by macrophages, which further contributes to the inflammatory response 28 . These findings provide a mechanistic insight into how macrophages may exacerbate the pathological processes in gouty arthritis. Our study also highlighted several key factors that drive macrophage differentiation and, consequently, the progression of arthritis. These include the activation of cytokines such as IL-1β, TNF, TGF-β, and VEGF, all of which have been shown to be upregulated in joint effusions associated with gouty arthritis 26 , 27 , 29 . Notably, we observed an increasing trend in VAV3 expression along the pseudo-time trajectory, suggesting that VAV3 plays a crucial role in the differentiation of monocytes into macrophages and in driving the pathogenesis of gouty arthritis. In comparison to previous studies, which often focus on isolated aspects of macrophage function or use bulk tissue samples, our study stands out by integrating single-cell sequencing data from PBMCs of patients with different severities of gout and synovial fluid from gout patients. This allowed us to investigate the differentiation trajectory of monocytes into macrophages within the distinct microenvironment of gouty arthritis. Based on this analysis, we identified VAV3 as a novel key molecule involved in the pathogenesis of gouty arthritis. Furthermore, our study suggests that VAV3 may not only serve as an expression marker for gouty arthritis but also as a potential marker for gouty nephropathy, providing new insights into the broader implications of this molecule in gout-related diseases. In conclusion, our findings demonstrate that macrophages play a critical role in the pathogenesis of gouty arthritis, and that VAV3 may serve as a potential key diagnostic and therapeutic target for the disease. Declarations Clinical trial number not applicable Funding This study was supported by Guangdong Provincial Natural Science Fund (No. 2021A1515111115). Author Contribution H.Z conceived and designed the experiments, analyzed and interpreted the data, and contributed to the writing of the manuscript. L.F analyzed the experimental results and assisted in manuscript preparation and editing. W.W contributed to data acquisition, reviewed relevant literature, provided intellectual input, and approved the final manuscript. Data Availability The dataset analyzed in the current study is available in the NCBI Gene Expression Omnibus (GEO) and NCBI BioProject databases, with dataset accession numbers GSE217561 and PRJNA861849, respectively. References Dalbeth N, Gosling AL, Gaffo A, Abhishek A. Gout. Lancet (London, England) 2021; 397 (10287): 1843-55. Desai J, Steiger S, Anders HJ. Molecular Pathophysiology of Gout. Trends in molecular medicine 2017; 23 (8): 756-68. Hume DA, Millard SM, Pettit AR. Macrophage heterogeneity in the single-cell era: facts and artifacts. Blood 2023; 142 (16): 1339-47. Locati M, Curtale G, Mantovani A. Diversity, Mechanisms, and Significance of Macrophage Plasticity. Annu Rev Pathol 2020; 15 : 123-47. Li X, Li X, Yang J, et al. In Situ Sustained Macrophage-Targeted Nanomicelle-Hydrogel Microspheres for Inhibiting Osteoarthritis. 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Arthritis & rheumatology (Hoboken, NJ) 2022; 74 (7): 1295-6. Ma C, Jiang Y, Xiang Y, et al. Metabolic Reprogramming of Macrophages by Biomimetic Melatonin-Loaded Liposomes Effectively Attenuates Acute Gouty Arthritis in a Mouse Model. Advanced science (Weinheim, Baden-Wurttemberg, Germany) 2024: e2410107. Xu X, Qiu H. BRD4 promotes gouty arthritis through MDM2-mediated PPARγ degradation and pyroptosis. Molecular medicine (Cambridge, Mass) 2024; 30 (1): 67. Liu W, Dai J, Chen X, Du N, Hu J. Integrated Network Pharmacology and In-silico Approaches to Decipher the Pharmacological Mechanism of Dioscorea septemloba Thunb in Treating Gout and Its Complications. Combinatorial chemistry & high throughput screening 2023. Lan Z, Chen L, Feng J, et al. Mechanosensitive TRPV4 is required for crystal-induced inflammation. Annals of the rheumatic diseases 2021; 80 (12): 1604-14. Lee HP, Lin YY, Duh CY, et al. Lemnalol attenuates mast cell activation and osteoclast activity in a gouty arthritis model. The Journal of pharmacy and pharmacology 2015; 67 (2): 274-85. Xu H, Zhang B, Chen Y, et al. Type II collagen facilitates gouty arthritis by regulating MSU crystallisation and inflammatory cell recruitment. Annals of the rheumatic diseases 2023; 82 (3): 416-27. Huang Z, Zhong X, Zhang Y, et al. A targeted proteomics screen reveals serum and synovial fluid proteomic signature in patients with gout. Frontiers in immunology 2024; 15 : 1468810. Neilson J, Bonnon A, Dickson A, Roddy E. Gout: diagnosis and management-summary of NICE guidance. BMJ (Clinical research ed) 2022; 378 : o1754. Hilfenhaus G, Nguyen DP, Freshman J, et al. Vav3-induced cytoskeletal dynamics contribute to heterotypic properties of endothelial barriers. The Journal of cell biology 2018; 217 (8): 2813-30. Kwiatkowska A, Didier S, Fortin S, et al. The small GTPase RhoG mediates glioblastoma cell invasion. Molecular cancer 2012; 11 : 65. Toumaniantz G, Ferland-McCollough D, Cario-Toumaniantz C, Pacaud P, Loirand G. The Rho protein exchange factor Vav3 regulates vascular smooth muscle cell proliferation and migration. Cardiovascular research 2010; 86 (1): 131-40. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6054213","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":419699623,"identity":"d743bfa4-61f9-4b6a-ad9c-9662c3733531","order_by":0,"name":"Fang Liu","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Liu","suffix":""},{"id":419699627,"identity":"76b64a13-5ce9-4f65-8b32-b05886ef3fde","order_by":1,"name":"Weizhen Weng","email":"","orcid":"","institution":"Shenzhen Third People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Weizhen","middleName":"","lastName":"Weng","suffix":""},{"id":419699628,"identity":"454940b6-51b3-4bbc-a3a5-a18d44085e23","order_by":2,"name":"Zuoyu Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApUlEQVRIiWNgGAWjYBACAxCRUGDDw8/fQJIWgzQZyRkHSNHCYHDYxqAhgUgt5hLpDx88MDjPY8BwgPHDxxwitFjOSEg2SDC4zWPO3MAsOXMbMQ67kXBMAqTFsuEAGzMvcVoS24BazvEYHEggWksyG1DLAVK0nHnGDPRLMo/kjIPNRPrlePrDhz8q7Oz5+ZsPfvhIjBYkwNhAmvpRMApGwSgYBbgBADO4NJEI+gHiAAAAAElFTkSuQmCC","orcid":"","institution":"Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zuoyu","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2025-02-18 08:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6054213/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6054213/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77234278,"identity":"46a21bd6-1d77-4bcb-9af0-f0924a20aa97","added_by":"auto","created_at":"2025-02-26 12:53:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":421418,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of macrophages in peripheral blood and synovial fluid sequencing data from gout patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A): UMAP distribution map of cell types identified in peripheral blood and synovial fluid.\u003c/p\u003e\n\u003cp\u003e(B): UMAP distribution map of cells derived from peripheral blood and synovial fluid.\u003c/p\u003e\n\u003cp\u003e(C): Heatmap of top-ranking marker genes expression in different cell types.\u003c/p\u003e\n\u003cp\u003e(D): Proportion of cell types in different groups.\u003c/p\u003e\n\u003cp\u003e(E): UMAP distribution of MNPs cell types in different groups.\u003c/p\u003e\n\u003cp\u003e(F): Dotplot of top-ranking marker genes expression in different MNPs cell types.\u003c/p\u003e\n\u003cp\u003e(G): Proportion of different MNPs cell types in different groups.\u003c/p\u003e\n\u003cp\u003e(H): Gene set enrichment analysis (GSEA) (GO biological processes) on genes ranked by log2 fold change between Macrophages and other MNPs.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6054213/v1/06618ffe29f2bdb6443aa57b.png"},{"id":77234292,"identity":"5b401efb-b5a4-4de1-8905-88e9055acdde","added_by":"auto","created_at":"2025-02-26 12:53:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":754522,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHigh-Dimensional Gene Co-expression Network Analysis (hdWGCNA) Identifies Key Gene Modules Linked to Macrophages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A): Plots showing the determination of the soft-threshold power used in hdWGCNA. A power of 6 was selected as the optimal threshold.\u003c/p\u003e\n\u003cp\u003e(B): Dendrogram showing hierarchical clustering of genes into distinct modules based on their co-expression patterns using WGCNA.\u003c/p\u003e\n\u003cp\u003e(C): Bar plots display the module eigengene (kME) values for genes within different color-coded modules.\u003c/p\u003e\n\u003cp\u003e(D): Correlation analysis illustrates the relationship between different gene modules and different MNPs cell types. Positive and negative correlations are indicated in blue and red, respectively.\u003c/p\u003e\n\u003cp\u003e(E-F): Network plots for key gene modules: MNPs5 and MNPs6.\u003c/p\u003e\n\u003cp\u003e(G-H): GO enrichment of the MNP5 module and the MNP6 module.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6054213/v1/d4a026fdc4ed1fe66b887c34.png"},{"id":77234283,"identity":"218dd5f3-fa84-4103-b7c8-3f81b0053e1c","added_by":"auto","created_at":"2025-02-26 12:53:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":633677,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentiation of synovial macrophages in gout\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-B): Boxplots displayed the CytoTRACE2 analysis results, revealing that MNPs in SF groups and macrophages exhibited lower differentiation potential, indicating a higher degree of differentiation.\u003c/p\u003e\n\u003cp\u003e(C-D): Pseudotime analysis for profiling trajectory of differentiating monocytes and macrophages, colored by cell types and pseudotime.\u003c/p\u003e\n\u003cp\u003e(E): GO enrichment of pseudo-temporal driver genes in monocyte differentiation into macrophages.\u003c/p\u003e\n\u003cp\u003e(F): The dynamic trend graphs showed the relative expression of VAV3 over time at different differentiation stages.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6054213/v1/340de16ce62af1b599f57960.png"},{"id":77234281,"identity":"8c6ef7c6-7573-4bc6-b27f-31deef04e703","added_by":"auto","created_at":"2025-02-26 12:53:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":701024,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of cell type-specific regulons by SCENIC analysis and prediction of target\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A): Dotplot shows the characteristic TFs in different groups of MNPs.\u003c/p\u003e\n\u003cp\u003e(B): Rank for regulons in MNPs based on the regulon specificity score (RSS).\u003c/p\u003e\n\u003cp\u003e(C): FeaturePlot shows that NFX6-2 (+) is characteristically highly expressed in macrophages.\u003c/p\u003e\n\u003cp\u003e(D): Venn diagram showing the intersection of the top 50 hub genes in the MNPs5 and MNPs6 modules with the top 50 predicted target genes of NKX6-2.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6054213/v1/89e160514de231e2273016ff.png"},{"id":77234361,"identity":"9420217b-e5a4-4bdc-950d-74e5abfae9b1","added_by":"auto","created_at":"2025-02-26 12:53:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":416745,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVerification of the biological function of VAV3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-F): Scatter plots of correlation between VAV3 expression level and GSVA scores of biological functions.\u003c/p\u003e\n\u003cp\u003e(G-I): Expression of VAV3 in different RNA sequencing datasets.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6054213/v1/75058abf5acfe45e9ec47fb2.png"},{"id":77234491,"identity":"f4ef288c-8505-4aa1-b056-708a7404cdc8","added_by":"auto","created_at":"2025-02-26 12:54:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3810053,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6054213/v1/4aa22adc-f6ec-4bc9-95ca-2a65c1ce6a19.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single-cell transcriptomic analysis identifies VAV3 as a critical regulator of macrophage function in gouty arthritis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGout is a common inflammatory disease characterized by the deposition of monosodium urate (MSU) crystals in both joint and non-articular tissues\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. When uric acid levels are elevated, coupled with favorable conditions such as specific temperature and pH, MSU crystals accumulate in local tissues, triggering both acute and chronic inflammatory responses. These responses can severely impair a patient's quality of life and complicate therapeutic interventions\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, the specific cell types and key molecules involved in the inflammatory microenvironment induced by MSU deposition remain insufficiently characterized.\u003c/p\u003e \u003cp\u003eMononuclear phagocytes (MNPs) represent a diverse family of cells, including progenitor cells, blood monocytes, dendritic cells (DCs), and resident tissue macrophages\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Macrophages exhibit remarkable plasticity, enabling them to perform a wide range of functions depending on the local microenvironment. This plasticity and diversity are pivotal to the pathophysiology of numerous diseases\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In synovial tissues, macrophages are capable of secreting pro-inflammatory cytokines and chemokines\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, thereby exacerbating synovial inflammation\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Moreover, they can disrupt the balance between extracellular matrix metabolism and biosynthesis through the secretion of matrix metalloproteinases\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Outside the synovium, macrophages in peripheral blood engage in diverse biological processes, such as pro-inflammatory responses, antigen presentation, tissue remodeling, and anti-inflammatory functions\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In the past, macrophages have been classified into two subtypes, \"M1\" and \"M2,\" based on the expression of specific markers identified by flow cytometry\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, with the advent of single-cell sequencing technologies, our understanding of macrophage differentiation has advanced to the subpopulation level, providing deeper insights into disease mechanisms. This approach facilitates detailed analyses of cell-to-cell interactions, the identification of key transcription factors driving cellular behavior, and the simulation of dynamic changes in cellular composition during disease progression\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.Previous studies have investigated the transition from gout flare to remission at the single-cell level, focusing on changes in immune cell composition and inflammatory markers in human peripheral blood mononuclear cells (PBMCs). These studies also examined the relationship between molecules such as HLA-DQA1 in PBMCs and systemic inflammation\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Nevertheless, the dynamic roles and phenotypic evolution of macrophages in gouty arthritis, particularly at the single-cell resolution, remain poorly understood.\u003c/p\u003e \u003cp\u003eIn this study, we utilized single-cell sequencing datasets to integrate and analyze RNA sequencing data from the peripheral blood of gout patients and synovial fluid of patients with gouty arthritis. Our analysis revealed that macrophages play a pivotal role in the onset of gouty arthritis. Through various single-cell analysis techniques, including biological function enrichment analysis, high-dimensional weighted gene co-expression network analysis (hdWGCNA), transcription factor analysis, and pseudo-time trajectory analysis, we elucidated the roles and differentiation conditions of macrophages in the pathogenesis of gouty arthritis. Moreover, we identified VAV3 as a key molecule implicated in macrophage function during gouty arthritis. This discovery enhances our understanding of the pathological mechanisms underlying gouty arthritis and may provide potential targets for its diagnosis and therapeutic intervention.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eSingle-Cell Data Acquisition and Processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe single-cell RNA sequencing datasets for peripheral blood mononuclear cells (PBMCs) from gout patients (GSE217561) and synovial fluid from gout patients (PRJNA861849) were retrieved from the NCBI Gene Expression Omnibus (GEO) and NCBI BioProject databases, respectively. The CellRanger software (version 4.6) was used to generate single-cell gene expression matrices for subsequent analysis. The data underwent quality control, normalization, and integration procedures. Batch effects were corrected using the R package \u0026quot;Harmony\u0026quot;. To visualize the data in two dimensions, we employed Uniform Manifold Approximation and Projection (UMAP), which allows for an unsupervised display of distinct cell subpopulations. Clustering results were visualized using the DimPlot function in the Seurat package.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Set Enrichment Analysis (GSEA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene Set Enrichment Analysis (GSEA) is a computational method employed to assess the distribution patterns of genes within predefined gene sets, based on a ranking of genes according to their association with specific phenotypic traits. This method facilitates the identification of gene sets that are significantly enriched in relation to the phenotype of interest, providing insights into their potential functional roles. In this study, differentially expressed genes associated with macrophage subpopulations were identified using the R package FindAllMarkers. Subsequently, GSEA was utilized to examine the functional enrichment of these differentially expressed genes, enabling the annotation of biological functions associated with each cell type.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ehdWGCNA Analysis and GO Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-dimensional weighted gene co-expression network analysis (hdWGCNA) is an advanced framework for analyzing co-expression networks within high-dimensional transcriptomic data, including single-cell and spatial RNA sequencing data. This method facilitates the identification of disease-associated co-expression network modules within distinct cell populations, offering valuable insights into the molecular mechanisms underlying disease\u003csup\u003e14,15\u003c/sup\u003e. \u0026nbsp; In this study, hdWGCNA was performed to identify potential key genes in mononuclear phagocytes (MNPs) associated with the severity of gout. MNP subpopulations were extracted from the single-cell RNA sequencing data to construct a gene expression correlation matrix. This was followed by the construction of a weighted gene co-expression network and module detection. The clusterProfiler R package was employed for Gene Ontology (GO) functional enrichment analysis of the key genes identified in the significant modules. This analysis enabled the characterization of the biological functions of the core genes and provided insight into their roles in gout pathogenesis and the functional characteristics of the key modules.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscription factor activity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003epySCENIC is an updated and Python-based reimplementation of the SCENIC tool, which is widely used for analyzing single-cell RNA sequencing data\u003csup\u003e16\u003c/sup\u003e. \u0026nbsp;SCENIC leverages transcription factors and cis-regulatory sequences to define distinct cell states. This approach allows for the identification of transcription factor combinations that regulate cell type-specific transcriptomes, providing deep insights into the regulatory mechanisms underlying cellular heterogeneity\u003csup\u003e17,18\u003c/sup\u003e.To predict the relationship between transcription factors and their target genes, the GENIE3 algorithm was utilized to train a random forest model, which quantifies the influence of individual transcription factors on gene expression regulation. The resulting transcription factor\u0026ndash;target gene (TF-TG) co-expression network was further validated using RcisTarget, which integrates transcription factor binding site data to provide an additional layer of reliability. The transcriptional activity of individual cells was quantified using the AUCell score, which enabled the identification of cells with significantly elevated transcriptional activity. Finally, the results of the SCENIC analysis were visualized using various graphical tools, allowing for the presentation of transcriptional regulatory networks and the comparison of regulatory patterns across different cell populations or groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrajectory Inference of Single-Cell Differentiation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCytoTRACE and Monocle3 were employed to simulate the differentiation of monocytes into macrophages. CytoTRACE is a computational framework designed to predict cellular differentiation states from single-cell RNA sequencing (scRNA-seq) data\u003csup\u003e19\u003c/sup\u003e. After identifying the differentiation state of each cell using appropriate computational algorithms, Monocle3 applies an unsupervised framework to map cells onto trajectories that accurately reflect the biological differentiation process. This method enables the reconstruction of dynamic cellular states along the differentiation pathway, offering valuable insights into the temporal progression and regulatory mechanisms underlying cell fate determination\u003csup\u003e20\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using R software (version 4.3.2) for data processing. To compare quantitative data across different groups, either a two-tailed unpaired Student\u0026apos;s t-test or one-way analysis of variance (ANOVA) with Tukey\u0026rsquo;s post hoc multiple comparison test was applied, depending on the experimental design. A p-value of \u0026lt;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Identification of Macrophages in gouty arthritis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo investigate the cellular characteristics associated with gouty arthritis, we analyzed the single-cell dataset GSE217561 from the GEO database, which contains PBMC sequencing data from patients in different stages of gout (gout remission, GR, n=3; late gout remission, GS, n=3) and healthy controls (HC, n=3). Additionally, we examined the PRJNA861849 dataset from the NCBI BioProject database, which includes synovial fluid sequencing data from gout patients (SF, n=3). After data integration and quality control, we obtained sequencing data from 117,405 cells, including 14,833 mononuclear phagocytes (MNPs). Compared to PBMCs, synovial fluid exhibited greater cellular heterogeneity, with a significant increase in the proportion of MNPs (Fig. 1A-D).\u003c/p\u003e\n\u003cp\u003eWe further categorized MNPs into four subpopulations: CD14+ monocytes, CD16+ monocytes, dendritic cells (DCs), and macrophages. Notably, the proportion of macrophages was significantly elevated in gout patients, especially in the synovial fluid, compared to the healthy control group. This suggests that macrophages play a crucial role in the pathogenesis of gouty arthritis within the joint microenvironment (Fig. 1E-G).\u003c/p\u003e\n\u003cp\u003eAdditionally, our analysis revealed that macrophages are central in regulating fibroblast proliferation, angiogenesis, endothelial cell proliferation, chemotaxis, and tissue remodeling\u0026mdash;biological processes that are integral to the progression of arthritis (Fig. 1H)\u003csup\u003e21-23\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Identification of module genes in MNPs by hdWGCNA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify key gene co-expression modules and biomarkers associated with gouty arthritis progression, we conducted high-dimensional weighted gene co-expression network analysis (hdWGCNA). A soft threshold of 6 was selected to construct an unweighted primary macrophage network for optimal connectivity (Fig. 2A). A total of 11 gene modules were detected (Fig. 2B and C). Among them, Modules 5 and 6 exhibited the strongest relevance to gout-related biological activities (Fig. 2E-H). Moreover, macrophages were identified as the predominant cell subset involved in these modules (Fig. 2D). Based on these findings, we selected the top 50 genes with the highest k-Module Membership (kME) values from Modules 5 and 6 for further investigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Reconstruction of transcriptional modules related to cell states during gouty arthritis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify the master regulators of gouty arthritis, we constructed transcriptional regulatory networks with transcriptional regulators and their target genes by applying SCENIC (single‐cell regulatory network inference and clustering) analysis. The active transcription factors in synovial fluid are significantly different from those in blood.(Fig 3A) We screened the most active transcription factors in different groups for visualization.(Fig 3B) The top 50 genes corresponding to the top 5 transcription factors in synovial fluid were used to intersect with the top 50 hub genes screened in hdWGCNA, and finally two key molecules were obtained: PPARG and VAV3.(Fig 3D) Since PPARG is a well-established marker for gout\u003csup\u003e24,25\u003c/sup\u003e, while VAV3 has not been reported to have similar functions, we further examined VAV3 expression in MNPs and found it to be highly expressed in macrophages.(Fig 3C)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Pseudotemporal analysis confirmed the trajectory of macrophage differentiation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe subsetted monocytes and macrophages from the MNP data and used CytoTRACE to assess the differentiation potential of monocytes and macrophages in different groups, as well as their individual differentiation trajectories. The CytoTRACE score, which ranges from 0 to 1, reflects stemness, with higher scores indicating lower differentiation potential. Our analysis revealed that the differentiation potential of both monocytes and macrophages was significantly lower in the GS and SF groups compared to the control and GR groups (Fig. 4A). Furthermore, macrophages exhibited markedly lower differentiation potential than monocytes (Fig. 4B).\u003c/p\u003e\n\u003cp\u003eTo build upon these findings, we employed Monocle3 to model the differentiation process from monocytes to macrophages and performed Gene Set Enrichment Analysis (GSEA) on the key driver genes involved (Fig. 4C-E). Our results suggest that the stimulation of cytokines such as IL-1\u0026beta;, TNF, and VEGF contributes to the recruitment of monocytes to the local joint microenvironment, where they differentiate into macrophages, driving the pathogenesis of gouty arthritis. Notably, the expression of VAV3 showed an increasing trend along the pseudo-time trajectory (Fig. 4F).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 VAV3 Is Implicated in Multiple Biological Functions Associated with Gouty Arthritis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the biological functions of macrophages in gouty arthritis, we applied Gene Set Variation Analysis (GSVA) to correlate VAV3 expression with various function scores. The results revealed a positive correlation between VAV3 expression and several arthritis-related biological functions, including limb joint morphogenesis, apical junction, angiogenesis, inflammation, endothelial cell proliferation, and endothelial cell migration (Fig. 5A-F)\u003csup\u003e26-29\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo validate these findings, we examined VAV3 expression across multiple distinct bulk RNA-seq datasets. Our results showed that VAV3 was upregulated in the ankle RNA sequencing data from a mouse model of MSU-induced gouty arthritis (Fig. 5G). Additionally, VAV3 was upregulated in MSU-stimulated bone marrow-derived macrophages (BMDMs) (Fig. 5H). Interestingly, VAV3 was also upregulated in macrophage sequencing data from hyperuricemia nephropathy (Fig. 5I). These findings suggest that VAV3 may not only serve as an expression marker for gouty arthritis but also as a potential marker for gouty nephropathy.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGout is a common and painful condition that often results in debilitating complications, including arthritis and kidney disease \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Both the serum environment, which influences uric acid levels, and the synovial fluid environment, which affects the formation of urate crystals, are crucial in the progression of gouty arthritis \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Given this, we selected single-cell sequencing data from PBMCs and synovial fluid for our investigation into the cellular and molecular mechanisms underlying gouty arthritis.\u003c/p\u003e \u003cp\u003eThe advent of single-cell sequencing technology has opened up new avenues for understanding the intricate biological processes that govern disease initiation and progression. By integrating and comparing single-cell RNA sequencing data from peripheral blood and synovial fluid, we identified macrophages as the predominant cell type involved in the pathogenesis of gouty arthritis. This finding highlights the central role of macrophages in the inflammatory response characteristic of gout.\u003c/p\u003e \u003cp\u003eFunctional enrichment analysis revealed that macrophages are primarily involved in the activation of matrix cells, including mesenchymal and endothelial cells. These processes are pivotal in the pathogenesis of gouty arthritis, as they contribute to tissue remodeling, fibrosis, and inflammation. Interestingly, the cartilage matrix and fibers may provide a conducive environment for the crystallization of monosodium urate (MSU) crystals, which further exacerbates the progression of arthritis \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, by leveraging advanced computational tools such as hdWGCNA and SCENIC, we systematically identified VAV3 as a key molecule potentially involved in the pathological progression of gouty arthritis. To validate this, we examined VAV3 expression across multiple distinct bulk RNA-seq datasets. VAV3 has been implicated in various biological processes, such as angiogenesis, cell migration, and cytoskeletal remodeling \u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Through hdWGCNA, we found that the gene expression module associated with VAV3 was significantly enriched for GO terms related to leukocyte proliferation, leukocyte activation, and the positive regulation of fibrous tissue formation. Collagen released by stromal cells alters the morphology of MSU crystals, promoting their endocytosis by macrophages, which further contributes to the inflammatory response \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. These findings provide a mechanistic insight into how macrophages may exacerbate the pathological processes in gouty arthritis.\u003c/p\u003e \u003cp\u003eOur study also highlighted several key factors that drive macrophage differentiation and, consequently, the progression of arthritis. These include the activation of cytokines such as IL-1β, TNF, TGF-β, and VEGF, all of which have been shown to be upregulated in joint effusions associated with gouty arthritis \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Notably, we observed an increasing trend in VAV3 expression along the pseudo-time trajectory, suggesting that VAV3 plays a crucial role in the differentiation of monocytes into macrophages and in driving the pathogenesis of gouty arthritis.\u003c/p\u003e \u003cp\u003eIn comparison to previous studies, which often focus on isolated aspects of macrophage function or use bulk tissue samples, our study stands out by integrating single-cell sequencing data from PBMCs of patients with different severities of gout and synovial fluid from gout patients. This allowed us to investigate the differentiation trajectory of monocytes into macrophages within the distinct microenvironment of gouty arthritis. Based on this analysis, we identified VAV3 as a novel key molecule involved in the pathogenesis of gouty arthritis. Furthermore, our study suggests that VAV3 may not only serve as an expression marker for gouty arthritis but also as a potential marker for gouty nephropathy, providing new insights into the broader implications of this molecule in gout-related diseases.\u003c/p\u003e \u003cp\u003eIn conclusion, our findings demonstrate that macrophages play a critical role in the pathogenesis of gouty arthritis, and that VAV3 may serve as a potential key diagnostic and therapeutic target for the disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003enot applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by Guangdong Provincial Natural Science Fund (No. 2021A1515111115).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.Z conceived and designed the experiments, analyzed and interpreted the data, and contributed to the writing of the manuscript. L.F analyzed the experimental results and assisted in manuscript preparation and editing. W.W contributed to data acquisition, reviewed relevant literature, provided intellectual input, and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset analyzed in the current study is available in the NCBI Gene Expression Omnibus (GEO) and NCBI BioProject databases, with dataset accession numbers GSE217561 and PRJNA861849, respectively.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDalbeth N, Gosling AL, Gaffo A, Abhishek A. Gout. \u003cem\u003eLancet (London, England)\u003c/em\u003e 2021; \u003cstrong\u003e397\u003c/strong\u003e(10287): 1843-55.\u003c/li\u003e\n\u003cli\u003eDesai J, Steiger S, Anders HJ. 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The Rho protein exchange factor Vav3 regulates vascular smooth muscle cell proliferation and migration. \u003cem\u003eCardiovascular research\u003c/em\u003e 2010; \u003cstrong\u003e86\u003c/strong\u003e(1): 131-40.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gouty arthritis, Macrophages, Single-cell RNA sequencing, Immunology","lastPublishedDoi":"10.21203/rs.3.rs-6054213/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6054213/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Gouty arthritis is a common inflammatory disease triggered by the deposition of monosodium urate (MSU)crystals in the joints, leading to both acute and chronic inflammation. While macrophages have long been implicated in the pathogenesis of gouty arthritis, the exact mechanisms, differentiation conditions, and key molecules involved remain unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Gene Set Enrichment Analysis (GSEA) was used to determine the primary functions of macrophages. High-dimensional weighted gene co-expression network analysis (hdWGCNA), transcription factor activity analysis, and pseudotemporal trajectory analysis were applied to identify VAV3 as a key gene regulating macrophage differentiation. The correlation between VAV3 expression and relevant biological processes was further validated through Gene Set Variation Analysis (GSVA) and by examining VAV3 expression in related bulk RNA sequencing datasets from the GEO database, confirming its association with gouty arthritis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOur analysis indicates that macrophages are a crucial cell type in the synovial fluid microenvironment of gouty arthritis, where their differentiation is influenced by various factors. VAV3 is a key gene regulating macrophage differentiation and function, and its expression is positively correlated with several phenotypic features of disease progression, including angiogenesis and inflammation. The differential expression of VAV3 is validated across multiple RNA sequencing datasets from the GEO database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eOur findings underscore the critical role of macrophages in gouty arthritis and identify VAV3 as a novel biomarker and potential therapeutic target. These results deepen our understanding of the inflammatory microenvironment in gouty arthritis and suggest that VAV3 could have broader implications in other gout-related conditions, such as gouty nephropathy.\u003c/p\u003e","manuscriptTitle":"Single-cell transcriptomic analysis identifies VAV3 as a critical regulator of macrophage function in gouty arthritis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-26 12:53:18","doi":"10.21203/rs.3.rs-6054213/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"37f9dd5b-72f3-450a-96df-c93e17ae854f","owner":[],"postedDate":"February 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-26T12:53:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-26 12:53:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6054213","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6054213","identity":"rs-6054213","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}