High Expression of THY1 in Gallbladder Fibroblasts Promotes the Formation and Progression of Gallstones

High Expression of THY1 in Gallbladder Fibroblasts Promotes the Formation and Progression of Gallstones | 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 High Expression of THY1 in Gallbladder Fibroblasts Promotes the Formation and Progression of Gallstones XINXING WANG, MINGZE MA, LICHAO ZHU, CHUAN QIN, SHUAI SHAO, XIANWEN XU, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7276422/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 THY1 (Thy-1 cell surface antigen) is a glycophosphatidylinositol (GPI)-anchored membrane glycoprotein involved in cell-cell interactions, tissue remodeling, and immune regulation. Initially studied in neural and immune cells, THY1 is increasingly recognized for its roles in inflammatory responses and fibrosis, processes that are central to gallstone formation. Methods We performed multi-omics analyses, including transcriptomics, proteomics, and single-cell sequencing, to investigate changes in the gallbladder during gallstone formation. A gallstone mouse model was established using a lithogenic diet, alongside a THY1 knockdown gallstone mouse model created via sh-RNA, to explore the role of THY1 in this process. Hematoxylin and eosin (HE) staining and quantitative reverse transcription PCR (qRT-PCR) were conducted to assess inflammation levels in THY1 knockdown mice during gallstone formation. Western blotting, immunohistochemistry, and immunofluorescence were employed to evaluate the expression of fibronectin (FN) and collagen I (COL-I), elucidating the role of THY1 in extracellular matrix (ECM) formation during gallstone progression. Additionally, biochemical assays were used to quantify bile acids, phospholipids, cholesterol, and triglycerides, and the cholesterol saturation index (CSI) was calculated to further analyze the biochemical environment. Results THY1 expression was significantly elevated in gallbladder fibroblasts during gallstone formation. Knockdown of THY1 alleviated gallstone formation induced by a lithogenic diet in mice. In THY1 knockdown mice, cholesterol levels in gallbladder bile were significantly reduced, bile acid concentration increased, and the CSI index decreased. Additionally, the expression of inflammatory cytokines in the gallbladders of THY1 knockdown mice was reduced, leading to decreased gallbladder inflammation. ECM formation in the gallbladders of THY1 knockdown mice was also alleviated. Conclusion This study reveals that high expression of THY1 in gallbladder fibroblasts promotes the progression of gallstones by increasing inflammation levels and ECM formation. Gallbladder Gallstones THY1 Cholesterol Fibroblasts ECM Immune inflammation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Cholesterol gallstone disease is one of the most common digestive disorders, affecting 10–15% of adults( 1 , 2 ). The incidence of gallstones is rising rapidly as the living standards of the Chinese population improve. Elevated bile saturation is a significant risk factor for gallstone formation( 1 , 3 ). Fibroblasts are essential for maintaining tissue homeostasis and play diverse roles in inflammatory diseases. They act as inflammatory mediators, recruit leukocytes, promote angiogenesis, and drive chronic inflammation( 4 ). A major source of IL-6, fibroblasts can be activated by cytokines and inflammatory mediators such as TNF, IL-17, IL-1β, LPS, and IFN-α, -β, and -γ, which significantly induce IL-6 expression( 5 ). Additionally, fibroblasts synthesize the extracellular matrix (ECM) of connective tissues, which is critical for maintaining tissue structural integrity( 6 ). Fibroblasts are closely linked to the ECM, contributing to its formation, secretion, and remodeling. They synthesize and secrete collagen, the primary structural protein of the ECM, which imparts strength and elasticity to tissues( 7 ). They also produce matrix metalloproteinases (MMPs), enzymes that degrade ECM components such as collagen, elastin, and proteoglycans, thereby participating in ECM remodeling. To maintain ECM homeostasis and prevent excessive degradation, fibroblasts secrete tissue inhibitors of metalloproteinases (TIMPs), which regulate MMP activity. The supersaturation of cholesterol in bile is a prerequisite for the pathogenesis of cholesterol gallstone disease, although the underlying mechanisms remain incompletely understood. Studies have shown that the intestinal microbiota, particularly Desulfovibrionales, is enriched in patients with gallstones. Fecal transplantation from gallstone patients to gallstone-resistant mouse strains induces gallstone formation. The presence of Desulfovibrionales is associated with increased secondary bile acid production in the cecum, greater bile acid hydrophobicity, and enhanced intestinal cholesterol absorption. These gallstone-prone microbiota modulate bile acid hydrophobicity and promote cholesterol secretion, thereby contributing to gallstone formation( 8 ). Gallstone formation primarily results from the supersaturation and deposition of cholesterol, bile pigments, and other components in the gallbladder. This process can trigger inflammatory reactions and lead to ECM alterations in gallbladder tissues, exacerbating gallstone progression. Given the critical role of fibroblasts in tissue inflammation and ECM remodeling, their involvement in gallstone pathology warrants further investigation. This study aims to explore the specific role of fibroblasts, particularly the expression of the THY1 protein, in the progression of cholesterol gallstone disease. Our experiments demonstrate that elevated THY1 protein expression in fibroblasts plays a crucial role in regulating cholesterol gallstone formation. Furthermore, we elucidate the mechanisms by which THY1 contributes to gallstone formation. Materials and Methods Data collection The raw sequence data (in RAW format) from a proteome sequencing dataset were downloaded from the iProX database (accession: PXD035915). This dataset includes 40 human bile samples, comprising 31 gallstone bile samples and 9 control bile samples (gallbladder polyps or normal bile). RNA-seq data were retrieved from the GSE202479 dataset in the GEO database. For analysis, three normal gallbladders and four gallstone gallbladders from this dataset were selected. Single-cell transcriptome data were obtained from the GSE179524 dataset in the GEO database. Comparative analysis was conducted using tissues from one normal mouse gallbladder and two gallstone mouse gallbladders. Proteomic Analysis MaxQuant was utilized for targeted sample-specific database searches using a streamlined workflow. Peptide Spectrum Matching (PSM) and False Discovery Rates (FDR) for both proteins and modification sites were set to 0.01. Searches were performed in parallel using reduced decoy mode and included contaminant sequences. The match-between-runs function was applied with the following parameters: match ion tolerance window = 0.05; alignment time window = 20 minutes; orientation ion tolerance = 1; and match unrecognized elements set to true. Protein expression was quantified using the Label-Free Quantification (LFQ) module with the following parameters: LFQ minimum ratio count = 1 and labeled minimum ratio count = 1. Proteins with identical peptide sets were automatically grouped into single protein groups. All other unspecified parameters were left at their default settings. The resulting proteinGroups.txt file was filtered using an R script to exclude proteins labeled as "potential contaminants," "reverse," or "identified by site only." Proteins with fewer than two unique peptides were also discarded. Filtered proteinGroups.txt files were further analyzed in R using the proBatch package. Preprocessing, differential expression analysis, and machine learning-based modeling were performed using R packages such as imputeLCMD, limma, and caret. Protein classification models were developed using machine learning algorithms including svmLinear, random forest (rf), naive Bayes, and k-nearest neighbors (knn)( 9 ). We used the pROC package to plot the ROC curves for the four machine-learning-identified specific proteins in the validation set. The area under the curve (AUC) was calculated to evaluate the specificity of these proteins. Protein-Protein Interaction Network A Protein-Protein Interaction (PPI) Network represents the interactions between individual proteins. The STRING database( 10 ) is a widely used resource for searching known proteins and predicting protein-protein interactions. In this study, we utilized the STRING database, specifying "human" as the biological species, to construct a PPI network associated with bile differential proteins of gallstones. A minimum interaction confidence score of 0.400 was applied. The PPI network model was visualized using Cytoscape( 11 ). Additionally, we employed the MCODE (Molecular Complex Detection) algorithm within the cytoHubba( 12 ) plugin to calculate and display scoring metrics for protein clusters. Differentially expressed genes Analysis In order to obtain the differentially expressed genes (DEGs) between the gallstone group and the normal group, we firstly used the R package sva to de-batch the dataset (GSE202479) Subsequently, based on the grouping information in the data, we used the R package limma( 13 ) for differential analysis to obtain differentially expressed genes. Finally, we will merge all DEGs with |logFC| >1 and p.adjust < 0.05 from the differential analysis, and draw volcano plots to display the results using the R package ggplot2, along with heat maps related to significant top 20 DEGs using the R package pheatmap. Enrichment analysis Enrichment analysis was performed using the GSEABase, ClusterProfiler, and org.Hs.eg.db packages in conjunction with the Metascape website.The databases used for enrichment analysis were obtained from Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Enrichment analysis was performed by using the "EnrichGO" function, where pathways with a P-value less than 0.05 were considered significantly enriched. Visualization of the results was done using the "ggplot2" and "ggpubr" software packages. GSEA enrichment analysis Gene Set Enrichment Analysis (GSEA)( 14 ) is a computational method proposed by the Broad Institute to determine whether a predefined set of genes show statistical differences between two biological states, and is commonly used to estimate changes in pathway and biological process activity in samples from expression data sets. To investigate the differences in biological processes between the two sets of samples, we downloaded the reference gene set "c2.cp.v7.2.symbols.gmt" from the MSigDB database( 15 ) based on the gene expression profiling dataset, using the R package " The GSEA method contained in the R package "clusterProfiler" was used for enrichment analysis and visualization of the dataset. The parameters used in this GSEA enrichment analysis were as follows: seed 2020, number of computations 1000, minimum number of genes per gene set 10, maximum number of genes per gene set 500, p-value correction method Benjamini-Hochberg (BH), and significant enrichment filtering criteria p.adjust < 0.05. ssGSEA Immune Infiltration Single sample Gene Set Enrichment Analysis (ssGSEA)( 16 ) estimates the number of specific immune infiltrating cells and the activity of specific immune responses. The algorithm utilized 29 gene sets from published research on tumor immune infiltration, which included various human immune cell subtypes such as CD8 + T cells, dendritic cells, macrophages, and regulatory T cells.Enrichment scores calculated by our analysis of the ssGSEA algorithm in the R package GSVA package were used to represent the level of infiltration of each immune cell type in each sample. Correlations between immune infiltrating cells were determined by Spearman correlation analysis. Single-Cell Transcriptome Data Analysis Single-cell transcriptome data were obtained from the GEO database (GEO accession number: GSE179524; https://www.ncbi.nlm.nih.gov/geo/ ). Quality control was performed in R using standard single-cell data processing workflows. The count matrix was imported using the "Read10X" function from the Seurat package (version 4.0.4) and converted to a dgCMatrix format. The "merge" function was used to integrate individual objects into a single aggregate object, and the "RenameCells" function ensured the uniqueness of all cell labels. Low-quality cells were filtered out based on the following criteria: genes expressed in fewer than three cells were removed, and cells expressing fewer than 200 genes were excluded. A global scaling normalization method ("LogNormalize") was applied to normalize gene expression across cells, with a scaling factor of 10,000. Highly variable genes (n = 2,000) were identified for downstream analysis using the "FindVariableFeatures" function. The "ScaleData" function was applied, with the "vars.to.regress" parameter set to UMI counts and mitochondrial percentage content, to mitigate unwanted variations. Dimensionality reduction was performed using PCA, focusing on highly variable features, and the top 30 principal components were retained for further analysis. Batch effects between samples were corrected using the Harmony method. Cells were visualized and downscaled using the UMAP method. Clustering was conducted based on edge weights between cells, and a shared nearest neighbor graph was generated using the Louvain algorithm implemented in the "FindNeighbors" and "FindClusters" functions. The resolution parameter in "FindClusters" was optimized between 0.1 and 1. Using the "clustree" function, the clustering tree was visualized, and the resolution of 0.9 yielded the most distinct clusters. Potential doublets were removed using the Scrublet algorithm. Cell clusters were annotated by identifying differentially expressed marker genes using the "FindAllMarkers" function. This analysis employed the default non-parametric Wilcoxon rank sum test with Bonferroni correction. Cell identities were assigned based on surface markers and gene annotations from relevant literature and the Cell Classification Database ( https://ngdc.cncb.ac.cn/celltaxonomy/ ). Cell-cell interaction analysis was performed using the CellChat method. Biochemical Index Detection The concentration of total bile acids in bile was measured using the circulating enzyme method (Ningbo Secke Biotechnology Co., LTD). Phospholipid concentration was determined through enzymatic colorimetry, following the manufacturer's instructions for the kit provided by Beijing Jinhao Pharmaceutical Co., LTD. The levels of bile acids (Bile Acid Assay Kit [Colorimetric], ab239702, Abcam, USA), cholesterol (ab65359), and triglycerides (ab65336) in bile and liver were quantified using respective assay kits. The CSI was calculated based on Carey's criticality table(17). Lentivirus Packaging and Production Recombinant vectors were amplified and purified using the EndoFree Midi Plasmid Kit (TIANGEN). The lentiviral packaging system was provided by GeneChem Company (Shanghai, China), which included a virus packaging helper vector (Helper 1.0) and a packaging assistant vector (Helper 2.0). The recombinant vector and packaging vectors were co-transfected into 293T cells, and the viral solution was harvested 48–72 hours post-transfection. The 293T cells were subsequently cultured in puromycin-supplemented DMEM. Before injecting the viral solution into mice via the tail vein, it was filtered and purified. The viral titer was determined using fluorimetry. Real-Time Fluorescence PCR PowerUp SYBR Green Master Mix (Applied Biosystems) was utilized for real-time fluorescence quantitative PCR (qPCR) to assess target gene expression. Relative mRNA expression levels were quantified using the ΔΔCt method, with GAPDH as the internal control. Primer sequences are provided in Table 1. THY1 expression recombinant vector construction First, we searched the NCBI database for the THY1 sequence to determine its genomic location and nucleotide composition. Primers were designed using an online primer design tool, incorporating restriction endonuclease recognition sites. The primer sequences for THY1 were THY1-P1: CCAGGGCTGCTTCTGATTATT and THY1-P2: TGTCCCATCACCATGTATAAT. The GV369 vector was employed for lentiviral vector construction, containing elements such as Ubi, MCS, SV40, EGFP, IRES, and puromycin in sequence. AgeI and NheI were selected as the restriction enzyme sites. Detailed information and a plasmid map of the GV369 vector are available in the related literature ( 13 ). The target gene was amplified via PCR, and the vector was linearized using restriction enzymes (NEB). Recombination was carried out using T4 DNA ligase, and the recombinant vector was transformed into E. coli. Positive clones were selected, and recombinant plasmids were extracted. After verification through restriction enzyme digestion and sequencing, the recombinant plasmids were confirmed to be correctly constructed. Western Blot Assay Total protein was extracted from gallbladder tissue using lysis buffer (Beyotime, Shanghai, China). The suspension was collected, lysed on ice, and centrifuged at 12,000 × g for 30 minutes at 4°C. Protein concentration was measured using a NanoDrop spectrophotometer. After quantification, 50 µg of total protein was loaded onto an SDS-PAGE gel, separated by electrophoresis, and subsequently transferred onto a PVDF membrane. The membrane was blocked and incubated with primary antibodies overnight at 4°C. The following day, it was incubated with secondary antibodies, washed, and protein bands were visualized. Immunoblot analysis was performed using the following primary antibodies: anti-fibronectin (FN) (1:1000, ab2413, Abcam, Cambridge, UK), anti-collagen I (COL-I) (1:500, sc-59772, Santa Cruz, Dallas, TX, USA), matrix metalloproteinase 3 (MMP3) (1:1000, GB11131-100, Servicebio, China), and matrix metalloproteinase 10 (MMP10) (1:1000, GB112994-100, Servicebio, China). Secondary antibodies included horseradish peroxidase-conjugated goat anti-rabbit and goat anti-mouse antibodies (1:10,000, Wuhan, China). GAPDH (1:1000, GB15004-100, Servicebio, China) served as the internal control. Images were quantified using ImageJ software. Animal Gallstone Model Construction Female C57BL/6 mice (weighing 18–20 g) were used for animal model construction. Ten mice were assigned to the control group and fed standard chow (0.02% cholesterol content) for seven weeks. Twenty mice were assigned to the model group and fed a lithogenic diet (15% fat, 2% cholesterol, and 1% bile acid) for seven weeks. The model group was further divided into a simple model group and a THY1-treated group. Mice in the THY1-treated group received a tail vein injection of THY1 lentivirus four weeks after the initiation of the lithogenic diet. Characteristics of the mice, including body weight, food intake, movement, and mental state, were recorded weekly. All mice were anesthetized with isoflurane administered via a gas anesthesia machine at a concentration of 1–3%. Isoflurane was chosen due to its advantages of rapid onset, easy control of anesthesia depth, and quick recovery, ensuring animal welfare during the experiment. Mice were sacrificed by cervical dislocation, and their gallbladders, bile, and sphincters were collected for analysis. Immunofluorescence and Immunohistochemistry Three-micrometer-thick paraffin-embedded kidney tissue sections were prepared. The sections were baked, dewaxed, hydrated, and blocked with 5% bovine serum albumin for 2 hours. Subsequent steps were carried out according to the manufacturer's instructions (Maixin, Fuzhou, China). Tissue sections were incubated with primary antibodies against FN and COL-I, followed by nuclear staining and sealing after incubation with secondary antibodies. The immunostained samples were observed, and images were captured using fluorescence microscopy. Statistical Analysis All data processing and analysis were conducted using R software (Version 4.2.1). Continuous variables were expressed as mean ± standard deviation, and comparisons between two groups were performed using the Wilcoxon Rank Sum Test. For categorical variables, statistical significance was determined using either the chi-square test or Fisher's exact test, depending on the distribution and sample size. Spearman correlation analysis was employed to calculate correlation coefficients between molecular variables, unless otherwise specified. A p-value of < 0.05 was considered statistically significant across all analyses. Results Proteomic Analysis of Human Bile from Patients with and without Gallstones We conducted a proteomic analysis of human bile samples from patients with gallstones and controls without gallstones. For the analysis, we utilized the MaxQuant secondary database. MaxQuant identified 23,033 peptides, which were further analyzed using categorical Latent Class Analysis (LCA) in Metalab. This process ultimately identified 2,749 human proteins. The data were interpolated and corrected across all samples using the proMod package, followed by differential analysis (Fig. 1 A, B). Protein-protein interaction (PPI) analysis was performed on 172 differentially expressed proteins using the STRING database. A PPI network for these proteins was constructed and visualized using Cytoscape software (Fig. 1 C). Additionally, the cytoHubba plug-in within Cytoscape was used to calculate differential protein scores based on the MCODE algorithm. To evaluate the diagnostic potential of the differential proteins for gallstones, we selected the top 20 proteins (Fig. 1 D). Diagnostic models were developed using four machine learning algorithms: svmLinear, random forest (rf), naive Bayes (naive_bayes), and k-nearest neighbors (knn) (Fig. 1 . E-F). As shown in Fig. 1 . E-F, screening with these algorithms identified 12 proteins (C4BPA, MANBA, SNX18, ABCBB, LYAG, ENPP3, THY1, SCTM1, TARSH, NTRI, F151A, and HOME2) with importance scores greater than 60. The ROC plot (Fig. 1 G) demonstrated that these 12 proteins possess significant diagnostic value for gallstones. Gallbladder Transcriptome Analysis in Patients with and without Gallstones To investigate differences in gene expression between the control (Normal) group and the gallstone group in the merged dataset, we conducted differential gene expression analysis using the R package limma. A total of 1,089 genes met the criteria for differential expression, defined as |logFC| >2 and p.adjust 2, p.adjust < 0.05) and 389 were downregulated (logFC < -2, p.adjust < 0.05) (Figs. 2 A-B). Gene Ontology (GO) enrichment analysis revealed that upregulated DEGs were primarily enriched in processes such as immune cell migration, inflammatory response, and immune effector activity. In contrast, downregulated DEGs were enriched in pathways related to ion stress responses (Fig. 2 C). KEGG enrichment analysis indicated that DEGs were associated with pathways involving cytokine signaling, immune responses, and extracellular matrix (ECM) formation. To assess the impact of gene expression on gallstone occurrence, we performed Gene Set Enrichment Analysis (GSEA) to explore biological processes, cellular components, and molecular functions. The results (Fig. 2 E) indicated significant enrichment of genes in pathways such as CHEMOKINE_SIGNALING, CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION, INTESTINAL_IMMUNE_NETWORK, PRIMARY_IMMUNODEFICIENCY, and SYSTEMIC_LUPUS_ERYTHEMATOSUS. Single-cell annotation of gallbladder cells from normal and gallstone-bearing mice identified six main cell types: epithelial, proliferative, endothelial, fibroblast, macrophage, and smooth muscle cells, with epithelial cells being the predominant type (Fig. 2 F, Fig. 3 A, C). Analysis of intercellular communication demonstrated that fibroblasts exhibited substantial interactions with other cell types (Fig. 3 D). Multi-Omics Analysis Reveals THY1 as an Important Gene for Gallstones We performed separate analyses of the proteome, transcriptome, and single-cell datasets to identify differences and intersecting differential genes (Fig. 4 A). Among these genes, THY1 was identified as a common intersecting gene. Single-cell analysis showed that THY1 was primarily expressed in fibroblasts in the gallstones group (Fig. 3 B, and Fig. 4 B-C). Previous studies have demonstrated that gallstones are closely associated with immune and inflammatory responses. To explore whether elevated THY1 expression is also linked to immune effects, we calculated the infiltration levels of 29 immune cell types across different transcriptome samples using the ssGSEA algorithm. The results were visualized using stacked bar graphs (Fig. 4 D). This analysis revealed significant differences in the infiltration levels of pro-inflammatory cells, MHC class I cells, NK cells, and T cell co-stimulatory cells, suggesting that THY1 is associated with multiple immune cell populations (Fig. 4 E-F). To further investigate, we induced gallstone formation in mice by feeding them a lithogenic diet (15% fat, 2% cholesterol, 1% bile acid) for seven weeks. Gallbladders were then collected from both control and experimental groups, and qRT-PCR was performed to validate THY1 expression levels (Fig. 4 G). Knockdown of THY1 Reduces Gallstones Using the established gallstone animal model, we employed AAV virus-packaged sh-THY1 to knock down THY1 expression in mice with gallstones. Our results showed that reducing THY1 expression significantly decreased the extent of gallstones in mice (Fig. 5 . A-C). Analysis of bile composition revealed elevated cholesterol molar levels and cholesterol saturation index (CSI) in the gallstone group, both of which were significantly reduced in the THY1 knockdown group. Furthermore, bile acid levels were higher in the THY1 knockdown group compared to the gallstone group (Fig. 5 D-F). Knockdown of THY1 Reduces Inflammation Levels in Gallstone Mice To evaluate the effect of THY1 knockdown on inflammation levels in gallstone mice, our results showed significant reductions in pro-inflammatory cytokines, including IL-6, TNF-α, IL-1β, and IL-8, in the THY1 knockdown group compared to the untreated gallstone group by PCR. However, IL-4 levels did not exhibit significant changes (Fig. 6 B-F). Knockdown of THY1 Alleviates ECM Formation in Gallstone Gallbladders Fibronectin (FN) and collagen type I (COL-I) are well-established key components of extracellular matrix (ECM) formation. In our study, immunofluorescence analysis of gallbladder tissues showed significantly increased fluorescence intensity of FN and COL-I in the gallstone group compared to the non-gallstone group, indicating alterations in ECM composition and structure that may impair gallbladder physiological function. In contrast, the fluorescence intensity of FN and COL-I was notably lower in the THY1 knockdown group compared to the gallstone group, suggesting that THY1 knockdown mitigates ECM formation during gallstone formation (Fig. 6 A, Fig. 7 ). Western blotting and immunohistochemistry further confirmed reduced expression levels of FN and COL-I in the THY1 knockdown group relative to the gallstone group. Additionally, Western blot analysis revealed a significant decrease in the expression of ECM-degrading enzymes, MMP-3 and MMP-10 shown in Fig. 7 , in the gallstone group. This decrease was partially restored upon THY1 knockdown. Discussion Gallstone disease is a prevalent condition worldwide, affecting 5–15% of adults( 18 , 19 ). Recent studies have highlighted the critical role of inflammation and immune responses in gallstone pathogenesis. Chronic inflammation in the gallbladder disrupts the local immune microenvironment, leading to bile stasis, epithelial injury, and extracellular matrix (ECM) remodeling. Inflammatory cytokines such as IL-6, TNF-α, and IL-1β are known to contribute to the initiation and progression of gallstones by promoting cholesterol crystallization and mucin production( 20 , 21 ). Furthermore, immune cell infiltration, including macrophages, T cells, and neutrophils, has been observed in gallstone-afflicted gallbladders, indicating a strong link between immune activation and gallstone formation( 22 ). This inflammatory milieu not only facilitates gallstone development but also exacerbates gallbladder dysfunction, fibrosis, and the risk of secondary complications such as cholecystitis and gallbladder cancer( 23 ). The pathogenesis of cholesterol gallstones is multifactorial, involving genetic factors such as the LITH gene, hepatic overproduction of cholesterol leading to bile oversaturation, impaired gallbladder motility, mucin gel accumulation within the gallbladder lumen, and immune-mediated gallbladder inflammation( 24 ). Cholesterol stones are also associated with obesity, which may alter bile cholesterol concentrations and promote gallstone formation. Inflammation is a key driver of gallstone development, as histopathologic changes in the gallbladder wall often precede cholesterol gallstone formation, as evidenced in both animal models and human studies( 25 , 26 ). Additionally, findings from the Danish WHO Multinational Vascular Disease Trends and Determinants Study highlighted a strong association between gallstone disease and factors such as insulin resistance, systemic inflammation, and genetic predisposition to obesity or type 2 diabetes( 27 ). In this study, we identified THY1 as a critical driver of gallstone progression, mediated through inflammatory factors and ECM formation. Using limma differential analysis and machine learning algorithms (svmLinear, rf, naive_bayes, and knn), we screened 12 key proteins from bile proteomic data of patients with and without gallstones. ROC curve analysis demonstrated excellent predictive accuracy for these proteins. Transcriptomic and single-cell analyses further confirmed that THY1 was consistently overexpressed in gallstone samples, with single-cell analysis localizing THY1 expression specifically to gallbladder fibroblasts. Moreover, ssGSEA immuno-infiltration analysis showed a strong correlation between THY1 expression and immune cell infiltration, highlighting its significant role in gallstone progression. Mechanistic studies revealed that THY1 promotes gallstone progression by influencing inflammatory responses and ECM formation. In a gallstone mouse model, we observed high THY1 expression in the gallbladder using qRT-PCR. Knockdown of THY1 via sh-RNA significantly reduced gallstone severity, evidenced by a decrease in the number of gallstones. Analysis of bile composition further revealed that THY1 knockdown improved the biliary lipid profile, including reductions in total bile salt content, cholesterol saturation index (CSI), and levels of bile acids, phospholipids, cholesterol, and triglycerides. Gallstone formation is a multifactorial process influenced by numerous factors, with inflammation playing a pivotal role( 21 , 22 ). Inflammation disrupts protein and lipid metabolism, impacting cholesterol and bile acid pathways, and can elevate bile salt levels, contributing to gallstone formation. Previous studies have linked gallstones to cytokines such as IL-6, IL-10, IL-12, and IL-13( 20 ). IL-6, expressed by the biliary epithelium, induces inflammatory cell infiltration and increases gallbladder wall thickness( 21 , 25 ). Consistent with this, our study found reduced levels of inflammatory factors, including IL-6, IL-1β, and TNF-α, in THY1 knockdown mice. Since fibroblasts are primary producers of IL-6, our findings suggest that THY1 knockdown mitigates inflammation by reducing IL-6 secretion from fibroblasts, thereby attenuating gallstone progression. THY1, a GPI-anchored protein with an RLD structural domain resembling the RGD integrin-binding sequence, facilitates binding of lipid rafts to αvβ3 and αvβ5 integrins( 22 ). Previous studies have shown that Thy-1 deletion in fibroblasts disrupts mechanotransduction and rigidity sensing in pulmonary fibrosis. Similarly, increased expression of ECM components, such as fibronectin (FN) and collagen type I (COL-I), in gallstone-afflicted gallbladders contributes to tissue sclerosis, thickening, and potentially gallbladder fibrosis, exacerbating gallstone progression. In gallstone-afflicted tissues, the expression of ECM-degrading enzymes, such as MMP-3, is significantly reduced. Conversely, our study demonstrated decreased expression of FN and COL-I and increased levels of ECM-degrading enzymes in THY1 knockdown mice, confirmed through Western blotting, immunofluorescence, and immunohistochemistry. This aligns with findings in cancer-associated fibroblasts (CAFs), where high Thy-1 expression promotes ECM remodeling( 23 , 28 ). These findings suggest a role for Thy-1 in regulating ECM dynamics in gallstone pathogenesis. Conclusion This study integrated proteomics, transcriptomics, and single-cell analyses to investigate changes in the gallbladder during gallstone disease. Our findings identified THY1 as a key contributor to gallstone pathogenesis, influencing inflammatory factor levels and ECM formation, two critical elements in gallstone progression. However, this study has limitations. The data were primarily derived from publicly available databases, necessitating further validation in clinical settings. Additionally, gallstones represent a multifactorial disease entity. While our findings suggest that reducing THY1 expression holds therapeutic potential, further research is needed to comprehensively understand the underlying mechanisms and develop targeted therapeutic strategies. Declarations Conflicts of Interest The authors declare that they have no competing interest. Ethic and approval statement All patients provided written informed consent before enrollment, and this study was approved by the Research Ethics Committee of Shandong Provincial Hospital Affiliated to Shandong First Medical University. All animal experiments involved in this study were approved by the Shandong Provincial Hospital Affiliated to Shandong First Medical University (approval number: 2019A56YB). All experimental methods were performed in accordance with relevant guidelines and regulations, including the ARRIVE guidelines ( https://arriveguidelines.org ). All experiments followed ethical and regulatory requirements for animal use. Funding The research was funded by Natural Science Foundation of Shandong Province (No.ZR2021QH186); National Natural Science Foundation of China (No.81870205); Chen Xiao-Ping Foundation for the Development of Science and Technology of Hubei Province (No.CXPJJH12000001-2020304); Foundation research project of Qinghai province ((No.2021-ZJ-719);National Natural Science Foundation of China (No. 82000579). Author Contribution Conceptualization, resources and project administration, X.X.W., Z.H.Z.and M.Z.M.; methodology and software, S.S., X.W.X., C.Q.and R.X.G; validation and formal analysis, M.Z.M., S.S. and X.W.X; investigation, C.Q.and R.X.G; data curation and visualization, Z.H.Z.and M.Z.M.; writing-original draft preparation, X.W.X., C.Q.and R.X.G.; writing-review and editing, X.X.W.and Z.H.Z. supervision, X.X.W.; funding acquisition, Z.H.Z. All authors have read and agreed to the published version of the manuscript. Acknowledgments None. Data Availability Statement The data that supports the findings of this study are available within the article. The original data can be obtained by sending an email to the corresponding author. References Portincasa P, Moschetta A and Palasciano G: Cholesterol gallstone disease. Lancet 368: 230–239, 2006. Chen Y, Kong J and Wu S: Cholesterol gallstone disease: focusing on the role of gallbladder. Lab Invest 95: 124–131, 2015. Gaby AR: Nutritional approaches to prevention and treatment of gallstones. Altern Med Rev 14: 258–267, 2009. Wei K, Nguyen HN and Brenner MB: Fibroblast pathology in inflammatory diseases. J Clin Invest 131: e149538, 2021. Nguyen HN, Noss EH, Mizoguchi F, Huppertz C, Wei KS, Watts G and Brenner MB: Autocrine Loop Involving IL-6 Family Member LIF, LIF Receptor, and STAT4 Drives Sustained Fibroblast Production of Inflammatory Mediators. Immunity 46: 220–232, 2017. Lynch MD and Watt FM: Fibroblast heterogeneity: implications for human disease. J Clin Invest 128: 26–35, 2018. Karsdal MA, Nielsen SH, Leeming DJ, Langholm LL, Nielsen MJ, Manon-Jensen T, Siebuhr A, Gudmann NS, Rønnow S, Sand JM, et al : The good and the bad collagens of fibrosis - Their role in signaling and organ function. Adv Drug Deliv Rev 121: 43–56, 2017. Hu H, Shao W, Liu Q, Liu N, Wang Q, Xu J, Zhang X, Weng Z, Lu Q, Jiao L, et al : Gut microbiota promotes cholesterol gallstone formation by modulating bile acid composition and biliary cholesterol secretion. Nat Commun 13: 252, 2022. Ranathunge C, Patel SS, Pinky L, Correll VL, Chen S, Semmes OJ, Armstrong RK, Combs CD and Nyalwidhe JO: promor: a comprehensive R package for label-free proteomics data analysis and predictive modeling. Bioinform Adv 3: vbad025, 2023. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, et al : STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47: D607-D613, 2019. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13: 2498–2504, 2003. Chin CH, Chen SH, Wu HH, Ho CW, Ko MT and Lin CY: cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8 Suppl 4: S11, 2014. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W and Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43: e47, 2015. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al : Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102: 15545–15550, 2005. Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP and Tamayo P: The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst 1: 417–425, 2015. Xiao B, Liu L, Li A, Xiang C, Wang P, Li H and Xiao T: Identification and Verification of Immune-Related Gene Prognostic Signature Based on ssGSEA for Osteosarcoma. Front Oncol 10: 607622, 2020. Carey MC: Critical tables for calculating the cholesterol saturation of native bile. J Lipid Res 19: 945–955, 1978. Everhart JE, Khare M, Hill M and Maurer KR: Prevalence and ethnic differences in gallbladder disease in the United States. Gastroenterology 117: 632–639, 1999. Chen CH, Huang MH, Yang JC, Nien CK, Etheredge GD, Yang CC, Yeh YH, Wu HS, Chou DA and Yueh SK: Prevalence and risk factors of gallstone disease in an adult population of Taiwan: an epidemiological survey. J Gastroenterol Hepatol 21: 1737–1743, 2006. Liu Z, Kemp TJ, Gao YT, Corbel A, McGee EE, Wang B, Shen MC, Rashid A, Hsing AW, Hildesheim A, et al : Association of circulating inflammation proteins and gallstone disease. J Gastroenterol Hepatol 33: 1920–1924, 2018. Iglesias M, Plowman GD and Woodworth CD: Interleukin-6 and interleukin-6 soluble receptor regulate proliferation of normal, human papillomavirus-immortalized, and carcinoma-derived cervical cells in vitro. Am J Pathol 146: 944–952, 1995. Zhou Y, Hagood JS, Lu B, Merryman WD and Murphy-Ullrich JE: Thy-1-integrin alphav beta5 interactions inhibit lung fibroblast contraction-induced latent transforming growth factor-beta1 activation and myofibroblast differentiation. J Biol Chem 285: 22382–22393, 2010. Calvo F, Ege N, Grande-Garcia A, Hooper S, Jenkins RP, Chaudhry SI, Harrington K, Williamson P, Moeendarbary E, Charras G, et al : Mechanotransduction and YAP-dependent matrix remodelling is required for the generation and maintenance of cancer-associated fibroblasts. Nat Cell Biol 15: 637–646, 2013. Lammert F, Gurusamy K, Ko CW, Miquel JF, Méndez-Sánchez N, Portincasa P, van Erpecum KJ, van Laarhoven CJ and Wang DQ: Gallstones. Nat Rev Dis Primers 2: 16024, 2016. van Erpecum KJ, Wang DQ, Moschetta A, Ferri D, Svelto M, Portincasa P, Hendrickx JJ, Schipper M and Calamita G: Gallbladder histopathology during murine gallstone formation: relation to motility and concentrating function. J Lipid Res 47: 32–41, 2006. Maurer KJ, Carey MC and Fox JG: Roles of infection, inflammation, and the immune system in cholesterol gallstone formation. Gastroenterology 136: 425–440, 2009. Shabanzadeh DM, Skaaby T, Sørensen LT, Eugen-Olsen J and Jørgensen T: Metabolic biomarkers and gallstone disease - a population-based study. Scand J Gastroenterol 52: 1270–1277, 2017. Saalbach A, Wetzel A, Haustein UF, Sticherling M, Simon JC and Anderegg U: Interaction of human Thy-1 (CD 90) with the integrin alphavbeta3 (CD51/CD61): an important mechanism mediating melanoma cell adhesion to activated endothelium. Oncogene 24: 4710–4720, 2005. Additional Declarations No competing interests reported. Supplementary Files suppl.docx 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. 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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-7276422","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498255487,"identity":"393be1bb-453d-4a26-b3b1-f1a858b98cf6","order_by":0,"name":"XINXING WANG","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"XINXING","middleName":"","lastName":"WANG","suffix":""},{"id":498255488,"identity":"9434cd43-7214-4f02-ad49-123fdd972eff","order_by":1,"name":"MINGZE MA","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"MINGZE","middleName":"","lastName":"MA","suffix":""},{"id":498255489,"identity":"c3f7c203-62a0-4f13-90ee-fcd50ea69b6c","order_by":2,"name":"LICHAO ZHU","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"LICHAO","middleName":"","lastName":"ZHU","suffix":""},{"id":498255490,"identity":"b25fc059-4020-42c6-a76e-05296b95212f","order_by":3,"name":"CHUAN QIN","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"CHUAN","middleName":"","lastName":"QIN","suffix":""},{"id":498255491,"identity":"a038f7d7-68c1-4315-8771-ed69aacfdcc4","order_by":4,"name":"SHUAI SHAO","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"SHUAI","middleName":"","lastName":"SHAO","suffix":""},{"id":498255492,"identity":"33ac493e-55ac-4903-84c0-19af5d8cf626","order_by":5,"name":"XIANWEN XU","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"XIANWEN","middleName":"","lastName":"XU","suffix":""},{"id":498255493,"identity":"7b0760fc-cf35-4a45-83f7-8c6096927457","order_by":6,"name":"RUXIN GAO","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"RUXIN","middleName":"","lastName":"GAO","suffix":""},{"id":498255494,"identity":"ca873c2c-bbc2-4019-8170-cf7b27638130","order_by":7,"name":"ZHENHAI ZHANG","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYDACCTB5AMw48IGBmR/M5yFWy8EZDMySDSRpYeYhRov87OZnD7/8uSNnLt388LDNH2sJ3RkJjA/etjHIm+PQwjjnmLmxDM8zY8s5xwwO57alS5jdSGA2nNvGYLizAbsWZokEM2kJicOJG24kALU0HK4DamGT5m1jSDA4gF0Lm0T6N2kJg8P1G26kfzhs8ecwyBb23/i08EjkmEl+SDicYHAjx+AwAxtYCxszPi0SEjll0gwHDhtuuJFTcLAX5JczD5sl55yTMNyAQ4v8jPRtkj/+HJY3uJG++cMPYIiZHU8++OFNmY08LlvAQYAWC4wNDLD4wgUYf+CVHgWjYBSMghEPAMR/YL1DkoF8AAAAAElFTkSuQmCC","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":true,"prefix":"","firstName":"ZHENHAI","middleName":"","lastName":"ZHANG","suffix":""}],"badges":[],"createdAt":"2025-08-02 07:38:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7276422/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7276422/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88902925,"identity":"59d3084b-d4d6-4ffa-96a6-dc18d79f9b18","added_by":"auto","created_at":"2025-08-12 13:59:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":893911,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. Volcano plot of differentially expressed proteins (DEPs) between bile samples from the gallstone group and the control group. \u003cstrong\u003eB\u003c/strong\u003e. Heatmap depicting the expression profiles of DEPs between bile samples from the gallstone group and the control group. \u003cstrong\u003eC.\u003c/strong\u003e Protein-protein interaction (PPI) network of DEPs. \u003cstrong\u003eD\u003c/strong\u003e. Box plot illustrating the expression levels of the top 20 proteins among DEPs. \u003cstrong\u003eE.\u003c/strong\u003e Importance scores of differentially expressed proteins determined by machine learning algorithms including \"svmLinear\", \"rf\", \"naive_bayes\", and \"knn\". \u003cstrong\u003eF\u003c/strong\u003e. Accuracy and Kappa coefficients validating the accuracy of machine learning predictions. \u003cstrong\u003eG\u003c/strong\u003e. Receiver operating characteristic (ROC) curves. DEPs: differentially expressed proteins. The accuracy of predictions is lower when the area under the curve (AUC) is between 0.5 and 0.7, moderately accurate when AUC is between 0.7 and 0.9, and highly accurate when AUC is above 0.9.\u003c/p\u003e","description":"","filename":"figs1.png","url":"https://assets-eu.researchsquare.com/files/rs-7276422/v1/8756bfa8045f11f6173cfede.png"},{"id":88902927,"identity":"373ff231-3057-43a1-ac51-40fe17835ed4","added_by":"auto","created_at":"2025-08-12 13:59:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":393223,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. Volcano plot depicting differentially expressed genes (DEGs) between gallstone gallbladders and control group gallbladders. \u003cstrong\u003eB\u003c/strong\u003e. Heatmap illustrating the expression profiles of DEGs between gallstone gallbladders and control group gallbladders. \u003cstrong\u003eC\u003c/strong\u003e. Bar graph presenting the results of Gene Ontology (GO) functional enrichment analysis of DEGs. \u003cstrong\u003eD\u003c/strong\u003e. Dot plot displaying the results of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. \u003cstrong\u003eE\u003c/strong\u003e. Significant enrichment of genes in the gallstone gallbladder and control group gallbladder datasets in CHEMOKINE_SIGNALING, CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION, INTESTINAL_IMMUNE_NETWORK, PRIMARY_IMMUNODEFICIENCY, and SYSTEMIC_LUPUS_ERYTHEMATOSUS. \u003cstrong\u003eF\u003c/strong\u003e. Marker genes for each cluster after dimensionality reduction and clustering of single-cell sequencing data.\u003c/p\u003e","description":"","filename":"figs2.png","url":"https://assets-eu.researchsquare.com/files/rs-7276422/v1/ba67007aab8e170680dd1dcf.png"},{"id":88901931,"identity":"5e00e694-5712-4df3-8f47-167482c5298e","added_by":"auto","created_at":"2025-08-12 13:51:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":608337,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. T-distributed stochastic neighbor embedding (tSNE) clustering plot based on cell type annotations. \u003cstrong\u003eB. \u003c/strong\u003eT-distributed stochastic neighbor embedding (tSNE) clustering plot based on THY1 expression.\u003cstrong\u003e C\u003c/strong\u003e. Bar graph depicting cell proportions based on cell types. \u003cstrong\u003eD\u003c/strong\u003e. Cell-cell interactions among different cell types.\u003c/p\u003e","description":"","filename":"figs3.png","url":"https://assets-eu.researchsquare.com/files/rs-7276422/v1/96cc0f42eb5ed3a5d93f9eba.png"},{"id":88901936,"identity":"09ebde78-951f-40f0-a63e-9b8dbda30ad5","added_by":"auto","created_at":"2025-08-12 13:51:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":385092,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. Venn diagram showing the intersection of differentially expressed genes (DEGs) in the transcriptome, differentially expressed proteins (DEPs) in the proteome, and DEGs in the single-cell dataset. \u003cstrong\u003eB\u003c/strong\u003e. Dot plot illustrating the expression levels of THY1 across different groups. \u003cstrong\u003eC.\u003c/strong\u003eDot plot displaying the expression levels of THY1 across different cell types. \u003cstrong\u003eD. \u003c/strong\u003eStacked bar plot showing the distribution of 29 immune cell types across samples in the dataset, where bars of different colors represent different immune cell types. \u003cstrong\u003eE.\u003c/strong\u003e Lollipop plot depicting the correlation between immune cells and THY1. \u003cstrong\u003eF.\u003c/strong\u003e Comparison of gene expression levels of different immune cell types between two groups, with the x-axis representing cells and the y-axis representing infiltration levels. \u003cstrong\u003eG\u003c/strong\u003e. mRNA expression of THY1 in gallstone mouse gallbladders compared to control mouse gallbladders. ***p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"figs4.png","url":"https://assets-eu.researchsquare.com/files/rs-7276422/v1/70c87884909a68088b16edd1.png"},{"id":88901933,"identity":"8fc901a8-fcc5-4cf6-89c2-d2ea700b8f14","added_by":"auto","created_at":"2025-08-12 13:51:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1701467,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. Comparison of liver and gallbladder conditions between control mice, gallstone mice, THY1 knockdown control mice, and THY1 knockdown gallstone mice. \u003cstrong\u003eB\u003c/strong\u003e. mRNA expression of THY1 in the gallbladders of four mouse groups. \u003cstrong\u003eC.\u003c/strong\u003e Gallstones in the gallbladders of THY1 knockdown gallstone mice compared to gallstone mice. \u003cstrong\u003eD-F\u003c/strong\u003e. Levels of cholesterol, bile acids, and cholesterol saturation index (CSI) in gallbladder bile. ***p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"figs5.png","url":"https://assets-eu.researchsquare.com/files/rs-7276422/v1/74207dd499c9e781804393f7.png"},{"id":88901934,"identity":"da150ed6-a64e-4f50-8678-6284a79298bb","added_by":"auto","created_at":"2025-08-12 13:51:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1392853,"visible":true,"origin":"","legend":"\u003cp\u003eA. HE and IHC staining of gallbladders from the animal model. B-F. mRNA expression levels of IL6, IL1β, TNF-α, IL8, and IL4 in the gallbladders of the four mouse groups. ns indicates p \u0026gt; 0.05, * indicates p \u0026lt; 0.05.*** indicates p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"figs6.png","url":"https://assets-eu.researchsquare.com/files/rs-7276422/v1/5611a1dc651a55bca1b477ea.png"},{"id":88902930,"identity":"879a69b7-35c1-4e9f-a30a-fb572a9bd79a","added_by":"auto","created_at":"2025-08-12 13:59:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1132878,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. Immunofluorescence staining of FN and COL-I in gallbladder tissues from the animal model. \u003cstrong\u003eB-F\u003c/strong\u003e. Western blot analysis of FN, COL-I, MMP3, and MMP10 expression in gallbladder tissues from the animal model. \u003cstrong\u003eG\u003c/strong\u003e. Immunohistochemical staining of FN and COL-I in gallbladder tissues from the animal model. * indicates p \u0026lt; 0.05,; ** indicates p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"figs7.png","url":"https://assets-eu.researchsquare.com/files/rs-7276422/v1/a2fe0aa255ff69e279baa876.png"},{"id":91670859,"identity":"cd853011-459e-4989-a9d2-8c5428660704","added_by":"auto","created_at":"2025-09-19 03:32:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7390888,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7276422/v1/c8cc87d0-49ae-47a4-ad02-3cdb77dfcde3.pdf"},{"id":88901930,"identity":"18339d44-069c-460f-96b2-6f2707057737","added_by":"auto","created_at":"2025-08-12 13:51:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12100,"visible":true,"origin":"","legend":"","description":"","filename":"suppl.docx","url":"https://assets-eu.researchsquare.com/files/rs-7276422/v1/9e64b907f7a77fef839d4c19.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eHigh Expression of THY1 in Gallbladder Fibroblasts Promotes the Formation and Progression of Gallstones\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCholesterol gallstone disease is one of the most common digestive disorders, affecting 10\u0026ndash;15% of adults(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The incidence of gallstones is rising rapidly as the living standards of the Chinese population improve. Elevated bile saturation is a significant risk factor for gallstone formation(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFibroblasts are essential for maintaining tissue homeostasis and play diverse roles in inflammatory diseases. They act as inflammatory mediators, recruit leukocytes, promote angiogenesis, and drive chronic inflammation(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). A major source of IL-6, fibroblasts can be activated by cytokines and inflammatory mediators such as TNF, IL-17, IL-1β, LPS, and IFN-α, -β, and -γ, which significantly induce IL-6 expression(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Additionally, fibroblasts synthesize the extracellular matrix (ECM) of connective tissues, which is critical for maintaining tissue structural integrity(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Fibroblasts are closely linked to the ECM, contributing to its formation, secretion, and remodeling. They synthesize and secrete collagen, the primary structural protein of the ECM, which imparts strength and elasticity to tissues(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). They also produce matrix metalloproteinases (MMPs), enzymes that degrade ECM components such as collagen, elastin, and proteoglycans, thereby participating in ECM remodeling. To maintain ECM homeostasis and prevent excessive degradation, fibroblasts secrete tissue inhibitors of metalloproteinases (TIMPs), which regulate MMP activity.\u003c/p\u003e\u003cp\u003eThe supersaturation of cholesterol in bile is a prerequisite for the pathogenesis of cholesterol gallstone disease, although the underlying mechanisms remain incompletely understood. Studies have shown that the intestinal microbiota, particularly Desulfovibrionales, is enriched in patients with gallstones. Fecal transplantation from gallstone patients to gallstone-resistant mouse strains induces gallstone formation. The presence of Desulfovibrionales is associated with increased secondary bile acid production in the cecum, greater bile acid hydrophobicity, and enhanced intestinal cholesterol absorption. These gallstone-prone microbiota modulate bile acid hydrophobicity and promote cholesterol secretion, thereby contributing to gallstone formation(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGallstone formation primarily results from the supersaturation and deposition of cholesterol, bile pigments, and other components in the gallbladder. This process can trigger inflammatory reactions and lead to ECM alterations in gallbladder tissues, exacerbating gallstone progression. Given the critical role of fibroblasts in tissue inflammation and ECM remodeling, their involvement in gallstone pathology warrants further investigation.\u003c/p\u003e\u003cp\u003eThis study aims to explore the specific role of fibroblasts, particularly the expression of the THY1 protein, in the progression of cholesterol gallstone disease. Our experiments demonstrate that elevated THY1 protein expression in fibroblasts plays a crucial role in regulating cholesterol gallstone formation. Furthermore, we elucidate the mechanisms by which THY1 contributes to gallstone formation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eData collection\u003c/p\u003e\u003cp\u003eThe raw sequence data (in RAW format) from a proteome sequencing dataset were downloaded from the iProX database (accession: PXD035915). This dataset includes 40 human bile samples, comprising 31 gallstone bile samples and 9 control bile samples (gallbladder polyps or normal bile). RNA-seq data were retrieved from the GSE202479 dataset in the GEO database. For analysis, three normal gallbladders and four gallstone gallbladders from this dataset were selected. Single-cell transcriptome data were obtained from the GSE179524 dataset in the GEO database. Comparative analysis was conducted using tissues from one normal mouse gallbladder and two gallstone mouse gallbladders.\u003c/p\u003e\u003cp\u003e\u003cb\u003eProteomic Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMaxQuant was utilized for targeted sample-specific database searches using a streamlined workflow. Peptide Spectrum Matching (PSM) and False Discovery Rates (FDR) for both proteins and modification sites were set to 0.01. Searches were performed in parallel using reduced decoy mode and included contaminant sequences. The match-between-runs function was applied with the following parameters: match ion tolerance window\u0026thinsp;=\u0026thinsp;0.05; alignment time window\u0026thinsp;=\u0026thinsp;20 minutes; orientation ion tolerance\u0026thinsp;=\u0026thinsp;1; and match unrecognized elements set to true. Protein expression was quantified using the Label-Free Quantification (LFQ) module with the following parameters: LFQ minimum ratio count\u0026thinsp;=\u0026thinsp;1 and labeled minimum ratio count\u0026thinsp;=\u0026thinsp;1. Proteins with identical peptide sets were automatically grouped into single protein groups. All other unspecified parameters were left at their default settings. The resulting proteinGroups.txt file was filtered using an R script to exclude proteins labeled as \"potential contaminants,\" \"reverse,\" or \"identified by site only.\" Proteins with fewer than two unique peptides were also discarded. Filtered proteinGroups.txt files were further analyzed in R using the proBatch package. Preprocessing, differential expression analysis, and machine learning-based modeling were performed using R packages such as imputeLCMD, limma, and caret. Protein classification models were developed using machine learning algorithms including svmLinear, random forest (rf), naive Bayes, and k-nearest neighbors (knn)(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). We used the pROC package to plot the ROC curves for the four machine-learning-identified specific proteins in the validation set. The area under the curve (AUC) was calculated to evaluate the specificity of these proteins.\u003c/p\u003e\u003cp\u003e\u003cb\u003eProtein-Protein Interaction Network\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA Protein-Protein Interaction (PPI) Network represents the interactions between individual proteins. The STRING database(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) is a widely used resource for searching known proteins and predicting protein-protein interactions. In this study, we utilized the STRING database, specifying \"human\" as the biological species, to construct a PPI network associated with bile differential proteins of gallstones. A minimum interaction confidence score of 0.400 was applied. The PPI network model was visualized using Cytoscape(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Additionally, we employed the MCODE (Molecular Complex Detection) algorithm within the cytoHubba(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) plugin to calculate and display scoring metrics for protein clusters.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDifferentially expressed genes Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn order to obtain the differentially expressed genes (DEGs) between the gallstone group and the normal group, we firstly used the R package sva to de-batch the dataset (GSE202479) Subsequently, based on the grouping information in the data, we used the R package limma(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) for differential analysis to obtain differentially expressed genes. Finally, we will merge all DEGs with |logFC| \u0026gt;1 and p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05 from the differential analysis, and draw volcano plots to display the results using the R package ggplot2, along with heat maps related to significant top 20 DEGs using the R package pheatmap.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEnrichment analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEnrichment analysis was performed using the GSEABase, ClusterProfiler, and org.Hs.eg.db packages in conjunction with the Metascape website.The databases used for enrichment analysis were obtained from Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Enrichment analysis was performed by using the \"EnrichGO\" function, where pathways with a P-value less than 0.05 were considered significantly enriched. Visualization of the results was done using the \"ggplot2\" and \"ggpubr\" software packages.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGSEA enrichment analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGene Set Enrichment Analysis (GSEA)(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) is a computational method proposed by the Broad Institute to determine whether a predefined set of genes show statistical differences between two biological states, and is commonly used to estimate changes in pathway and biological process activity in samples from expression data sets. To investigate the differences in biological processes between the two sets of samples, we downloaded the reference gene set \"c2.cp.v7.2.symbols.gmt\" from the MSigDB database(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) based on the gene expression profiling dataset, using the R package \" The GSEA method contained in the R package \"clusterProfiler\" was used for enrichment analysis and visualization of the dataset. The parameters used in this GSEA enrichment analysis were as follows: seed 2020, number of computations 1000, minimum number of genes per gene set 10, maximum number of genes per gene set 500, p-value correction method Benjamini-Hochberg (BH), and significant enrichment filtering criteria p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003e\u003cb\u003essGSEA Immune Infiltration\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSingle sample Gene Set Enrichment Analysis (ssGSEA)(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) estimates the number of specific immune infiltrating cells and the activity of specific immune responses. The algorithm utilized 29 gene sets from published research on tumor immune infiltration, which included various human immune cell subtypes such as CD8\u0026thinsp;+\u0026thinsp;T cells, dendritic cells, macrophages, and regulatory T cells.Enrichment scores calculated by our analysis of the ssGSEA algorithm in the R package GSVA package were used to represent the level of infiltration of each immune cell type in each sample. Correlations between immune infiltrating cells were determined by Spearman correlation analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSingle-Cell Transcriptome Data Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSingle-cell transcriptome data were obtained from the GEO database (GEO accession number: GSE179524; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Quality control was performed in R using standard single-cell data processing workflows. The count matrix was imported using the \"Read10X\" function from the Seurat package (version 4.0.4) and converted to a dgCMatrix format. The \"merge\" function was used to integrate individual objects into a single aggregate object, and the \"RenameCells\" function ensured the uniqueness of all cell labels. Low-quality cells were filtered out based on the following criteria: genes expressed in fewer than three cells were removed, and cells expressing fewer than 200 genes were excluded. A global scaling normalization method (\"LogNormalize\") was applied to normalize gene expression across cells, with a scaling factor of 10,000. Highly variable genes (n\u0026thinsp;=\u0026thinsp;2,000) were identified for downstream analysis using the \"FindVariableFeatures\" function. The \"ScaleData\" function was applied, with the \"vars.to.regress\" parameter set to UMI counts and mitochondrial percentage content, to mitigate unwanted variations.\u003c/p\u003e\u003cp\u003eDimensionality reduction was performed using PCA, focusing on highly variable features, and the top 30 principal components were retained for further analysis. Batch effects between samples were corrected using the Harmony method. Cells were visualized and downscaled using the UMAP method. Clustering was conducted based on edge weights between cells, and a shared nearest neighbor graph was generated using the Louvain algorithm implemented in the \"FindNeighbors\" and \"FindClusters\" functions. The resolution parameter in \"FindClusters\" was optimized between 0.1 and 1. Using the \"clustree\" function, the clustering tree was visualized, and the resolution of 0.9 yielded the most distinct clusters. Potential doublets were removed using the Scrublet algorithm. Cell clusters were annotated by identifying differentially expressed marker genes using the \"FindAllMarkers\" function. This analysis employed the default non-parametric Wilcoxon rank sum test with Bonferroni correction. Cell identities were assigned based on surface markers and gene annotations from relevant literature and the Cell Classification Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngdc.cncb.ac.cn/celltaxonomy/\u003c/span\u003e\u003cspan address=\"https://ngdc.cncb.ac.cn/celltaxonomy/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Cell-cell interaction analysis was performed using the CellChat method.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBiochemical Index Detection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe concentration of total bile acids in bile was measured using the circulating enzyme method (Ningbo Secke Biotechnology Co., LTD). Phospholipid concentration was determined through enzymatic colorimetry, following the manufacturer's instructions for the kit provided by Beijing Jinhao Pharmaceutical Co., LTD. The levels of bile acids (Bile Acid Assay Kit [Colorimetric], ab239702, Abcam, USA), cholesterol (ab65359), and triglycerides (ab65336) in bile and liver were quantified using respective assay kits. The CSI was calculated based on Carey's criticality table(17).\u003c/p\u003e\u003cp\u003e\u003cb\u003eLentivirus Packaging and Production\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRecombinant vectors were amplified and purified using the EndoFree Midi Plasmid Kit (TIANGEN). The lentiviral packaging system was provided by GeneChem Company (Shanghai, China), which included a virus packaging helper vector (Helper 1.0) and a packaging assistant vector (Helper 2.0). The recombinant vector and packaging vectors were co-transfected into 293T cells, and the viral solution was harvested 48\u0026ndash;72 hours post-transfection. The 293T cells were subsequently cultured in puromycin-supplemented DMEM. Before injecting the viral solution into mice via the tail vein, it was filtered and purified. The viral titer was determined using fluorimetry.\u003c/p\u003e\u003cp\u003e\u003cb\u003eReal-Time Fluorescence PCR\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePowerUp SYBR Green Master Mix (Applied Biosystems) was utilized for real-time fluorescence quantitative PCR (qPCR) to assess target gene expression. Relative mRNA expression levels were quantified using the ΔΔCt method, with GAPDH as the internal control. Primer sequences are provided in Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTHY1 expression recombinant vector construction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFirst, we searched the NCBI database for the THY1 sequence to determine its genomic location and nucleotide composition. Primers were designed using an online primer design tool, incorporating restriction endonuclease recognition sites. The primer sequences for THY1 were THY1-P1: CCAGGGCTGCTTCTGATTATT and THY1-P2: TGTCCCATCACCATGTATAAT. The GV369 vector was employed for lentiviral vector construction, containing elements such as Ubi, MCS, SV40, EGFP, IRES, and puromycin in sequence. AgeI and NheI were selected as the restriction enzyme sites. Detailed information and a plasmid map of the GV369 vector are available in the related literature (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The target gene was amplified via PCR, and the vector was linearized using restriction enzymes (NEB). Recombination was carried out using T4 DNA ligase, and the recombinant vector was transformed into E. coli. Positive clones were selected, and recombinant plasmids were extracted. After verification through restriction enzyme digestion and sequencing, the recombinant plasmids were confirmed to be correctly constructed.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWestern Blot Assay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTotal protein was extracted from gallbladder tissue using lysis buffer (Beyotime, Shanghai, China). The suspension was collected, lysed on ice, and centrifuged at 12,000 \u0026times; g for 30 minutes at 4\u0026deg;C. Protein concentration was measured using a NanoDrop spectrophotometer. After quantification, 50 \u0026micro;g of total protein was loaded onto an SDS-PAGE gel, separated by electrophoresis, and subsequently transferred onto a PVDF membrane.\u003c/p\u003e\u003cp\u003eThe membrane was blocked and incubated with primary antibodies overnight at 4\u0026deg;C. The following day, it was incubated with secondary antibodies, washed, and protein bands were visualized. Immunoblot analysis was performed using the following primary antibodies: anti-fibronectin (FN) (1:1000, ab2413, Abcam, Cambridge, UK), anti-collagen I (COL-I) (1:500, sc-59772, Santa Cruz, Dallas, TX, USA), matrix metalloproteinase 3 (MMP3) (1:1000, GB11131-100, Servicebio, China), and matrix metalloproteinase 10 (MMP10) (1:1000, GB112994-100, Servicebio, China).\u003c/p\u003e\u003cp\u003eSecondary antibodies included horseradish peroxidase-conjugated goat anti-rabbit and goat anti-mouse antibodies (1:10,000, Wuhan, China). GAPDH (1:1000, GB15004-100, Servicebio, China) served as the internal control. Images were quantified using ImageJ software.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnimal Gallstone Model Construction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFemale C57BL/6 mice (weighing 18\u0026ndash;20 g) were used for animal model construction. Ten mice were assigned to the control group and fed standard chow (0.02% cholesterol content) for seven weeks. Twenty mice were assigned to the model group and fed a lithogenic diet (15% fat, 2% cholesterol, and 1% bile acid) for seven weeks. The model group was further divided into a simple model group and a THY1-treated group. Mice in the THY1-treated group received a tail vein injection of THY1 lentivirus four weeks after the initiation of the lithogenic diet.\u003c/p\u003e\u003cp\u003eCharacteristics of the mice, including body weight, food intake, movement, and mental state, were recorded weekly. All mice were anesthetized with isoflurane administered via a gas anesthesia machine at a concentration of 1\u0026ndash;3%. Isoflurane was chosen due to its advantages of rapid onset, easy control of anesthesia depth, and quick recovery, ensuring animal welfare during the experiment. Mice were sacrificed by cervical dislocation, and their gallbladders, bile, and sphincters were collected for analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImmunofluorescence and Immunohistochemistry\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThree-micrometer-thick paraffin-embedded kidney tissue sections were prepared. The sections were baked, dewaxed, hydrated, and blocked with 5% bovine serum albumin for 2 hours. Subsequent steps were carried out according to the manufacturer's instructions (Maixin, Fuzhou, China). Tissue sections were incubated with primary antibodies against FN and COL-I, followed by nuclear staining and sealing after incubation with secondary antibodies. The immunostained samples were observed, and images were captured using fluorescence microscopy.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll data processing and analysis were conducted using R software (Version 4.2.1). Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and comparisons between two groups were performed using the Wilcoxon Rank Sum Test. For categorical variables, statistical significance was determined using either the chi-square test or Fisher's exact test, depending on the distribution and sample size. Spearman correlation analysis was employed to calculate correlation coefficients between molecular variables, unless otherwise specified. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant across all analyses.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eProteomic Analysis of Human Bile from Patients with and without Gallstones\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe conducted a proteomic analysis of human bile samples from patients with gallstones and controls without gallstones. For the analysis, we utilized the MaxQuant secondary database. MaxQuant identified 23,033 peptides, which were further analyzed using categorical Latent Class Analysis (LCA) in Metalab. This process ultimately identified 2,749 human proteins. The data were interpolated and corrected across all samples using the proMod package, followed by differential analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eProtein-protein interaction (PPI) analysis was performed on 172 differentially expressed proteins using the STRING database. A PPI network for these proteins was constructed and visualized using Cytoscape software (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Additionally, the cytoHubba plug-in within Cytoscape was used to calculate differential protein scores based on the MCODE algorithm.\u003c/p\u003e\u003cp\u003eTo evaluate the diagnostic potential of the differential proteins for gallstones, we selected the top 20 proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Diagnostic models were developed using four machine learning algorithms: svmLinear, random forest (rf), naive Bayes (naive_bayes), and k-nearest neighbors (knn) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. E-F). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. E-F, screening with these algorithms identified 12 proteins (C4BPA, MANBA, SNX18, ABCBB, LYAG, ENPP3, THY1, SCTM1, TARSH, NTRI, F151A, and HOME2) with importance scores greater than 60. The ROC plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG) demonstrated that these 12 proteins possess significant diagnostic value for gallstones.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGallbladder Transcriptome Analysis in Patients with and without Gallstones\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate differences in gene expression between the control (Normal) group and the gallstone group in the merged dataset, we conducted differential gene expression analysis using the R package limma. A total of 1,089 genes met the criteria for differential expression, defined as |logFC| \u0026gt;2 and p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Among these, 700 genes were upregulated (logFC\u0026thinsp;\u0026gt;\u0026thinsp;2, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and 389 were downregulated (logFC \u0026lt; -2, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGene Ontology (GO) enrichment analysis revealed that upregulated DEGs were primarily enriched in processes such as immune cell migration, inflammatory response, and immune effector activity. In contrast, downregulated DEGs were enriched in pathways related to ion stress responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). KEGG enrichment analysis indicated that DEGs were associated with pathways involving cytokine signaling, immune responses, and extracellular matrix (ECM) formation.\u003c/p\u003e\u003cp\u003eTo assess the impact of gene expression on gallstone occurrence, we performed Gene Set Enrichment Analysis (GSEA) to explore biological processes, cellular components, and molecular functions. The results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) indicated significant enrichment of genes in pathways such as CHEMOKINE_SIGNALING, CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION, INTESTINAL_IMMUNE_NETWORK, PRIMARY_IMMUNODEFICIENCY, and SYSTEMIC_LUPUS_ERYTHEMATOSUS.\u003c/p\u003e\u003cp\u003eSingle-cell annotation of gallbladder cells from normal and gallstone-bearing mice identified six main cell types: epithelial, proliferative, endothelial, fibroblast, macrophage, and smooth muscle cells, with epithelial cells being the predominant type (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, C). Analysis of intercellular communication demonstrated that fibroblasts exhibited substantial interactions with other cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMulti-Omics Analysis Reveals THY1 as an Important Gene for Gallstones\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe performed separate analyses of the proteome, transcriptome, and single-cell datasets to identify differences and intersecting differential genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Among these genes, THY1 was identified as a common intersecting gene. Single-cell analysis showed that THY1 was primarily expressed in fibroblasts in the gallstones group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-C).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePrevious studies have demonstrated that gallstones are closely associated with immune and inflammatory responses. To explore whether elevated THY1 expression is also linked to immune effects, we calculated the infiltration levels of 29 immune cell types across different transcriptome samples using the ssGSEA algorithm. The results were visualized using stacked bar graphs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). This analysis revealed significant differences in the infiltration levels of pro-inflammatory cells, MHC class I cells, NK cells, and T cell co-stimulatory cells, suggesting that THY1 is associated with multiple immune cell populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-F).\u003c/p\u003e\u003cp\u003eTo further investigate, we induced gallstone formation in mice by feeding them a lithogenic diet (15% fat, 2% cholesterol, 1% bile acid) for seven weeks. Gallbladders were then collected from both control and experimental groups, and qRT-PCR was performed to validate THY1 expression levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003e\u003cb\u003eKnockdown of THY1 Reduces Gallstones\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing the established gallstone animal model, we employed AAV virus-packaged sh-THY1 to knock down THY1 expression in mice with gallstones. Our results showed that reducing THY1 expression significantly decreased the extent of gallstones in mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. A-C). Analysis of bile composition revealed elevated cholesterol molar levels and cholesterol saturation index (CSI) in the gallstone group, both of which were significantly reduced in the THY1 knockdown group. Furthermore, bile acid levels were higher in the THY1 knockdown group compared to the gallstone group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eKnockdown of THY1 Reduces Inflammation Levels in Gallstone Mice\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the effect of THY1 knockdown on inflammation levels in gallstone mice, our results showed significant reductions in pro-inflammatory cytokines, including IL-6, TNF-α, IL-1β, and IL-8, in the THY1 knockdown group compared to the untreated gallstone group by PCR. However, IL-4 levels did not exhibit significant changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eKnockdown of THY1 Alleviates ECM Formation in Gallstone Gallbladders\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFibronectin (FN) and collagen type I (COL-I) are well-established key components of extracellular matrix (ECM) formation. In our study, immunofluorescence analysis of gallbladder tissues showed significantly increased fluorescence intensity of FN and COL-I in the gallstone group compared to the non-gallstone group, indicating alterations in ECM composition and structure that may impair gallbladder physiological function. In contrast, the fluorescence intensity of FN and COL-I was notably lower in the THY1 knockdown group compared to the gallstone group, suggesting that THY1 knockdown mitigates ECM formation during gallstone formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWestern blotting and immunohistochemistry further confirmed reduced expression levels of FN and COL-I in the THY1 knockdown group relative to the gallstone group. Additionally, Western blot analysis revealed a significant decrease in the expression of ECM-degrading enzymes, MMP-3 and MMP-10 shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, in the gallstone group. This decrease was partially restored upon THY1 knockdown.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGallstone disease is a prevalent condition worldwide, affecting 5\u0026ndash;15% of adults(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Recent studies have highlighted the critical role of inflammation and immune responses in gallstone pathogenesis. Chronic inflammation in the gallbladder disrupts the local immune microenvironment, leading to bile stasis, epithelial injury, and extracellular matrix (ECM) remodeling. Inflammatory cytokines such as IL-6, TNF-α, and IL-1β are known to contribute to the initiation and progression of gallstones by promoting cholesterol crystallization and mucin production(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Furthermore, immune cell infiltration, including macrophages, T cells, and neutrophils, has been observed in gallstone-afflicted gallbladders, indicating a strong link between immune activation and gallstone formation(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This inflammatory milieu not only facilitates gallstone development but also exacerbates gallbladder dysfunction, fibrosis, and the risk of secondary complications such as cholecystitis and gallbladder cancer(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe pathogenesis of cholesterol gallstones is multifactorial, involving genetic factors such as the LITH gene, hepatic overproduction of cholesterol leading to bile oversaturation, impaired gallbladder motility, mucin gel accumulation within the gallbladder lumen, and immune-mediated gallbladder inflammation(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Cholesterol stones are also associated with obesity, which may alter bile cholesterol concentrations and promote gallstone formation. Inflammation is a key driver of gallstone development, as histopathologic changes in the gallbladder wall often precede cholesterol gallstone formation, as evidenced in both animal models and human studies(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Additionally, findings from the Danish WHO Multinational Vascular Disease Trends and Determinants Study highlighted a strong association between gallstone disease and factors such as insulin resistance, systemic inflammation, and genetic predisposition to obesity or type 2 diabetes(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, we identified THY1 as a critical driver of gallstone progression, mediated through inflammatory factors and ECM formation. Using limma differential analysis and machine learning algorithms (svmLinear, rf, naive_bayes, and knn), we screened 12 key proteins from bile proteomic data of patients with and without gallstones. ROC curve analysis demonstrated excellent predictive accuracy for these proteins. Transcriptomic and single-cell analyses further confirmed that THY1 was consistently overexpressed in gallstone samples, with single-cell analysis localizing THY1 expression specifically to gallbladder fibroblasts.\u003c/p\u003e\u003cp\u003eMoreover, ssGSEA immuno-infiltration analysis showed a strong correlation between THY1 expression and immune cell infiltration, highlighting its significant role in gallstone progression. Mechanistic studies revealed that THY1 promotes gallstone progression by influencing inflammatory responses and ECM formation.\u003c/p\u003e\u003cp\u003eIn a gallstone mouse model, we observed high THY1 expression in the gallbladder using qRT-PCR. Knockdown of THY1 via sh-RNA significantly reduced gallstone severity, evidenced by a decrease in the number of gallstones. Analysis of bile composition further revealed that THY1 knockdown improved the biliary lipid profile, including reductions in total bile salt content, cholesterol saturation index (CSI), and levels of bile acids, phospholipids, cholesterol, and triglycerides.\u003c/p\u003e\u003cp\u003eGallstone formation is a multifactorial process influenced by numerous factors, with inflammation playing a pivotal role(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Inflammation disrupts protein and lipid metabolism, impacting cholesterol and bile acid pathways, and can elevate bile salt levels, contributing to gallstone formation. Previous studies have linked gallstones to cytokines such as IL-6, IL-10, IL-12, and IL-13(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). IL-6, expressed by the biliary epithelium, induces inflammatory cell infiltration and increases gallbladder wall thickness(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Consistent with this, our study found reduced levels of inflammatory factors, including IL-6, IL-1β, and TNF-α, in THY1 knockdown mice. Since fibroblasts are primary producers of IL-6, our findings suggest that THY1 knockdown mitigates inflammation by reducing IL-6 secretion from fibroblasts, thereby attenuating gallstone progression.\u003c/p\u003e\u003cp\u003eTHY1, a GPI-anchored protein with an RLD structural domain resembling the RGD integrin-binding sequence, facilitates binding of lipid rafts to αvβ3 and αvβ5 integrins(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Previous studies have shown that Thy-1 deletion in fibroblasts disrupts mechanotransduction and rigidity sensing in pulmonary fibrosis. Similarly, increased expression of ECM components, such as fibronectin (FN) and collagen type I (COL-I), in gallstone-afflicted gallbladders contributes to tissue sclerosis, thickening, and potentially gallbladder fibrosis, exacerbating gallstone progression. In gallstone-afflicted tissues, the expression of ECM-degrading enzymes, such as MMP-3, is significantly reduced.\u003c/p\u003e\u003cp\u003eConversely, our study demonstrated decreased expression of FN and COL-I and increased levels of ECM-degrading enzymes in THY1 knockdown mice, confirmed through Western blotting, immunofluorescence, and immunohistochemistry. This aligns with findings in cancer-associated fibroblasts (CAFs), where high Thy-1 expression promotes ECM remodeling(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). These findings suggest a role for Thy-1 in regulating ECM dynamics in gallstone pathogenesis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study integrated proteomics, transcriptomics, and single-cell analyses to investigate changes in the gallbladder during gallstone disease. Our findings identified THY1 as a key contributor to gallstone pathogenesis, influencing inflammatory factor levels and ECM formation, two critical elements in gallstone progression.\u003c/p\u003e\u003cp\u003eHowever, this study has limitations. The data were primarily derived from publicly available databases, necessitating further validation in clinical settings. Additionally, gallstones represent a multifactorial disease entity. While our findings suggest that reducing THY1 expression holds therapeutic potential, further research is needed to comprehensively understand the underlying mechanisms and develop targeted therapeutic strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of Interest\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interest.\u003c/p\u003e\u003ch2\u003eEthic and approval statement\u003c/h2\u003e\u003cp\u003e All patients provided written informed consent before enrollment, and this study was approved by the Research Ethics Committee of Shandong Provincial Hospital Affiliated to Shandong First Medical University. All animal experiments involved in this study were approved by the Shandong Provincial Hospital Affiliated to Shandong First Medical University (approval number: 2019A56YB). All experimental methods were performed in accordance with relevant guidelines and regulations, including the ARRIVE guidelines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arriveguidelines.org\u003c/span\u003e\u003cspan address=\"https://arriveguidelines.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All experiments followed ethical and regulatory requirements for animal use.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe research was funded by Natural Science Foundation of Shandong Province (No.ZR2021QH186); National Natural Science Foundation of China (No.81870205); Chen Xiao-Ping Foundation for the Development of Science and Technology of Hubei Province (No.CXPJJH12000001-2020304); Foundation research project of Qinghai province ((No.2021-ZJ-719);National Natural Science Foundation of China (No. 82000579).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, resources and project administration, X.X.W., Z.H.Z.and M.Z.M.; methodology and software, S.S., X.W.X., C.Q.and R.X.G; validation and formal analysis, M.Z.M., S.S. and X.W.X; investigation, C.Q.and R.X.G; data curation and visualization, Z.H.Z.and M.Z.M.; writing-original draft preparation, X.W.X., C.Q.and R.X.G.; writing-review and editing, X.X.W.and Z.H.Z. supervision, X.X.W.; funding acquisition, Z.H.Z. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eNone.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e\u003cp\u003eThe data that supports the findings of this study are available within the article. The original data can be obtained by sending an email to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePortincasa P, Moschetta A and Palasciano G: Cholesterol gallstone disease. Lancet 368: 230\u0026ndash;239, 2006.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Y, Kong J and Wu S: Cholesterol gallstone disease: focusing on the role of gallbladder. Lab Invest 95: 124\u0026ndash;131, 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGaby AR: Nutritional approaches to prevention and treatment of gallstones. Altern Med Rev 14: 258\u0026ndash;267, 2009.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei K, Nguyen HN and Brenner MB: Fibroblast pathology in inflammatory diseases. J Clin Invest 131: e149538, 2021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen HN, Noss EH, Mizoguchi F, Huppertz C, Wei KS, Watts G and Brenner MB: Autocrine Loop Involving IL-6 Family Member LIF, LIF Receptor, and STAT4 Drives Sustained Fibroblast Production of Inflammatory Mediators. Immunity 46: 220\u0026ndash;232, 2017.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLynch MD and Watt FM: Fibroblast heterogeneity: implications for human disease. J Clin Invest 128: 26\u0026ndash;35, 2018.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarsdal MA, Nielsen SH, Leeming DJ, Langholm LL, Nielsen MJ, Manon-Jensen T, Siebuhr A, Gudmann NS, R\u0026oslash;nnow S, Sand JM, \u003cem\u003eet al\u003c/em\u003e: The good and the bad collagens of fibrosis - Their role in signaling and organ function. Adv Drug Deliv Rev 121: 43\u0026ndash;56, 2017.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu H, Shao W, Liu Q, Liu N, Wang Q, Xu J, Zhang X, Weng Z, Lu Q, Jiao L, \u003cem\u003eet al\u003c/em\u003e: Gut microbiota promotes cholesterol gallstone formation by modulating bile acid composition and biliary cholesterol secretion. Nat Commun 13: 252, 2022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRanathunge C, Patel SS, Pinky L, Correll VL, Chen S, Semmes OJ, Armstrong RK, Combs CD and Nyalwidhe JO: promor: a comprehensive R package for label-free proteomics data analysis and predictive modeling. Bioinform Adv 3: vbad025, 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSzklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, \u003cem\u003eet al\u003c/em\u003e: STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47: D607-D613, 2019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13: 2498\u0026ndash;2504, 2003.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChin CH, Chen SH, Wu HH, Ho CW, Ko MT and Lin CY: cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8 Suppl 4: S11, 2014.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRitchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W and Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43: e47, 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, \u003cem\u003eet al\u003c/em\u003e: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102: 15545\u0026ndash;15550, 2005.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiberzon A, Birger C, Thorvaldsd\u0026oacute;ttir H, Ghandi M, Mesirov JP and Tamayo P: The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst 1: 417\u0026ndash;425, 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiao B, Liu L, Li A, Xiang C, Wang P, Li H and Xiao T: Identification and Verification of Immune-Related Gene Prognostic Signature Based on ssGSEA for Osteosarcoma. Front Oncol 10: 607622, 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarey MC: Critical tables for calculating the cholesterol saturation of native bile. J Lipid Res 19: 945\u0026ndash;955, 1978.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEverhart JE, Khare M, Hill M and Maurer KR: Prevalence and ethnic differences in gallbladder disease in the United States. Gastroenterology 117: 632\u0026ndash;639, 1999.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen CH, Huang MH, Yang JC, Nien CK, Etheredge GD, Yang CC, Yeh YH, Wu HS, Chou DA and Yueh SK: Prevalence and risk factors of gallstone disease in an adult population of Taiwan: an epidemiological survey. J Gastroenterol Hepatol 21: 1737\u0026ndash;1743, 2006.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Z, Kemp TJ, Gao YT, Corbel A, McGee EE, Wang B, Shen MC, Rashid A, Hsing AW, Hildesheim A, \u003cem\u003eet al\u003c/em\u003e: Association of circulating inflammation proteins and gallstone disease. J Gastroenterol Hepatol 33: 1920\u0026ndash;1924, 2018.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIglesias M, Plowman GD and Woodworth CD: Interleukin-6 and interleukin-6 soluble receptor regulate proliferation of normal, human papillomavirus-immortalized, and carcinoma-derived cervical cells in vitro. Am J Pathol 146: 944\u0026ndash;952, 1995.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou Y, Hagood JS, Lu B, Merryman WD and Murphy-Ullrich JE: Thy-1-integrin alphav beta5 interactions inhibit lung fibroblast contraction-induced latent transforming growth factor-beta1 activation and myofibroblast differentiation. J Biol Chem 285: 22382\u0026ndash;22393, 2010.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCalvo F, Ege N, Grande-Garcia A, Hooper S, Jenkins RP, Chaudhry SI, Harrington K, Williamson P, Moeendarbary E, Charras G, \u003cem\u003eet al\u003c/em\u003e: Mechanotransduction and YAP-dependent matrix remodelling is required for the generation and maintenance of cancer-associated fibroblasts. Nat Cell Biol 15: 637\u0026ndash;646, 2013.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLammert F, Gurusamy K, Ko CW, Miquel JF, M\u0026eacute;ndez-S\u0026aacute;nchez N, Portincasa P, van Erpecum KJ, van Laarhoven CJ and Wang DQ: Gallstones. Nat Rev Dis Primers 2: 16024, 2016.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan Erpecum KJ, Wang DQ, Moschetta A, Ferri D, Svelto M, Portincasa P, Hendrickx JJ, Schipper M and Calamita G: Gallbladder histopathology during murine gallstone formation: relation to motility and concentrating function. J Lipid Res 47: 32\u0026ndash;41, 2006.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaurer KJ, Carey MC and Fox JG: Roles of infection, inflammation, and the immune system in cholesterol gallstone formation. Gastroenterology 136: 425\u0026ndash;440, 2009.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShabanzadeh DM, Skaaby T, S\u0026oslash;rensen LT, Eugen-Olsen J and J\u0026oslash;rgensen T: Metabolic biomarkers and gallstone disease - a population-based study. Scand J Gastroenterol 52: 1270\u0026ndash;1277, 2017.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaalbach A, Wetzel A, Haustein UF, Sticherling M, Simon JC and Anderegg U: Interaction of human Thy-1 (CD 90) with the integrin alphavbeta3 (CD51/CD61): an important mechanism mediating melanoma cell adhesion to activated endothelium. Oncogene 24: 4710\u0026ndash;4720, 2005.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Gallbladder, Gallstones, THY1, Cholesterol, Fibroblasts, ECM, Immune inflammation","lastPublishedDoi":"10.21203/rs.3.rs-7276422/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7276422/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eTHY1 (Thy-1 cell surface antigen) is a glycophosphatidylinositol (GPI)-anchored membrane glycoprotein involved in cell-cell interactions, tissue remodeling, and immune regulation. Initially studied in neural and immune cells, THY1 is increasingly recognized for its roles in inflammatory responses and fibrosis, processes that are central to gallstone formation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe performed multi-omics analyses, including transcriptomics, proteomics, and single-cell sequencing, to investigate changes in the gallbladder during gallstone formation. A gallstone mouse model was established using a lithogenic diet, alongside a THY1 knockdown gallstone mouse model created via sh-RNA, to explore the role of THY1 in this process. Hematoxylin and eosin (HE) staining and quantitative reverse transcription PCR (qRT-PCR) were conducted to assess inflammation levels in THY1 knockdown mice during gallstone formation. Western blotting, immunohistochemistry, and immunofluorescence were employed to evaluate the expression of fibronectin (FN) and collagen I (COL-I), elucidating the role of THY1 in extracellular matrix (ECM) formation during gallstone progression. Additionally, biochemical assays were used to quantify bile acids, phospholipids, cholesterol, and triglycerides, and the cholesterol saturation index (CSI) was calculated to further analyze the biochemical environment.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eTHY1 expression was significantly elevated in gallbladder fibroblasts during gallstone formation. Knockdown of THY1 alleviated gallstone formation induced by a lithogenic diet in mice. In THY1 knockdown mice, cholesterol levels in gallbladder bile were significantly reduced, bile acid concentration increased, and the CSI index decreased. Additionally, the expression of inflammatory cytokines in the gallbladders of THY1 knockdown mice was reduced, leading to decreased gallbladder inflammation. ECM formation in the gallbladders of THY1 knockdown mice was also alleviated.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study reveals that high expression of THY1 in gallbladder fibroblasts promotes the progression of gallstones by increasing inflammation levels and ECM formation.\u003c/p\u003e","manuscriptTitle":"High Expression of THY1 in Gallbladder Fibroblasts Promotes the Formation and Progression of Gallstones","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 13:51:42","doi":"10.21203/rs.3.rs-7276422/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":"ea5b4962-cad0-4e30-bc4e-d8eb58f3ca9a","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-19T03:23:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 13:51:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7276422","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7276422","identity":"rs-7276422","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}