Novel insight into the gene etiology of ulcerative colitis gained from transcriptome association study and single cell sequence analysis

Novel insight into the gene etiology of ulcerative colitis gained from transcriptome association study and single cell sequence analysis | 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 Novel insight into the gene etiology of ulcerative colitis gained from transcriptome association study and single cell sequence analysis Zhenhua dong, Jianling Jia, Donghui Ren, Kai Yu, Dingliang Zhao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5133569/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: Ulcerative colitis (UC) is a prevalent chronic gastrointestinal disease. Gene plays an important role in UC pathogenesis. Therefore, we aim to identify UC susceptibility genes and specific cell types expressing these genes. Methods: We conducted a cross-tissue transcriptome-wide association study (TWAS) by integrating UC GWAS with 49 tissues gene-expression matrix from the Genotype-Tissue Expression project (GTEx). Subsequently, we employed Functional Summary-based Imputation (Fusion) to verify candidate genes within colon tissue. Conditional and Joint Analysis (COJO) was utilized to filter out genes potentially influenced by linkage disequilibrium. Multimarker Analysis of Genomic Annotation (MAGMA) was then applied to pinpoint genes relevant to UC. Validation of the selected genes was performed using Mendelian randomization (MR). GeneMANIA analysis was conducted to elucidate biological functions of identified genes. Finally, single-cell RNA sequencingwas employed to ascertain cell types in which these genes are enriched. Results: The cross-tissue TWAS, Fusion and MAGMA analyses identified a total of 5 genes, of which 3 genes, ADCY3 , ITGB6 , and MTMR3, were retained after MR. These genes were found to be implicated in several functional pathways, including the cAMP metabolic process and phosphorus-oxygen lyase activity. Furthermore, we observed ADCY3 predominantly enriched in B cells, while ITGB6 and MTMR3 enriched in epithelial cells. Conclusion: Our study has identified three genes associated with UC susceptibility. These findings not only enhance our understanding of the genetic underpinnings of UC but also offer novel avenues for exploring molecular mechanisms and potential therapeutic targets for UC. Ulcerative Colitis TWAS MAGMA Mendelian Randomization Single-cell RNA sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Ulcerative colitis is a prevalent chronic inflammatory bowel disease characterized by a relapsing and remitting course, imposing a significant burden on patients' quality of life and healthcare system [ 1 ]. The disease is typified by mucosal inflammation, often initiating in the rectum and potentially extending to involve the entire colon. Clinical manifestations of UC include bloody diarrhea, abdominal pain, fecal urgency, and tenesmus [ 2 ]. The diagnosis of UC relies heavily on clinical symptoms and colonoscopy findings. With the rapid advancement of medical technology, drugs such as 5-aminosalicylates (5-ASA), thiopurines, and biological agents have been instrumental in alleviating patient suffering [ 3 ]. However, approximately 15% of UC patients eventually require surgery due to UC complications, underscoring the urgency of elucidating the disease's etiology and identifying novel therapeutic targets [ 4 ]. UC is a multifaceted condition, involving complex interactions among susceptibility genes, immune dysregulation, environmental factors, and the gut microbiome. Familial aggregation studies indicate that 8%-14% of UC patients have a family history of inflammatory bowel disease (IBD), with first-degree relatives exhibiting a fourfold increased risk, highlighting the genetic component in UC pathogenesis [ 5 ]. Numerous researchers have endeavored to elucidate the role of specific genes in UC development. For instance, gene fucosyltransferase 8 has been shown to modulate the properties of the mucus layer by regulating the levels of MUC1, MUC2, and MUC5AC, which help facilitate the communication between bacterial and epithelial cells, resulting in proregression of UC finally. [ 6 ]. Another example is gene tripartite motif-containing 22 , which can exacerbate inflammation by modulating the NF-κB signaling pathway, thereby promoting UC [ 7 ]. Despite these insights, the number of genes definitively linked to UC remains limited. GWAS have successfully mapped numerous risk loci associated with IBD, with HLA-DQA1 being particularly implicated in UC susceptibility. However, the majority of GWAS-identified variants are located in non-coding regions, which constrains our understanding of gene transcriptional regulation [ 7 ]. Transcriptome-wide association studies (TWAS) offer a novel approach by integrating GWAS data with gene expression profiles from various tissues or cell types, allowing for the direct mapping of genetic variants to gene expression and reducing the number of comparisons, thereby enhancing study precision [ 8 ]. TWAS has been successfully applied to identify risk genes for a range of diseases, including age-related macular degeneration [ 9 ], systemic lupus erythematosus [ 10 ], and stroke [ 11 ]. In this study, we leverage the integration of UC GWAS data with the GTExV8 database to identify risk genes for UC. These genes are further validated using MR. We employ GeneMANIA analysis to explore gene functions and single-cell RNA sequencing to determine the cell types in which these genes are enriched. Our findings aim to contribute to the discovery of potential causal genes, which may inform novel treatment and intervention strategies for UC. 2. Methods 2.1 UC GWAS Data Source We amalgamated two UC summary datasets: one from the R11 version of the FinnGen database, comprising 6,435 cases and 446,419 controls of European ancestry, and another from the Integrative Epidemiology Unit database (ieu-a-32), consisting of 6,968 cases and 20,464 controls of European ancestry [ 12 ]. UC diagnoses were strictly adhered to the K11 standard of the International Statistical Classification of Diseases and Related Health Problems (ICD-10). Further details are accessible on the FinnGen website ( https://www.finngen.fi/en/access_results ). A study flowchart is depicted in Fig. 1 . 2.2 Cross-Tissue TWAS Analysis Employing the Unified Test for Molecular Signature (UTMOST) method, we conduct a cross-tissue TWAS using UC GWAS and expression quantitative trait loci (eQTL) data from 49 tissues of the GTExV8 project. Gene-trait associations are calculated for each tissue, and the statistics are utilized to evaluate associations across all tissues using the generalized Berk-Jones test. The utmost method facilitates the identification of genes shared across tissues and those specific to a single tissue, thereby enhancing the precision of TWAS [ 13 ]. After false discovery rate (FDR) correction, genes with p < 0.05 are subjected to further analysis. 2.3 Single-Tissue TWAS Analysis We perform a TWAS analysis by integrating UC GWAS with eQTL data from sigmoid and transverse colon tissues of GTExV8 project to identify candidate genes. Initially, the Fusion software is used to construct five predictive models—Best Linear Unbiased Prediction (BLUP), Bayesian Sparse Linear Mixed Model (BSLMM), Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net, and top SNPs from reference expression panels—to estimate gene expression weights in colon tissue. The optimal model is selected based on the R2 value derived from fivefold cross-validation. Subsequently, we combine gene expression weights with UC GWAS data to calculate association significance using the formula: ZTWAS = W′Z(W′LW)1/2ZTWAS​=(W′LW)1/2W′Z​, where Z represents the BID score, W the weight, and L the SNP-correlation linkage disequilibrium matrix. After FDR correction, genes with TWAS p < 0.05 are selected for further analysis [ 14 ]. 2.4 Gene-Based Association Analysis MAGMA is utilized to construct multiple regression models, calculating the cumulative effect of SNPs mapping to specific genes, thereby identifying genes or gene sets associated with UC [ 15 ]. MAGMA determines the association strength between identified genes and phenotypes based on gene scores, allowing for the identification of more genes and gene sets while maintaining an accurate type 1 error rate. After FDR correction, genes with FDR p < 0.05 are considered as candidate genes. 2.5 Conditional and Joint Analysis Following the Fusion and MAGMA analyses, we identifiy numerous associated genes that might be influenced by linkage disequilibrium (LD). COJO is employed to ascertain which genes are conditionally independent. COJO provides a comprehensive depiction of gene structure related to the phenotype by considering the LD between candidate genes. The analysis categorizes genes into two groups: jointly significant genes, whose results remain significant, and marginally significant genes, which become non-significant [ 16 , 17 ]. 2.6 Mendelian Randomization MR was conducted using the "TwoSampleMR" R package. In MR, gene expression data in colon tissue (sigmoid and transverse) served as the exposure, and UC GWAS as the outcome. Significant (P < 5e-8) and independent SNPs (R2 < 0.001 within a 10,000 kb window) from cis-eQTL data were selected as instrumental variables (IVs) [ 18 ]. Only one IV is chosen for each exposure, and word ratio is applied to explore the causal effect between exposure and outcome. A P-value of less than 0.05 indicates the presence of a causal relationship [ 19 ]. The causal relationship between QTL and GWAS is further strengthened by MR. 2.7 GeneMANIA Analysis and Single-Cell RNA Sequencing The GeneMANIA platform ( https://genemania.org/ ) is utilized to predict the functions of our genes of interest. It provides a comprehensive set of results, including physical interactions, co-expression, and enrichment pathways for targeted genes [ 20 ]. To explore the cell type-specific expression of target genes, we analyze single-cell RNA sequencing data (GSE214695) from colon tissues of six UC patients obtained from the Gene Expression Omnibus (GEO) [ 21 ]. We employed the "Read10X" package to read and integrate raw single-cell RNA-seq data. The "Seurat" package ( https://satijalab.org/seurat/ ) is used to create a Seurat object, excluding genes present in fewer than three cells and cells with less than 200 features. Cells with 200 < RNA features < 2500 and mitochondria percent ≥ 5% are included. Then, we perform normalization, finding high variable features, scale for data. After harmonizing data to eliminate batch effects, we conduct Uniform Manifold Approximation and Projection (UMAP). Cell types are annotated using specific markers.(Plasma cell - DERL3, MZB1, XBP1; CD4 + T cell – CD4 ; Neutrophils cell - PROK2, CMTM2, CXCL8, FCRG3B, AQP9, S100A8, S100A9; CD8 + T - CD8A, CD8B ; Fibroblasts cell - ADAMDEC1, CP, FABP4 ; Macrophages cell - CD68, CD14 C1QA, C1QB ; Epithelial cell - EPCAM, AQP8, BEST4, MUC2, OLFM4, PLCG2, TRPM5, ZG16 ; B cell - CD20 (MS4A1) ; Cycling cells (PC and B cells) - TUBB, TOP2A, MKI67 ) The density UMAP plots are utilized to depict the cell type-specific expression for these genes. 3. Results 3.1 Utmost and Fusion Analysis Following false discovery rate (FDR) correction, the utmost software identified 49 genes across 49 tissues (Supplementary Sheet 1), while the Fusion software identified 358 genes in sigmoid and transverse colon tissues (Supplementary Sheet 2). A total of 11 variants, as determined by both utmost and Fusion analyses, were considered as candidate genes: LINC01882, ARPC2, AHSA2P, ITGB6, AAMP, SANBR, DNAJC27-AS1, MTMR3, C2orf74-DT, ADCY3 , and PIM3 (Supplementary Sheet 3). 3.2 Conditional and Joint Analysis Nine of the 11 genes located on chromosomes 2, 18, and 22 passed the conditional and joint analysis, indicating that these genes are jointly significant and not confounded by LD. The results of the COJO analysis are depicted in Fig. 2 . In the sigmoid colon tissue (chromosome 2), the TWAS signal for AHSA2P was significantly attenuated when accounting for the predicted expression of SANBR, suggesting that AHSA2P did not pass COJO (Fig. 2 a). Similarly, the TWAS signal for ARPC2 was significantly decreased when accounting for the predicted expression of AAMP, indicating that ARPC2 did not pass COJO (Fig. 2 b). Consequently, LINC01882, ITGB6, AAMP, SANBR, DNAJC27-AS1 , and MTMR3 passed COJO. In the transverse colon tissue (chromosome 2), the TWAS signal for C2orf74-DT was significantly decreased when accounting for the predicted expression of AHSA2P (Fig. 2 c). The TWAS signal for ARPC2 significantly declined when accounting for the predicted expression of AAMP (Fig. 2 d). Thus, LINC01882, AAMP, ADCY3, AHSA2P , and PIM3 passed COJO. 3.3 MAGMA Analysis Among the 9 genes, 5 genes were identified as being related to UC in the MAGMA analysis: ITGB6, AAMP, MTMR3, ADCY3 , and PIM3 . A Venn diagram was created to illustrate the genes detected by Fusion, utmost, and MAGMA (Fig. 3 ). 3.4 Mendelian Randomization Among 5 genes, ITGB6, AAMP, MTMR3 were located in sigmoid tissue, and ADCY3, PIM3 in transverse tissue. MR results supported the causality of ITGB6, MTMR3 in sigmoid and ADCY3 in transverse colon. The forest plot is shown in Fig. 4 . Genes ITGB6, MTMR3 , and ADCY3 , mapped to chromosome 2p160099667-160200313 in sigmoid colon, chromosome 22p29883169-30030868 in sigmoid colon, and chromosome 2p24819169-24920237 in transverse colon respectively, showed a significant association with UC according to Fusion. MR analysis substantiated the causal links between ITGB6, MTMR3, ADCY3 , and UC. The associations between UC and ITGB6 [odds ratios (OR), 1.138, 95% confidence intervals (CI), 1.067–1.215, P value, 8.58E-05], MTMR3 [OR, 0.836, 95% CI, 0.755–0.925, P, 5.45E-04], and ADCY3 [OR, 1.189, 95% CI, 1.078–1.311, P, 5.3E-04] were significant. 3.5 GeneMANIA Analysis The potential gene interaction network with ITGB6 as a center is shown in Fig. 5 a. The functional pathways that genes related to ITGB6 enrich include the cAMP metabolic process, phosphorus-oxygen lyase activity, and response to glucagon. The gene interaction network with ADCY3 as a core is depicted in Fig. 5 b, with functional pathways including the cAMP metabolic process, phosphorus-oxygen lyase activity, and cyclic purine nucleotide metabolic process. The gene interaction network with MTMR3 as a center is shown in Fig. 5 c, with functional pathways including the phosphatidylinositol metabolic process, glycerophospholipid biosynthetic process, and phospholipid biosynthetic process. The gene interaction network with the three genes as the core is exhibited in Fig. 5 d, with functional pathways including the cAMP metabolic process, phosphorus-oxygen lyase activity, and response to glucagon. 3.6 Single-Cell RNA Sequencing Analysis After UMAP analysis, all cells were clustered into 13 clusters (Fig. 6 a). These clusters were further classified into 9 cell types based on typical cell markers, including plasma cells, CD4 + T cells, neutrophils, CD8 + T cells, fibroblasts, macrophages, epithelial cells, B cells, and cycling cells (Fig. 6 b). All three coding genes had expression data in colon tissue of UC patients. Figures 6 c and 6 d show the expression data of the three genes across the 9 cell types. It was observed that ADCY3 primarily expresses in B cells, ITGB6 in epithelial cells, and MTMR3 in all cell types, particularly epithelial cells. 4. Discussion In this study, leveraging UC GWAS data and the GTExV8 database, we conducted a comprehensive TWAS to explore the relationship between gene expression and UC susceptibility. Our integrative approach, employing cross-tissue TWAS for discovery and colon tissue TWAS, MAGMA, and MR for validation, successfully identified three genes associated with UC. Single-cell RNA sequencing analysis further delineated the primary cell types expressing these genes in the colon tissue, while GeneMANIA analysis expanded our understanding for their potential biological functions. Multi-omics association studies have gained momentum in the identification of risk genes for inflammatory bowel diseases (IBD), including Crohn's disease (CD) and UC. For instance, a study utilizing Summary-PrediXcan and Summary-MultiXcan methods identified 39 novel genes in the colon whose expression levels influence IBD susceptibility [ 22 ]. Japanese researchers were the first to construct effector memory T cells (TEM) eQTL data and, through TWAS, identified tenascin-XA in TEM as related to IBD risk [ 23 ]. Another study compared IBD patients with and without psychiatric comorbidities (PC) and identified risk genes, including RBPMS in skeletal muscle and several others in brain regions [ 24 ]. Lastly, HLA-DRB1 and TAP2 were detected as associated with IBD susceptibility in the colon through the combination of TWAS and messenger RNA expression data [ 25 ]. These studies underscore the potential of TWAS in identifying disease susceptibility genes, and our study adds to this body of work by identifying ITGB6, MTMR3 , and ADCY3 as risk genes related to UC. ITGB6 , primarily encoding a member of the integrin superfamily, plays a role in cell signaling and adhesion [ 25 ]. Increased ITGB6 expression has been observed in inflamed colon tissue from IBD patients, and in a mouse model, ITGB6 exacerbated dextran sulfate sodium (DSS)-induced colitis and was associated with poor prognosis [ 26 ]. Further research suggests that ITGB6 may promote macrophage infiltration, pro-inflammatory cytokine secretion, upregulation of integrin ligands, and activation of the Stat1 signaling pathway. ITGB6 's role in intestinal fibrosis, a severe IBD complication, has also been noted, with potential to exacerbate the condition via the focal adhesion kinase (FAK/AKT) pathway [ 27 ]. Our single-cell RNA sequencing analysis aligns with these findings, showing ITGB6 expression primarily in epithelial cells. MTMR3 encodes a member of the myotubularin dual specificity protein phosphatase gene family whose structrue is similar to myotubularin but in addition contains a FYVE domain and an N-terminal PH-GRAM domain.[ 28 ] Host pattern recognition receptors (PRRs) could regulate the balance between microbial interactions and cytokine production, which is relevant to IBD pathogenesis. In human macrophages, MTMR3 may increase IBD risk by reducing PRR-induced phosphatidylinositol 3-phosphate and autophagy levels, prompting PRR-induced caspase-1 and NFκB signaling pathway activation, enhancing autocrine IL-1β even overall cytokine secretion.[ 29 ] GWAS have also highlighted MTMR3 's role in autophagy, relevant to CD, with functional experiments confirming increased risk with higher MTMR3 expression.[ 30 ] ADCY3 encodes adenylyl cyclase 3 which is a membrane-associated enzyme and catalyzes the formation of the secondary messenger cyclic adenosine monophosphate. ADCY3 is widely expressed in various tissues and involved in IBD. Some scholars calculated a comprehensive gene expression data of distal colon tissue samples from 40 healthy African Americans, and identified ADCY3 as risk gene implicated in IBD.[ 31 ] Another GWAS is conducted between 2345 cases of African Americans with IBD and 5002 individuals without IBD to determine ADCY3 as IBD susceptibility Loci.[ 32 ] In summary, our study applied TWAS and multiple validation methods to identify three UC-associated risk genes and elucidate their biological functions. While TWAS enhances statistical power and mitigates reverse causality, our study has several limitations. First, the focus on European ancestry limits the generalizability of our findings. Second, the absence of independent validation data may reduce the study's credibility. Third, the stringent criteria for significant cis-heritability genes in TWAS may have led to the exclusion of some genes. Lastly, the sample and tissue limitations in the GTEx V8 database may have restricted the identification of more UC risk genes. Future studies, with the availability of more high-throughput gene expression data across diverse tissues, will likely enable more comprehensive identification of UC risk genes. 5. Conclusion Our study, utilizing TWAS, has identified three risk genes—ITGB6, MTMR3, and ADCY3—that are significantly associated with ulcerative colitis (UC) susceptibility. This work contributes to the genetic understanding of UC and suggests potential therapeutic targets. However, further research is needed to fully elucidate the biological mechanisms of these genes in UC pathogenesis. Abbreviations Ulcerative colitis, UC inflammatory bowel disease, IBD transcriptome-wide association study, TWAS Genotype-Tissue Expression project, GTE Functional Summary-based Imputation, Fusion Conditional and Joint Analysis, COJO Multimarker Analysis of Genomic Annotation, MAGMA Mendelian randomization, MR expression quantitative trait loci, eQTL Gene Expression Omnibus, GEO linkage disequilibrium, LD Declarations Ethics approval and consent to participate The datasets utilized in this study were sourced from publicly accessible and free databases, namely FinnGen and the Gene Expression Omnibus (GEO). Each of the original studies from which these datasets were derived was conducted in compliance with the ethical standards of the Declaration of Helsinki. Ethical approval was obtained from the respective review committees, and informed consent was secured from all participants involved. Given that our research involves only the analysis of existing, de-identified data, no additional ethical review was required for this study. Our methodology adheres to the guidelines for the use of public databases and ensures the protection of human subjects' data. Consent for publication Not applicable Availability of data and materials The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This work was supported by the Major Research Program of the National Natural Science Foundation of China (52072142). Authors' contributions ZHD: Study design, literature search and manuscript writing. JLJ: Study selection and data analysis. KY and DLZ: Data collection. DGW: Article Guidance. All authors revised the manuscript and approved the final manuscript as submitted and agree to be accountable for all aspects of the work. Acknowledgements Thank for all the patients in this research, thank for all the scholars in this article. Thank for FinnGen and GTXv8 database. Thank for all the teammates for supporting this research. We are also particularly grateful to our colleagues in The First Affiliated Hospital of Jilin University for their contributions. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. References Khor B, Gardet A, Xavier RJ. Genetics and pathogenesis of inflammatory bowel disease. Nature. 2011;474(7351):307–17. 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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-5133569","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374349974,"identity":"b4982a4f-32d3-4268-b603-5d966f21effa","order_by":0,"name":"Zhenhua dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACNvb+DwckeNjq+9mbDxCnhY/ngOEBCxk+xpk9xxKI0yInkWB8oMJGjnHDjRwDIh0mkZBw4EaOGTPDjZyPN94w2MnpNhDSwvPgwMEZZ9LYGHvebracw5BsbHaAkBb2xIbDkj3HeJjZc7dJ8zAcSNxGUAtDMsPhv//+S7Ax5DwjUgtHGgMokA14OHLYiNTCcwasJUGC55ix5RwDIvwi397D/AGkxf5488Mbbyrs5AhqQQESPERGDbIWUnWMglEwCkbBiAAAvRVB5O1IW+UAAAAASUVORK5CYII=","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":true,"prefix":"","firstName":"Zhenhua","middleName":"","lastName":"dong","suffix":""},{"id":374349975,"identity":"c94d052c-ac98-4170-8e44-1cafe9c2e7cf","order_by":1,"name":"Jianling Jia","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jianling","middleName":"","lastName":"Jia","suffix":""},{"id":374349976,"identity":"dacbe681-9d07-4103-a4d2-85f5deef7d4d","order_by":2,"name":"Donghui Ren","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Donghui","middleName":"","lastName":"Ren","suffix":""},{"id":374349977,"identity":"5b573aaf-78c6-41d1-bbdb-f7250b8c07ba","order_by":3,"name":"Kai Yu","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Yu","suffix":""},{"id":374349978,"identity":"e378dac6-a308-4239-959f-2dc04f3f1c83","order_by":4,"name":"Dingliang Zhao","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Dingliang","middleName":"","lastName":"Zhao","suffix":""},{"id":374349979,"identity":"bc440213-17e2-46dc-9645-1bbdd061aadc","order_by":5,"name":"Daguang Wang","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Daguang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-09-22 17:59:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5133569/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5133569/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69349007,"identity":"51b89407-4f25-4c00-8a04-dad1b98e4770","added_by":"auto","created_at":"2024-11-19 12:41:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":604066,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of this study. UC, ulcerative colitis; GWAS, genome-wide association; GTEx, Genotype-Tissues Expression Project; TWAS, transcriptome-wide association studies; FDR, False Discovery Rate\u003c/p\u003e","description":"","filename":"Figure146.png","url":"https://assets-eu.researchsquare.com/files/rs-5133569/v1/e6a3643e01ad42f74f951abe.png"},{"id":69347854,"identity":"aab1031c-7968-40b7-b857-d9c091a4ee0a","added_by":"auto","created_at":"2024-11-19 12:33:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":165656,"visible":true,"origin":"","legend":"\u003cp\u003eThe COJO result. (A) SANBR and ANSA2P in sigmoid colon; (B) AAMP and ARPC2 in sigmoid colon; (C) AHSA2P and C2orf74-DT in transverse colon; (D) AAMP and ARPC2 in transverse colon. The top panel highlights all genes in the region. The marginally associated TWAS genes are shown in blue, and the jointly significant genes are shown in green. The bottom panel shows a regional Manhattan plot of GWAS data before (grey) and after (blue) conditioning on the predicted expression of the green genes.\u003c/p\u003e","description":"","filename":"Onlinefigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5133569/v1/2ab2687ab0078b859eeac929.png"},{"id":69347858,"identity":"100c7e88-19b6-4535-aa43-e17e64e854a7","added_by":"auto","created_at":"2024-11-19 12:33:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":65348,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram. UTMOST identified 49 significant genes associated with UC, FUSION identified 358, COJO identified 9 and MAGMA identified 803, of which 5 were common.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5133569/v1/5ae611e00228991f315ece5c.png"},{"id":69349006,"identity":"acdca3bc-e10c-4558-b268-5db8ab993d90","added_by":"auto","created_at":"2024-11-19 12:41:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":18432,"visible":true,"origin":"","legend":"\u003cp\u003eThe forest plot of MR. WR, Wald Ratio, CI, Confidence Intervals\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5133569/v1/b01d35a0d74042d4269ba27d.png"},{"id":69347859,"identity":"489aa83c-2a1b-4462-a586-0332126f6d7d","added_by":"auto","created_at":"2024-11-19 12:33:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":644124,"visible":true,"origin":"","legend":"\u003cp\u003eGeneMania gene network. (A) ITGB6 as the core; (B) ADCY3 as the core; (C) MTMR3 as the core; B) three genes as the core\u003c/p\u003e","description":"","filename":"Onlinefigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5133569/v1/5948097356e6a06bebf37e06.png"},{"id":69347855,"identity":"24451356-df9c-4e62-a255-6057c6c82997","added_by":"auto","created_at":"2024-11-19 12:33:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":311193,"visible":true,"origin":"","legend":"\u003cp\u003eThe result of Single-cell RNA sequence analysis. (A) all cells are clustered into 13 clusters; (B) all cells are classified into 9 cell types; (C, D) the expression level of targeted genes in each cluster.\u003c/p\u003e","description":"","filename":"Onlinefigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5133569/v1/cea4d43c1d5585e65dd10674.png"},{"id":70040556,"identity":"c3ffe708-d2bc-47ec-94f5-e4550d423ce4","added_by":"auto","created_at":"2024-11-27 17:56:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2524838,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5133569/v1/c782f656-92f5-457c-aae5-65fcefb2094c.pdf"}],"financialInterests":"","formattedTitle":"Novel insight into the gene etiology of ulcerative colitis gained from transcriptome association study and single cell sequence analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eUlcerative colitis is a prevalent chronic inflammatory bowel disease characterized by a relapsing and remitting course, imposing a significant burden on patients' quality of life and healthcare system [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The disease is typified by mucosal inflammation, often initiating in the rectum and potentially extending to involve the entire colon. Clinical manifestations of UC include bloody diarrhea, abdominal pain, fecal urgency, and tenesmus [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The diagnosis of UC relies heavily on clinical symptoms and colonoscopy findings. With the rapid advancement of medical technology, drugs such as 5-aminosalicylates (5-ASA), thiopurines, and biological agents have been instrumental in alleviating patient suffering [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, approximately 15% of UC patients eventually require surgery due to UC complications, underscoring the urgency of elucidating the disease's etiology and identifying novel therapeutic targets [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUC is a multifaceted condition, involving complex interactions among susceptibility genes, immune dysregulation, environmental factors, and the gut microbiome. Familial aggregation studies indicate that 8%-14% of UC patients have a family history of inflammatory bowel disease (IBD), with first-degree relatives exhibiting a fourfold increased risk, highlighting the genetic component in UC pathogenesis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Numerous researchers have endeavored to elucidate the role of specific genes in UC development. For instance, gene \u003cem\u003efucosyltransferase 8\u003c/em\u003e has been shown to modulate the properties of the mucus layer by regulating the levels of MUC1, MUC2, and MUC5AC, which help facilitate the communication between bacterial and epithelial cells, resulting in proregression of UC finally. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Another example is gene \u003cem\u003etripartite motif-containing 22\u003c/em\u003e, which can exacerbate inflammation by modulating the NF-κB signaling pathway, thereby promoting UC [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Despite these insights, the number of genes definitively linked to UC remains limited.\u003c/p\u003e \u003cp\u003eGWAS have successfully mapped numerous risk loci associated with IBD, with \u003cem\u003eHLA-DQA1\u003c/em\u003e being particularly implicated in UC susceptibility. However, the majority of GWAS-identified variants are located in non-coding regions, which constrains our understanding of gene transcriptional regulation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Transcriptome-wide association studies (TWAS) offer a novel approach by integrating GWAS data with gene expression profiles from various tissues or cell types, allowing for the direct mapping of genetic variants to gene expression and reducing the number of comparisons, thereby enhancing study precision [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. TWAS has been successfully applied to identify risk genes for a range of diseases, including age-related macular degeneration [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], systemic lupus erythematosus [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and stroke [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we leverage the integration of UC GWAS data with the GTExV8 database to identify risk genes for UC. These genes are further validated using MR. We employ GeneMANIA analysis to explore gene functions and single-cell RNA sequencing to determine the cell types in which these genes are enriched. Our findings aim to contribute to the discovery of potential causal genes, which may inform novel treatment and intervention strategies for UC.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 UC GWAS Data Source\u003c/h2\u003e \u003cp\u003eWe amalgamated two UC summary datasets: one from the R11 version of the FinnGen database, comprising 6,435 cases and 446,419 controls of European ancestry, and another from the Integrative Epidemiology Unit database (ieu-a-32), consisting of 6,968 cases and 20,464 controls of European ancestry [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. UC diagnoses were strictly adhered to the K11 standard of the International Statistical Classification of Diseases and Related Health Problems (ICD-10). Further details are accessible on the FinnGen website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/en/access_results\u003c/span\u003e\u003cspan address=\"https://www.finngen.fi/en/access_results\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A study flowchart is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Cross-Tissue TWAS Analysis\u003c/h2\u003e \u003cp\u003eEmploying the Unified Test for Molecular Signature (UTMOST) method, we conduct a cross-tissue TWAS using UC GWAS and expression quantitative trait loci (eQTL) data from 49 tissues of the GTExV8 project. Gene-trait associations are calculated for each tissue, and the statistics are utilized to evaluate associations across all tissues using the generalized Berk-Jones test. The utmost method facilitates the identification of genes shared across tissues and those specific to a single tissue, thereby enhancing the precision of TWAS [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. After false discovery rate (FDR) correction, genes with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are subjected to further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Single-Tissue TWAS Analysis\u003c/h2\u003e \u003cp\u003eWe perform a TWAS analysis by integrating UC GWAS with eQTL data from sigmoid and transverse colon tissues of GTExV8 project to identify candidate genes. Initially, the Fusion software is used to construct five predictive models\u0026mdash;Best Linear Unbiased Prediction (BLUP), Bayesian Sparse Linear Mixed Model (BSLMM), Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net, and top SNPs from reference expression panels\u0026mdash;to estimate gene expression weights in colon tissue. The optimal model is selected based on the R2 value derived from fivefold cross-validation. Subsequently, we combine gene expression weights with UC GWAS data to calculate association significance using the formula: ZTWAS\u0026thinsp;=\u0026thinsp;W\u0026prime;Z(W\u0026prime;LW)1/2ZTWAS​=(W\u0026prime;LW)1/2W\u0026prime;Z​, where Z represents the BID score, W the weight, and L the SNP-correlation linkage disequilibrium matrix. After FDR correction, genes with TWAS p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are selected for further analysis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Gene-Based Association Analysis\u003c/h2\u003e \u003cp\u003eMAGMA is utilized to construct multiple regression models, calculating the cumulative effect of SNPs mapping to specific genes, thereby identifying genes or gene sets associated with UC [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. MAGMA determines the association strength between identified genes and phenotypes based on gene scores, allowing for the identification of more genes and gene sets while maintaining an accurate type 1 error rate. After FDR correction, genes with FDR p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are considered as candidate genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Conditional and Joint Analysis\u003c/h2\u003e \u003cp\u003eFollowing the Fusion and MAGMA analyses, we identifiy numerous associated genes that might be influenced by linkage disequilibrium (LD). COJO is employed to ascertain which genes are conditionally independent. COJO provides a comprehensive depiction of gene structure related to the phenotype by considering the LD between candidate genes. The analysis categorizes genes into two groups: jointly significant genes, whose results remain significant, and marginally significant genes, which become non-significant [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Mendelian Randomization\u003c/h2\u003e \u003cp\u003eMR was conducted using the \"TwoSampleMR\" R package. In MR, gene expression data in colon tissue (sigmoid and transverse) served as the exposure, and UC GWAS as the outcome. Significant (P\u0026thinsp;\u0026lt;\u0026thinsp;5e-8) and independent SNPs (R2\u0026thinsp;\u0026lt;\u0026thinsp;0.001 within a 10,000 kb window) from cis-eQTL data were selected as instrumental variables (IVs) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Only one IV is chosen for each exposure, and word ratio is applied to explore the causal effect between exposure and outcome. A P-value of less than 0.05 indicates the presence of a causal relationship [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The causal relationship between QTL and GWAS is further strengthened by MR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 GeneMANIA Analysis and Single-Cell RNA Sequencing\u003c/h2\u003e \u003cp\u003eThe GeneMANIA platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genemania.org/\u003c/span\u003e\u003cspan address=\"https://genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is utilized to predict the functions of our genes of interest. It provides a comprehensive set of results, including physical interactions, co-expression, and enrichment pathways for targeted genes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To explore the cell type-specific expression of target genes, we analyze single-cell RNA sequencing data (GSE214695) from colon tissues of six UC patients obtained from the Gene Expression Omnibus (GEO) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. We employed the \"Read10X\" package to read and integrate raw single-cell RNA-seq data. The \"Seurat\" package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://satijalab.org/seurat/\u003c/span\u003e\u003cspan address=\"https://satijalab.org/seurat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is used to create a Seurat object, excluding genes present in fewer than three cells and cells with less than 200 features. Cells with 200\u0026thinsp;\u0026lt;\u0026thinsp;RNA features\u0026thinsp;\u0026lt;\u0026thinsp;2500 and mitochondria percent\u0026thinsp;\u0026ge;\u0026thinsp;5% are included. Then, we perform normalization, finding high variable features, scale for data. After harmonizing data to eliminate batch effects, we conduct Uniform Manifold Approximation and Projection (UMAP). Cell types are annotated using specific markers.(Plasma cell - \u003cem\u003eDERL3, MZB1, XBP1;\u003c/em\u003e CD4\u0026thinsp;+\u0026thinsp;T cell \u0026ndash; \u003cem\u003eCD4\u003c/em\u003e; Neutrophils cell - \u003cem\u003ePROK2, CMTM2, CXCL8, FCRG3B, AQP9, S100A8, S100A9; CD8\u0026thinsp;+\u0026thinsp;T - CD8A, CD8B\u003c/em\u003e; Fibroblasts cell - \u003cem\u003eADAMDEC1, CP, FABP4\u003c/em\u003e; Macrophages cell - \u003cem\u003eCD68, CD14 C1QA, C1QB\u003c/em\u003e; Epithelial cell - \u003cem\u003eEPCAM, AQP8, BEST4, MUC2, OLFM4, PLCG2, TRPM5, ZG16\u003c/em\u003e; B cell - \u003cem\u003eCD20 (MS4A1)\u003c/em\u003e; Cycling cells (PC and B cells) - \u003cem\u003eTUBB, TOP2A, MKI67\u003c/em\u003e) The density UMAP plots are utilized to depict the cell type-specific expression for these genes.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Utmost and Fusion Analysis\u003c/h2\u003e \u003cp\u003eFollowing false discovery rate (FDR) correction, the utmost software identified 49 genes across 49 tissues (Supplementary Sheet 1), while the Fusion software identified 358 genes in sigmoid and transverse colon tissues (Supplementary Sheet 2). A total of 11 variants, as determined by both utmost and Fusion analyses, were considered as candidate genes: \u003cem\u003eLINC01882, ARPC2, AHSA2P, ITGB6, AAMP, SANBR, DNAJC27-AS1, MTMR3, C2orf74-DT, ADCY3\u003c/em\u003e, and \u003cem\u003ePIM3\u003c/em\u003e (Supplementary Sheet 3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Conditional and Joint Analysis\u003c/h2\u003e \u003cp\u003eNine of the 11 genes located on chromosomes 2, 18, and 22 passed the conditional and joint analysis, indicating that these genes are jointly significant and not confounded by LD. The results of the COJO analysis are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the sigmoid colon tissue (chromosome 2), the TWAS signal for AHSA2P was significantly attenuated when accounting for the predicted expression of SANBR, suggesting that \u003cem\u003eAHSA2P\u003c/em\u003e did not pass COJO (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Similarly, the TWAS signal for \u003cem\u003eARPC2\u003c/em\u003e was significantly decreased when accounting for the predicted expression of AAMP, indicating that \u003cem\u003eARPC2\u003c/em\u003e did not pass COJO (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Consequently, \u003cem\u003eLINC01882, ITGB6, AAMP, SANBR, DNAJC27-AS1\u003c/em\u003e, and \u003cem\u003eMTMR3\u003c/em\u003e passed COJO.\u003c/p\u003e \u003cp\u003eIn the transverse colon tissue (chromosome 2), the TWAS signal for \u003cem\u003eC2orf74-DT\u003c/em\u003e was significantly decreased when accounting for the predicted expression of \u003cem\u003eAHSA2P\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The TWAS signal for \u003cem\u003eARPC2\u003c/em\u003e significantly declined when accounting for the predicted expression of \u003cem\u003eAAMP\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Thus, \u003cem\u003eLINC01882, AAMP, ADCY3, AHSA2P\u003c/em\u003e, and \u003cem\u003ePIM3\u003c/em\u003e passed COJO.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 MAGMA Analysis\u003c/h2\u003e \u003cp\u003eAmong the 9 genes, 5 genes were identified as being related to UC in the MAGMA analysis: \u003cem\u003eITGB6, AAMP, MTMR3, ADCY3\u003c/em\u003e, and \u003cem\u003ePIM3\u003c/em\u003e. A Venn diagram was created to illustrate the genes detected by Fusion, utmost, and MAGMA (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Mendelian Randomization\u003c/h2\u003e \u003cp\u003eAmong 5 genes, \u003cem\u003eITGB6, AAMP, MTMR3\u003c/em\u003e were located in sigmoid tissue, and \u003cem\u003eADCY3, PIM3\u003c/em\u003e in transverse tissue. MR results supported the causality of \u003cem\u003eITGB6, MTMR3\u003c/em\u003e in sigmoid and \u003cem\u003eADCY3\u003c/em\u003e in transverse colon. The forest plot is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGenes \u003cem\u003eITGB6, MTMR3\u003c/em\u003e, and \u003cem\u003eADCY3\u003c/em\u003e, mapped to chromosome 2p160099667-160200313 in sigmoid colon, chromosome 22p29883169-30030868 in sigmoid colon, and chromosome 2p24819169-24920237 in transverse colon respectively, showed a significant association with UC according to Fusion. MR analysis substantiated the causal links between \u003cem\u003eITGB6, MTMR3, ADCY3\u003c/em\u003e, and UC. The associations between UC and \u003cem\u003eITGB6\u003c/em\u003e [odds ratios (OR), 1.138, 95% confidence intervals (CI), 1.067\u0026ndash;1.215, P value, 8.58E-05], \u003cem\u003eMTMR3\u003c/em\u003e [OR, 0.836, 95% CI, 0.755\u0026ndash;0.925, P, 5.45E-04], and \u003cem\u003eADCY3\u003c/em\u003e [OR, 1.189, 95% CI, 1.078\u0026ndash;1.311, P, 5.3E-04] were significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 GeneMANIA Analysis\u003c/h2\u003e \u003cp\u003eThe potential gene interaction network with \u003cem\u003eITGB6\u003c/em\u003e as a center is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea. The functional pathways that genes related to \u003cem\u003eITGB6\u003c/em\u003e enrich include the cAMP metabolic process, phosphorus-oxygen lyase activity, and response to glucagon. The gene interaction network with \u003cem\u003eADCY3\u003c/em\u003e as a core is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, with functional pathways including the cAMP metabolic process, phosphorus-oxygen lyase activity, and cyclic purine nucleotide metabolic process. The gene interaction network with \u003cem\u003eMTMR3\u003c/em\u003e as a center is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, with functional pathways including the phosphatidylinositol metabolic process, glycerophospholipid biosynthetic process, and phospholipid biosynthetic process. The gene interaction network with the three genes as the core is exhibited in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed, with functional pathways including the cAMP metabolic process, phosphorus-oxygen lyase activity, and response to glucagon.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Single-Cell RNA Sequencing Analysis\u003c/h2\u003e \u003cp\u003eAfter UMAP analysis, all cells were clustered into 13 clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). These clusters were further classified into 9 cell types based on typical cell markers, including plasma cells, CD4\u0026thinsp;+\u0026thinsp;T cells, neutrophils, CD8\u0026thinsp;+\u0026thinsp;T cells, fibroblasts, macrophages, epithelial cells, B cells, and cycling cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). All three coding genes had expression data in colon tissue of UC patients. Figures\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed show the expression data of the three genes across the 9 cell types. It was observed that \u003cem\u003eADCY3\u003c/em\u003e primarily expresses in B cells, \u003cem\u003eITGB6\u003c/em\u003e in epithelial cells, and \u003cem\u003eMTMR3\u003c/em\u003e in all cell types, particularly epithelial cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, leveraging UC GWAS data and the GTExV8 database, we conducted a comprehensive TWAS to explore the relationship between gene expression and UC susceptibility. Our integrative approach, employing cross-tissue TWAS for discovery and colon tissue TWAS, MAGMA, and MR for validation, successfully identified three genes associated with UC. Single-cell RNA sequencing analysis further delineated the primary cell types expressing these genes in the colon tissue, while GeneMANIA analysis expanded our understanding for their potential biological functions.\u003c/p\u003e \u003cp\u003eMulti-omics association studies have gained momentum in the identification of risk genes for inflammatory bowel diseases (IBD), including Crohn's disease (CD) and UC. For instance, a study utilizing Summary-PrediXcan and Summary-MultiXcan methods identified 39 novel genes in the colon whose expression levels influence IBD susceptibility [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Japanese researchers were the first to construct effector memory T cells (TEM) eQTL data and, through TWAS, identified \u003cem\u003etenascin-XA in\u003c/em\u003e TEM as related to IBD risk [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Another study compared IBD patients with and without psychiatric comorbidities (PC) and identified risk genes, including \u003cem\u003eRBPMS\u003c/em\u003e in skeletal muscle and several others in brain regions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Lastly, \u003cem\u003eHLA-DRB1\u003c/em\u003e and \u003cem\u003eTAP2\u003c/em\u003e were detected as associated with IBD susceptibility in the colon through the combination of TWAS and messenger RNA expression data [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These studies underscore the potential of TWAS in identifying disease susceptibility genes, and our study adds to this body of work by identifying \u003cem\u003eITGB6, MTMR3\u003c/em\u003e, and \u003cem\u003eADCY3\u003c/em\u003e as risk genes related to UC.\u003c/p\u003e \u003cp\u003e \u003cem\u003eITGB6\u003c/em\u003e, primarily encoding a member of the integrin superfamily, plays a role in cell signaling and adhesion [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Increased \u003cem\u003eITGB6\u003c/em\u003e expression has been observed in inflamed colon tissue from IBD patients, and in a mouse model, ITGB6 exacerbated dextran sulfate sodium (DSS)-induced colitis and was associated with poor prognosis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Further research suggests that \u003cem\u003eITGB6\u003c/em\u003e may promote macrophage infiltration, pro-inflammatory cytokine secretion, upregulation of integrin ligands, and activation of the Stat1 signaling pathway. \u003cem\u003eITGB6\u003c/em\u003e's role in intestinal fibrosis, a severe IBD complication, has also been noted, with potential to exacerbate the condition via the focal adhesion kinase (FAK/AKT) pathway [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our single-cell RNA sequencing analysis aligns with these findings, showing \u003cem\u003eITGB6\u003c/em\u003e expression primarily in epithelial cells.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMTMR3\u003c/em\u003e encodes a member of the myotubularin dual specificity protein phosphatase gene family whose structrue is similar to myotubularin but in addition contains a FYVE domain and an N-terminal PH-GRAM domain.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] Host pattern recognition receptors (PRRs) could regulate the balance between microbial interactions and cytokine production, which is relevant to IBD pathogenesis. In human macrophages, \u003cem\u003eMTMR3\u003c/em\u003e may increase IBD risk by reducing PRR-induced phosphatidylinositol 3-phosphate and autophagy levels, prompting PRR-induced caspase-1 and NFκB signaling pathway activation, enhancing autocrine IL-1β even overall cytokine secretion.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] GWAS have also highlighted \u003cem\u003eMTMR3\u003c/em\u003e's role in autophagy, relevant to CD, with functional experiments confirming increased risk with higher \u003cem\u003eMTMR3\u003c/em\u003e expression.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cem\u003eADCY3\u003c/em\u003e encodes adenylyl cyclase 3 which is a membrane-associated enzyme and catalyzes the formation of the secondary messenger cyclic adenosine monophosphate. \u003cem\u003eADCY3\u003c/em\u003e is widely expressed in various tissues and involved in IBD. Some scholars calculated a comprehensive gene expression data of distal colon tissue samples from 40 healthy African Americans, and identified \u003cem\u003eADCY3\u003c/em\u003e as risk gene implicated in IBD.[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] Another GWAS is conducted between 2345 cases of African Americans with IBD and 5002 individuals without IBD to determine \u003cem\u003eADCY3\u003c/em\u003e as IBD susceptibility Loci.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn summary, our study applied TWAS and multiple validation methods to identify three UC-associated risk genes and elucidate their biological functions. While TWAS enhances statistical power and mitigates reverse causality, our study has several limitations. First, the focus on European ancestry limits the generalizability of our findings. Second, the absence of independent validation data may reduce the study's credibility. Third, the stringent criteria for significant cis-heritability genes in TWAS may have led to the exclusion of some genes. Lastly, the sample and tissue limitations in the GTEx V8 database may have restricted the identification of more UC risk genes. Future studies, with the availability of more high-throughput gene expression data across diverse tissues, will likely enable more comprehensive identification of UC risk genes.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur study, utilizing TWAS, has identified three risk genes\u0026mdash;ITGB6, MTMR3, and ADCY3\u0026mdash;that are significantly associated with ulcerative colitis (UC) susceptibility. This work contributes to the genetic understanding of UC and suggests potential therapeutic targets. However, further research is needed to fully elucidate the biological mechanisms of these genes in UC pathogenesis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eUlcerative colitis, UC\u003c/p\u003e\n\u003cp\u003einflammatory bowel disease, IBD\u003c/p\u003e\n\u003cp\u003etranscriptome-wide association study, TWAS\u003c/p\u003e\n\u003cp\u003eGenotype-Tissue Expression project, GTE\u003c/p\u003e\n\u003cp\u003eFunctional Summary-based Imputation, Fusion\u003c/p\u003e\n\u003cp\u003eConditional and Joint Analysis, COJO\u003c/p\u003e\n\u003cp\u003eMultimarker Analysis of Genomic Annotation, MAGMA\u003c/p\u003e\n\u003cp\u003eMendelian randomization, MR\u003c/p\u003e\n\u003cp\u003eexpression quantitative trait loci, eQTL\u003c/p\u003e\n\u003cp\u003eGene Expression Omnibus, GEO\u003c/p\u003e\n\u003cp\u003elinkage disequilibrium, LD\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets utilized in this study were sourced from publicly accessible and free databases, namely FinnGen and the Gene Expression Omnibus (GEO). Each of the original studies from which these datasets were derived was conducted in compliance with the ethical standards of the Declaration of Helsinki. Ethical approval was obtained from the respective review committees, and informed consent was secured from all participants involved. Given that our research involves only the analysis of existing, de-identified data, no additional ethical review was required for this study. Our methodology adheres to the guidelines for the use of public databases and ensures the protection of human subjects\u0026apos; data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Major Research Program of the National Natural Science Foundation of China (52072142).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZHD: Study design, literature search and manuscript writing. JLJ: Study selection and data analysis. KY and DLZ: Data collection. DGW: Article Guidance. All authors revised the manuscript and approved the final manuscript as submitted and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThank for all the patients in this research, thank for all the scholars in this article. Thank for FinnGen and GTXv8 database. Thank for all the teammates for supporting this research. We are also particularly grateful to our colleagues in The First Affiliated Hospital of Jilin University for their contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePublisher\u0026rsquo;s note\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKhor B, Gardet A, Xavier RJ. Genetics and pathogenesis of inflammatory bowel disease. Nature. 2011;474(7351):307\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSegal JP, LeBlanc JF, Hart AL. Ulcerative colitis: an update. 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Exp Cell Res. 2022;411(2):113003.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobinson FL, Dixon JE. Myotubularin phosphatases: policing 3-phosphoinositides. Trends Cell Biol. 2006;16(8):403\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLahiri A, Hedl M, Abraham C. MTMR3 risk allele enhances innate receptor-induced signaling and cytokines by decreasing autophagy and increasing caspase-1 activation. Proc Natl Acad Sci U S A. 2015;112(33):10461\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoefkens E, et al. Genetic association and functional role of Crohn disease risk alleles involved in microbial sensing, autophagy, and endoplasmic reticulum (ER) stress. Autophagy. 2013;9(12):2046\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHulur I, et al. Enrichment of inflammatory bowel disease and colorectal cancer risk variants in colon expression quantitative trait loci. BMC Genomics. 2015;16(1):138.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrant SR, et al. Genome-Wide Association Study Identifies African-Specific Susceptibility Loci in African Americans With Inflammatory Bowel Disease. Gastroenterology. 2017;152(1):206\u0026ndash;e2172.\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":"Ulcerative Colitis, TWAS, MAGMA, Mendelian Randomization, Single-cell RNA sequencing","lastPublishedDoi":"10.21203/rs.3.rs-5133569/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5133569/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eUlcerative colitis (UC) is a prevalent chronic gastrointestinal disease. Gene plays an important role in UC pathogenesis. Therefore, we aim to identify UC susceptibility genes and specific cell types expressing these genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe conducted a cross-tissue transcriptome-wide association study (TWAS) by integrating UC GWAS with 49 tissues gene-expression matrix from the Genotype-Tissue Expression project (GTEx). Subsequently, we employed Functional Summary-based Imputation (Fusion) to verify candidate genes within colon tissue. Conditional and Joint Analysis (COJO) was utilized to filter out genes potentially influenced by linkage disequilibrium. Multimarker Analysis of Genomic Annotation (MAGMA) was then applied to pinpoint genes relevant to UC. Validation of the selected genes was performed using Mendelian randomization (MR). GeneMANIA analysis was conducted to elucidate biological functions of identified genes. Finally, single-cell RNA sequencingwas employed to ascertain cell types in which these genes are enriched.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe cross-tissue TWAS, Fusion and MAGMA analyses identified a total of 5 genes, of which 3 genes,\u003cem\u003e ADCY3\u003c/em\u003e, \u003cem\u003eITGB6\u003c/em\u003e, and\u003cem\u003e MTMR3,\u003c/em\u003e were retained after MR. These genes were found to be implicated in several functional pathways, including the cAMP metabolic process and phosphorus-oxygen lyase activity. Furthermore, we observed \u003cem\u003eADCY3\u003c/em\u003e predominantly enriched in B cells, while \u003cem\u003eITGB6\u003c/em\u003e and \u003cem\u003eMTMR3\u003c/em\u003e enriched in epithelial cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eOur study has identified three genes associated with UC susceptibility. These findings not only enhance our understanding of the genetic underpinnings of UC but also offer novel avenues for exploring molecular mechanisms and potential therapeutic targets for UC.\u003c/p\u003e","manuscriptTitle":"Novel insight into the gene etiology of ulcerative colitis gained from transcriptome association study and single cell sequence analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 12:33:41","doi":"10.21203/rs.3.rs-5133569/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":"9081cfd9-272e-4487-be80-2a0d9f9713d2","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-27T17:48:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-19 12:33:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5133569","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5133569","identity":"rs-5133569","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}