Integrative multi-omic analyses identify candidate targets for celiac disease involving tissue-specific regulation

Integrative multi-omic analyses identify candidate targets for celiac disease involving tissue-specific regulation | 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 Integrative multi-omic analyses identify candidate targets for celiac disease involving tissue-specific regulation Jiazheng Sun, Yulan Zeng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5246482/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 Introduction: Celiac disease (CeD) is an autoimmune condition characterized by a reversible inflammatory reaction in the mucous membrane of the small intestine. Nevertheless, there is a limited availability of efficient control approaches. Prior research has demonstrated that pharmacological targets supported by genetic evidence can greatly enhance the efficacy of drug development. Hence, the study aims to integrate transcriptomic and proteomic information to identify candidate targets for CeD. Methods The study employed proteome-wide Mendelian randomization (MR) analysis of circulating plasma proteins to investigate their causal association with CeD. The candidate targets for CeD were further assessed employing colocalization analysis, transcriptome-wide summary-data-based Mendelian randomization (SMR) analysis, multimarker analysis of genomic annotation (MAGMA) gene-based analysis, and bulk RNAseq-based differential expression analysis. For the proteins that were identified, extended Phenome-wide association studies (PheWAS) were conducted to assess their side-effect profiles, while the DGIdb database provided information on the approved or investigated drugs for candidate targets. Results Systematic MR analysis identified 22 candidate targets for CeD. Among the proteins analyzed, BTN2A1 passed all subsequent verification analyses. Additionally, three proteins, including CatH, IL-18R1, and PTPRC, passed the majority of the subsequent verification analyses. The other 18 proteins were also candidate targets (Trehalase, CD226, SH2B3, ICOSLG, ULK3, Park7, ALDH2, RABEP1, TNFRSF9, COL11A2, GNPDA1, IL-1RL1, B3galt6, TNFSF11, CCL21, BTN3A3, OLFM2 and Colipase). Conclusions The study employed a combination of human transcriptomic and proteomic information, employing several analytical methods. As a result, 22 proteins, divided into four tiers, were identified as prospective therapeutic targets for CeD. Celiac disease Mendelian randomization Proteome Transcriptome Therapeutic targets Figures Figure 1 Figure 2 Figure 3 1. Introduction Celiac disease (CeD) is an autoimmune condition characterized by a reversible inflammatory reaction in the mucous membrane of the small intestine. ( 1 ) Genetically predisposed individuals experience the activation of this mechanism upon consuming gluten. The sole efficacious remedy for individuals with CeD is presently a diet devoid of gluten. ( 2 ) However, the treatment has its constraints and is arduous to sustain over an extended period. ( 3 ) Therefore, it is crucial to identify and understand the biomarkers involved in the molecular process of CeD to identify potential targets for therapeutic intervention. With the advancement of bioinformatics and the development of comprehensive human gene research databases, medication target investigation centered around the integration of Mendelian randomization (MR) with proteomics has become notably significant in pharmaceutical development. ( 4 , 5 ) MR analysis employs instrumental variables (IVs), genetic polymorphisms substantially associated with exposure factors, to assess causation. These genetic variations remain unaltered by environmental and behavioral influences, minimizing the potential for reverse causation and interference from other causes. MR analysis can not only reassess the effectiveness of approved medications, uncover novel therapeutic targets, and conduct comprehensive investigations into illness mechanisms but also offer fresh avenues and prospects for drug development and disease management. With integrative genomic analyses and multi-omics information, the study aimed to identify candidate targets for future CeD treatment. The specific process of the study is shown in Fig. 1 . Initially, we acquired the IVs from the plasma proteome data through meticulous quality control. The proteome-wide MR analysis was utilized to identify candidate targets for CeD. In addition, colocalization analysis was applied to mitigate the impact of linkage disequilibrium (LD) effects and confirm if the candidate targets and CeD shared the same causal variant. The study further performed transcriptome-wide summary-data-based Mendelian randomization (SMR) to assess the correlation between alterations in the expression of coding genes for candidate targets and the likelihood of developing CeD. Multimarker analysis of genomic annotation (MAGMA) analysis was employed to confirm the causal association between coding genes for protein targets and CeD. Furthermore, this study utilized the bulk-RNAseq cohorts from the GEO database to validate the differential expression of putative treatment targets in both the cohort with CeD and the cohort of healthy controls. Phenome-wide association studies (PheWAS) were employed to explore any potential adverse effects of prioritizing these candidate targets for CeD, while the DGIdb database provided information on their approved or investigated drugs. 2. Materials and methods 2.1 Data acquisition and processing The pQTL data associated with plasma proteins was obtained from three distinct GWAS cohorts. The UKB-PPP cohort ( 6 ) contains plasma proteome profiles of 54,219 UKB individuals, with information on 2,923 proteins in total. Proteomic analysis was carried out by the Olink platform. The Fenland cohort ( 7 ) collected aggregate information on 4,979 plasma proteins from 10,708 individuals, which were analyzed using SomaScan version 4. The Iceland cohort ( 8 ) includes a proteomic genome-wide association study (GWAS) of 35,559 Icelanders with information on 4907 plasma proteins. The eQTL data, obtained from the eQTLGen consortium ( https://www.eqtlgen.org/ ), was collected for individuals of both genders. The consortium provides information on the expression of 16,987 genes in whole blood. The information is obtained from 37 datasets, which collectively include 31,684 individuals of both genders. In addition, the study obtained the tissue-specific cis-eQTLs from 49 tissues (n = 15,201) from the GTEx (v8) project ( 9 ) to investigate the tissue-specific relationships and potential unintended consequences of medications that target genes. The eQTL data are represented as the effect of each additional allele on a 1-SD change in the gene expression level (mRNA). The information for the CeD was obtained from a previously published GWAS, in which 97,422 SNPs were analyzed in a discovery cohort including 11,812 CeD patients and 229 healthy controls. ( 10 ) Bulk-seq datasets of CeD were obtained from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ). GSE134900 cohort ( 11 ) was composed of duodenal tissue from 51 CeD patients and 44 healthy controls. GSE131705 cohort ( 12 ) was composed of duodenal tissue from 33 CeD patients and 21 healthy controls. 2.2 Proteome-wide MR analysis For MR analysis, the "TwoSampleMR" R package was utilized. The pQTLs were selected as the IVs. The CeD GWAS dataset was selected as the outcome data. The pQTLs were selected based on the following criteria: ( 1 ) their significant association with proteins at the genome-wide level (P < 5 × 10 − 8 ); ( 2 ) The pQTLs were located outside of the Major Histocompatibility Complex (MHC) region (chr6: 25.5–34.0 Mb); ( 3 ) The pQTLs were independently associated (LD clumping r2 10); ( 6 ) Any pQTL with missing information was removed from the analysis. The main analytical technique utilized in this study is the Wald ratio method, specifically designed for cases involving proteins with only one cis-pQTL. The inverse variance weighting (IVW) method is the principal analysis method utilized for proteins with multiple cis-pQTLs. The measured outcome odds ratio represents the risk of CeD for each additional unit of plasma protein. In order to minimize the occurrence of incorrect positive results, the False Discovery Rate (FDR) correction was implemented. Statistically significant results were considered as FDR < 0.05. We employed sensitivity analysis to assess and improve the robustness of the MR analysis. The heterogeneity was evaluated using Cochran's Q test ( 13 ). The significance level of Cochran's Q test was set at P > 0.05, which indicated that there is no heterogeneity in the IVs employed in the inquiry. The evaluation of horizontal pleiotropy was performed by the MR-Egger intercept test ( 14 ). The significance level of the MR-Egger intercept test was set at P < 0.05. 2.3 Colocalization analysis Bayesian colocalization analysis ( 15 ) was employed to ascertain whether the same causal variant influenced both plasma proteins and CeD. Colocalization analysis assumed that there was a single underlying causal variation for each of the two features within a given region. This resulted in establishing five distinct and independent model assumptions (H0-H4). These five model assumptions encompassed all possible correlation possibilities in colocalization analysis. For each model, a posterior probability (PP.H0-PP.H4) was computed. Based on the available evidence, a model hypothesis with a higher posterior probability was more likely to be accurate. A hypothesis based on the H4 model posited that a common source of variation influenced both traits. The H4 model assumption was valid when PP.H4 > 0.8. 2.4 Transcriptome-wide SMR analysis The study further conducted SMR analysis ( 16 ) to assess the correlation between alterations in the expression of coding genes for candidate targets and the likelihood of developing CeD. The SMR analysis utilizes a single SNP (located close to the target gene) that is most strongly associated, serving as the IV. The heterogeneity in the dependent instrument (HEIDI) test was utilized to differentiate linkage in the causal association in the case of more than three SNPs. The screening criteria with SMR P-value threshold 0.05 were employed to assess candidate target coding genes. 2.5 MAGMA gene-based analysis The MAGMA analysis ( 17 ) was utilized to further investigate the correlation between coding genes for protein targets and CeD. MAGMA analysis employs multiple regression techniques to convert SNP-level correlations derived from GWAS into gene-level associations, taking into account LD across variants and identifying the effects of multiple variants. Furthermore, MAGMA analysis assigns a P-value to each gene to evaluate its correlation with the disease. Statistically significant results were considered as FDR < 0.05, which indicates a substantial relationship between the gene and CeD. 2.6 Protein-protein interaction network In order to examine the connections between candidate targets, the study conducted a protein-protein interaction (PPI) network analysis for candidate targets with CeD, which was conducted using the STRING database ( https://string-db.org/ ) version 11.5. 2.7 Functional enrichment analysis KEGG enrichment analysis, a widely used bioinformatics method, is employed to examine gene pathways and functional enrichment in a certain geneset, which was performed through the “clusterProfiler” R package. 2.8 PheWAS analysis In order to further evaluate the horizontal pleiotropy of candidate targets and possible side effects, a PheWAS was performed on the GWAS ATLAS ( https://atlas.ctglab.nl/ ) ( 18 ) database containing 4,756 GWAS from 473 unique studies across 3,302 unique traits and 28 domains. The significance level of PheWAS analysis was set at P < 1.05e-5 (0.05/4,756). 2.9 Statistical analysis Statistical differences between groups were determined by Student's t-test for normally distributed variables, and for normally distributed variables, statistical differences between groups were determined by the Wilcoxon test. The R software (version 4.3.3) and the smr-1.3.1-win software were used in the study. 3. Result 3.1 The candidate targets for CeD The proteome-wide MR analysis for three cohorts displayed candidate targets related to CeD. (Fig. 2 A) A total of 22 proteins were found to have a significant association with CeD (FDR < 0.05), including 13 proteins from the UKB-PPP cohort, 6 proteins from the Iceland cohort, and 8 proteins from the Fenland cohort. (Fig. 2 B and STable 1 ) Among these, proteins BTN2A1, GNPDA1, IL-18R1, IL-1RL1, Park7, PTPRC, SH2B3, Trehalase, ULK3, OLFM2, ICOSLG, Colipase, B3galt6, COL11A2, and ALDH2 exhibited a positive correlation with CeD. Conversely, proteins CCL21, CD226, RABEP1, TNFRSF9, TNFSF11, BTN3A3, and CatH showed a negative correlation with CeD. Out of these proteins, B3galt6, BTN3A3, CCL21, IL-18R1, and IL-1RL1, their replication was confirmed in two distinct datasets. None of the candidate targets exhibited substantial heterogeneity and pleiotropy, as indicated in STable 2 and STable 3 . The F statistic of the IVs for each candidate target demonstrated a robust measure of tool efficacy ( STable 4 ). 3.2 Conclusions of the colocalization analysis The Bayesian colocalization analysis ( STable 5 ) revealed a significant colocalization between ALDH2C, CL21, PTPRC, B3galt6, RABEP1, ICOSLG, IL-18R1, BTN2A1, IL-1RL1, and CeD, demonstrating that these proteins and CeD shared causal genetic drivers (PPH4 > 0.8). In addition, the findings suggested that Park7, TREH, OLFM2, BTN3A3, COL11A2, BTN3A3, CCL21, CatH, GNPDA1, IL-1RL1, and CeD may be influenced by two causal variants in the genome (PPH3 > 0.8). 3.3 Associations of the protein-coding gene expression on CeD The study mapped 22 proteins to 22 coding genes. The SMR analysis revealed that 9 genes exhibited consistent results for CeD with their respective proteins. (Fig. 2 ) The genes CTSH, CD226, TNFSF11, and ICOSLG were found to be related to a decreased risk of CeD in several blood samples or tissue-specific samples. Conversely, B3GALT6, ULK3, GNPDA1, BTN2A1, and PARK7 showed a notable correlation with an elevated susceptibility to CeD. The genes IL18R1, RABEP1, COL11A2, SH2B3, IL1RL1, and ALDH2 exhibited varying impacts on the risk of CeD in different blood or tissue-specific samples, which could indicate the presence of potential off-target effects. For instance, the upregulation of the COL11A2 gene in plasma is associated with an elevated risk of CeD, which aligns with the function of the COL11A2 protein in plasma. Nevertheless, the likelihood of developing CeD, as indicated by the presence of the COL11A2 gene, was diminished in brain tissue. The impact of ALDH2 gene expression on CeD varied in plasma and different tissues. Consequently, medications that specifically target certain genes in various organs may have unintended effects on other genes. 3.4 Conclusions of the MAGMA analysis Following the removal of missing information, a total of 18 protein-coding genes were enrolled in the MAGMA analysis. Out of the genes analyzed, 14 protein-coding genes showed a significant correlation with CeD (FDR < 0.05), with COL11A2 being the gene that showed the strongest correlation with CeD ( STable 6 ). 3.5 Conclusions of the differential expression analysis The differential expression analysis revealed substantial heterogeneity in the expression of nine genes in both RNAseq cohorts including CeD samples and healthy samples ( SFigure 1 ). It is worth noting that there is heterogeneity in the expression of the four genes and conclusions of Proteome-wide MR analysis, which may be caused by the different organizational sources of proteome data and RNAseq data. Ultimately, five genes (BTN2A1, PTPRC, SH2B3, IL18R1, and CTSH) passed bulk RNAseq-based differential expression analysis. 3.6 The druggability analysis of candidate targets After performing proteome-wide MR analysis, colocalization analysis, transcriptome-wide SMR analysis, MAGMA gene-based analysis, and bulk RNAseq-based differential expression analysis, the study divided the 22 identified proteins into four tiers based on the following criteria: ( 1 ) The direction of effects in primary MR analysis and SMR analysis are consistent (both ORs are greater than 1 or less than 1). ( 2 ) pQTLs of the protein demonstrate colocalization with CeD; ( 3 ) The protein passes transcriptome-wide SMR analysis (p-value 0.05); ( 4 ) The protein-coding gene is differentially expressed both RNAseq cohorts including CeD samples and healthy samples. Under the prerequisite of meeting the principle of directionality (criteria ( 1 )) in the blood or small intestine tissue, proteins that pass 4 criteria are tier 1 targets, proteins that pass 3 criteria are tier 2 targets, proteins that pass 2 criteria are tier 3 targets, and the remaining proteins are tier 4 targets. ( STable 7 ) PPI analysis revealed the interaction between potential targets (Fig. 3 A). For the major targets in Tier One and Tier Two, there is an interaction between BTN2A1 and BTN3A3, and simultaneously, PTPRC, IL-18R1, and CatH are interconnected indirectly. Figure 3 B depicts the biological pathways associated with candidate target-coding genes. The majority of these genes are primarily focused on various immune responses and inflammatory responses. The study further evaluated whether the proposed protein targets had advantageous or harmful effects on other indications. Consequently, we conducted a PheWAS analysis on the primary and secondary targets (Fig. 3 C). The findings indicated that apart from CeD, BTN2A1 was also linked to primary sclerosing cholangitis, schizophrenia, and diabetes ( STable 8 ). Furthermore, CatH is linked to both type 1 diabetes (T1D) and coronary artery disease ( STable 9 ). The IL-18R1 exhibits a strong correlation with the occurrence of asthma ( STable 10 ). Regarding PTPRC, there is a correlation between the target and the occurrence of vitiligo and male pattern baldness ( STable 11 ). Subsequently, we conducted an analysis of the prospective pharmaceuticals for CeD. By utilizing the DGIbd database ( https://dgidb.org/ ), it was found that 13 out of the 22 candidate target coding genes were identified as targets of the 107 anticipated medicines ( STable 12 ). 4. Discussion CeD is a condition that is not life-threatening but significantly affects individuals' lives. It is crucial to discover new medications for the treatment of patients. The study employed a proteome-wide MR analysis of circulating plasma proteins to investigate their causal association with CeD. The candidate targets for CeD were further assessed by employing colocalization analysis, transcriptome-wide SMR analysis, MAGMA gene-based analysis, and bulk RNAseq-based differential expression analysis. Ultimately, the study identified 22 candidate targets with distinct dependability of the causal association. Furthermore, the development of a PPI network and the subsequent enrichment analysis provided insights into putative mechanisms for candidate targets with CeD. For Tier one, BTN2A1, a member of the butyrophilin (BTN) family, has been discovered as a new ligand for DC-SIGN1 ( 19 ). Altered expression of BTN and BTN-like proteins has been observed in individuals with known CeD. ( 20 ) In addition, studies have suggested that BTN2A1 may affect γδ TCR repertoires in CeD, indicating a potential role in disease progression ( 21 ). We proposed that BTN2A1 may elevate the susceptibility to CeD (β = 2.336, P = 1.54e-198) in the study. Furthermore, apart from the aforementioned crucial protein, the study has detected other proteins (CatH, IL-18R1, PTPRC) at the Tier two level that might play a role in the intricate network of the CeD. CatH is a lysosomal cysteine protease with a unique aminopeptidase activity that is extensively expressed in the lung, pancreas, thymus, kidney, liver, skin, and brain. ( 22 ) Several studies have highlighted the role of CatH in various autoimmune diseases such as CeD, multiple sclerosis (MS), and T1D ( 23 ). Additionally, CatH has been identified as one of the shared genetic variants in both T1D and CeD, suggesting a potential common pathogenesis between the two autoimmune conditions ( 24 ). IL-18R1, a cytokine receptor, specifically binds to IL18, thereby exerting biological effects. According to Sharma et al., the association of IL-18R1 with the risk of CeD varies with geographical differences ( 25 ). Additionally, the IL-18R1 locus contains genetic variants that may impact T cell function in CeD ( 26 ). The leucocyte common antigen, protein tyrosine phosphatase receptor type C (PTPRC), also known as CD45, is a transmembrane glycoprotein, expressed on almost all hematopoietic cells except for mature erythrocytes, and is an essential regulator of T and B cell antigen receptor-mediated activation. ( 27 ) Cording et al. provided fresh evidence supporting that PTPRC has been implicated in the oncogenetic landscape of lymphomagenesis in CeD.( 28 ) There are undoubtedly unavoidable limitations to this study. Initially, the data utilized in this study predominantly originated from individuals of European descent. Therefore, additional verification is required to enhance the applicability of these findings to other ancestral groups. Furthermore, plasma proteins are subject to the influence of other variables outside of genetics. Considering the objectives of this study, future research needs to focus on converting the results of the proteins analyzed into targeted treatment strategies. This should be done while considering the intricate connections between genes, the environment, and the pathways that link proteins to diseases. Ultimately, despite the widespread use of bioinformatics analysis and computational approaches, experimental validation remains a crucial component of scientific research. Through the execution of experiments, we can acquire vital knowledge regarding the functional consequences of observed patterns and enhance the reliability of our discoveries. Hence, future research must prioritize the implementation of focused experiments to validate the biological role of candidate targets in CeD. 5. Conclusion The study employed a combination of human genetics and proteomics data, employing several analytical approaches. As a result, 22 proteins were identified as prospective therapeutic targets for CeD. Furthermore, the study assessed the causal association, safety, and druggability of these proteins. The results of our research will contribute to the comprehension of the fundamental mechanisms of CeD and facilitate the progress of medication development. Declarations Acknowledgments All authors would like to express our sincere thanks for sharing the online databases. Data availability The original contributions presented in the study are included in the article. The code applied in the study is available on the https://github.com/sjz17/Sun04. Conflict of Interest The authors declare no competing interests. Author Contributions JS and YZ designed and conducted the entire study. JS performed the data collection, bioinformatics, and statistical data analysis. YZ was responsible for the integrity of the entire study and manuscript review. All authors contributed to the article and approved the submitted version. 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 assessed in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Funding Not applicable. Ethics approval and consent to participate Not applicable. Patient consent for publication Not applicable. References Catassi C, Verdu EF, Bai JC, Lionetti E. Coeliac disease. Lancet. 2022;399(10344):2413–26. Aljada B, Zohni A, El-Matary W. The Gluten-Free Diet for Celiac Disease and Beyond. Nutrients. 2021;13(11):3993. Lee AR, Ng DL, Zivin J, Green PHR. Economic burden of a gluten-free diet. J Hum Nutr Diet. 2007;20(5):423–30. 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Identification of Non-HLA Genes Associated with Celiac Disease and Country-Specific Differences in a Large, International Pediatric Cohort. PLoS ONE. 2016;11(3):e0152476. Bakker OB, Ramírez-Sánchez AD, Borek ZA, de Klein N, Li Y, Modderman R, et al. Potential impact of celiac disease genetic risk factors on T cell receptor signaling in gluten-specific CD4 + T cells. Sci Rep. 2021;11(1):9252. Al Barashdi MA, Ali A, McMullin MF, Mills K. Protein tyrosine phosphatase receptor type C (PTPRC or CD45). J Clin Pathol. 2021;74(9):548–52. Cording S, Lhermitte L, Malamut G, Berrabah S, Trinquand A, Guegan N, et al. Oncogenetic landscape of lymphomagenesis in coeliac disease. Gut. 2022;71(3):497–508. Additional Declarations No competing interests reported. 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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-5246482","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":370035226,"identity":"bddb5979-3767-4cf0-a746-e70cf34f2643","order_by":0,"name":"Jiazheng Sun","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiazheng","middleName":"","lastName":"Sun","suffix":""},{"id":370035228,"identity":"d5cb8249-40e1-4950-b382-d72fa3a49b1e","order_by":1,"name":"Yulan Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIie3RMUvEMBTA8RcKqcOrWVMC9Ss8rlARD+6r9DjIFES4xc2DQm7xPoLfQRd19Ajc1A8guAiFTjeLBx3soZuSeptD/kMggR8PXgBCoX8Yj6t1w6y8nlTR5vut9JNj3MxyZsclxVz3dxommTSFglqXJLD4G+FSawVX7uI0wvcGuw5EbAh2Tx6CjTsHcvOzKnnME0uQ3myJrWoPifX0tSds4ZIHlSwI6MVQxKyHgCH1RbBV2BFMBsmRGfVET+8ccoW8nyKHyH7JQOM8rXiR3tocZd1erlcecrLsv/KDZCaEa+W2yzKxnN2/7TzkR7g/ng8AoVAoFPqlT1VXSGJkzq9BAAAAAElFTkSuQmCC","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Yulan","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2024-10-11 13:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5246482/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5246482/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68323075,"identity":"e61b4f7a-c5a9-4a34-9b4b-e146300384b7","added_by":"auto","created_at":"2024-11-06 05:10:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":417580,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe study's flowchart diagram\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5246482/v1/77c9bff43b66c2cee3bac4a4.jpg"},{"id":68323071,"identity":"f108aa18-3425-448f-9def-d7989090a61c","added_by":"auto","created_at":"2024-11-06 05:10:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":749615,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of identified protein-coding genes associated with CeD.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeatmap of the effect of plasma and tissue-specific protein-coding gene expression on CeD risk for the identified proteins. The color represents the β estimators of SMR analysis, where green represents a decreased CeD risk and red represents an increased CeD risk for per-SD increased gene expression. “*” : P \u0026lt; 0.05; “**” : multiple tests, P \u0026lt; 0.0023 (0.05/22); “-”\u003c/p\u003e\n\u003cp\u003e: protein-coding gene failed in the SMR analysis and HEIDI test; blank: missing information.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5246482/v1/6e570501db33e443d421b17b.jpg"},{"id":68323074,"identity":"76efc047-8dc4-40fc-9f90-ede29886cdbb","added_by":"auto","created_at":"2024-11-06 05:10:11","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":426826,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe druggability analysis of candidate targets.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Results of protein-protein interaction network. (\u003cstrong\u003eB\u003c/strong\u003e) Functional enrichment analysis of coding genes for candidate targets. (\u003cstrong\u003eC\u003c/strong\u003e) Manhattan plot for PheWAS analysis results of BTN2A1, CSTH, IL18R1, and PTPRC.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5246482/v1/eee7b1074d338714bd24f724.jpg"},{"id":70588932,"identity":"4fa669d7-98f0-4936-b5c8-add3fee78718","added_by":"auto","created_at":"2024-12-04 16:23:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2146663,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5246482/v1/94abc159-3e55-4fb2-a44b-892fa23ba4a5.pdf"},{"id":68323070,"identity":"8c676b69-fe45-4b0d-83a4-012a8f54019a","added_by":"auto","created_at":"2024-11-06 05:10:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":266763,"visible":true,"origin":"","legend":"","description":"","filename":"SFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-5246482/v1/42162ac6f1623ef3a9ad66bb.docx"},{"id":68323845,"identity":"a9730d57-6634-4619-aa64-27ad01fd74e8","added_by":"auto","created_at":"2024-11-06 05:26:11","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1632857,"visible":true,"origin":"","legend":"","description":"","filename":"STable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5246482/v1/99bff91e7ee7e9f9d6001b99.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative multi-omic analyses identify candidate targets for celiac disease involving tissue-specific regulation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCeliac disease (CeD) is an autoimmune condition characterized by a reversible inflammatory reaction in the mucous membrane of the small intestine. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Genetically predisposed individuals experience the activation of this mechanism upon consuming gluten. The sole efficacious remedy for individuals with CeD is presently a diet devoid of gluten. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) However, the treatment has its constraints and is arduous to sustain over an extended period. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Therefore, it is crucial to identify and understand the biomarkers involved in the molecular process of CeD to identify potential targets for therapeutic intervention.\u003c/p\u003e \u003cp\u003eWith the advancement of bioinformatics and the development of comprehensive human gene research databases, medication target investigation centered around the integration of Mendelian randomization (MR) with proteomics has become notably significant in pharmaceutical development. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) MR analysis employs instrumental variables (IVs), genetic polymorphisms substantially associated with exposure factors, to assess causation. These genetic variations remain unaltered by environmental and behavioral influences, minimizing the potential for reverse causation and interference from other causes. MR analysis can not only reassess the effectiveness of approved medications, uncover novel therapeutic targets, and conduct comprehensive investigations into illness mechanisms but also offer fresh avenues and prospects for drug development and disease management.\u003c/p\u003e \u003cp\u003eWith integrative genomic analyses and multi-omics information, the study aimed to identify candidate targets for future CeD treatment. The specific process of the study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Initially, we acquired the IVs from the plasma proteome data through meticulous quality control. The proteome-wide MR analysis was utilized to identify candidate targets for CeD. In addition, colocalization analysis was applied to mitigate the impact of linkage disequilibrium (LD) effects and confirm if the candidate targets and CeD shared the same causal variant. The study further performed transcriptome-wide summary-data-based Mendelian randomization (SMR) to assess the correlation between alterations in the expression of coding genes for candidate targets and the likelihood of developing CeD. Multimarker analysis of genomic annotation (MAGMA) analysis was employed to confirm the causal association between coding genes for protein targets and CeD. Furthermore, this study utilized the bulk-RNAseq cohorts from the GEO database to validate the differential expression of putative treatment targets in both the cohort with CeD and the cohort of healthy controls. Phenome-wide association studies (PheWAS) were employed to explore any potential adverse effects of prioritizing these candidate targets for CeD, while the DGIdb database provided information on their approved or investigated drugs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data acquisition and processing\u003c/h2\u003e \u003cp\u003eThe pQTL data associated with plasma proteins was obtained from three distinct GWAS cohorts. The UKB-PPP cohort (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) contains plasma proteome profiles of 54,219 UKB individuals, with information on 2,923 proteins in total. Proteomic analysis was carried out by the Olink platform. The Fenland cohort (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) collected aggregate information on 4,979 plasma proteins from 10,708 individuals, which were analyzed using SomaScan version 4. The Iceland cohort (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) includes a proteomic genome-wide association study (GWAS) of 35,559 Icelanders with information on 4907 plasma proteins.\u003c/p\u003e \u003cp\u003eThe eQTL data, obtained from the eQTLGen consortium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eqtlgen.org/\u003c/span\u003e\u003cspan address=\"https://www.eqtlgen.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), was collected for individuals of both genders. The consortium provides information on the expression of 16,987 genes in whole blood. The information is obtained from 37 datasets, which collectively include 31,684 individuals of both genders. In addition, the study obtained the tissue-specific cis-eQTLs from 49 tissues (n\u0026thinsp;=\u0026thinsp;15,201) from the GTEx (v8) project (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) to investigate the tissue-specific relationships and potential unintended consequences of medications that target genes. The eQTL data are represented as the effect of each additional allele on a 1-SD change in the gene expression level (mRNA).\u003c/p\u003e \u003cp\u003eThe information for the CeD was obtained from a previously published GWAS, in which 97,422 SNPs were analyzed in a discovery cohort including 11,812 CeD patients and 229 healthy controls. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eBulk-seq datasets of CeD were obtained from the Gene Expression Omnibus (GEO) database (\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). GSE134900 cohort (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) was composed of duodenal tissue from 51 CeD patients and 44 healthy controls. GSE131705 cohort (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) was composed of duodenal tissue from 33 CeD patients and 21 healthy controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Proteome-wide MR analysis\u003c/h2\u003e \u003cp\u003eFor MR analysis, the \"TwoSampleMR\" R package was utilized. The pQTLs were selected as the IVs. The CeD GWAS dataset was selected as the outcome data.\u003c/p\u003e \u003cp\u003eThe pQTLs were selected based on the following criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) their significant association with proteins at the genome-wide level (P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) The pQTLs were located outside of the Major Histocompatibility Complex (MHC) region (chr6: 25.5\u0026ndash;34.0 Mb); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) The pQTLs were independently associated (LD clumping r2\u0026thinsp;\u0026lt;\u0026thinsp;0.001); (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) The pQTLs were located within 1 Mb of the transcription start site of the protein-coding genes (cis- pQTLs); (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) The pQTLs belonged to strong genetic variants (F statistics\u0026thinsp;\u0026gt;\u0026thinsp;10); (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) Any pQTL with missing information was removed from the analysis.\u003c/p\u003e \u003cp\u003eThe main analytical technique utilized in this study is the Wald ratio method, specifically designed for cases involving proteins with only one cis-pQTL. The inverse variance weighting (IVW) method is the principal analysis method utilized for proteins with multiple cis-pQTLs. The measured outcome odds ratio represents the risk of CeD for each additional unit of plasma protein. In order to minimize the occurrence of incorrect positive results, the False Discovery Rate (FDR) correction was implemented. Statistically significant results were considered as FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eWe employed sensitivity analysis to assess and improve the robustness of the MR analysis. The heterogeneity was evaluated using Cochran's Q test (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The significance level of Cochran's Q test was set at P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, which indicated that there is no heterogeneity in the IVs employed in the inquiry. The evaluation of horizontal pleiotropy was performed by the MR-Egger intercept test (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The significance level of the MR-Egger intercept test was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Colocalization analysis\u003c/h2\u003e \u003cp\u003eBayesian colocalization analysis (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) was employed to ascertain whether the same causal variant influenced both plasma proteins and CeD. Colocalization analysis assumed that there was a single underlying causal variation for each of the two features within a given region. This resulted in establishing five distinct and independent model assumptions (H0-H4). These five model assumptions encompassed all possible correlation possibilities in colocalization analysis. For each model, a posterior probability (PP.H0-PP.H4) was computed. Based on the available evidence, a model hypothesis with a higher posterior probability was more likely to be accurate. A hypothesis based on the H4 model posited that a common source of variation influenced both traits. The H4 model assumption was valid when PP.H4\u0026thinsp;\u0026gt;\u0026thinsp;0.8.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Transcriptome-wide SMR analysis\u003c/h2\u003e \u003cp\u003eThe study further conducted SMR analysis (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) to assess the correlation between alterations in the expression of coding genes for candidate targets and the likelihood of developing CeD. The SMR analysis utilizes a single SNP (located close to the target gene) that is most strongly associated, serving as the IV. The heterogeneity in the dependent instrument (HEIDI) test was utilized to differentiate linkage in the causal association in the case of more than three SNPs. The screening criteria with SMR P-value threshold\u0026thinsp;\u0026lt;\u0026thinsp;0.05/ (number of candidate targets) and HEIDI test P-value threshold\u0026thinsp;\u0026gt;\u0026thinsp;0.05 were employed to assess candidate target coding genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 MAGMA gene-based analysis\u003c/h2\u003e \u003cp\u003eThe MAGMA analysis (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) was utilized to further investigate the correlation between coding genes for protein targets and CeD. MAGMA analysis employs multiple regression techniques to convert SNP-level correlations derived from GWAS into gene-level associations, taking into account LD across variants and identifying the effects of multiple variants. Furthermore, MAGMA analysis assigns a P-value to each gene to evaluate its correlation with the disease. Statistically significant results were considered as FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, which indicates a substantial relationship between the gene and CeD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Protein-protein interaction network\u003c/h2\u003e \u003cp\u003eIn order to examine the connections between candidate targets, the study conducted a protein-protein interaction (PPI) network analysis for candidate targets with CeD, which was conducted using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) version 11.5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Functional enrichment analysis\u003c/h2\u003e \u003cp\u003eKEGG enrichment analysis, a widely used bioinformatics method, is employed to examine gene pathways and functional enrichment in a certain geneset, which was performed through the \u0026ldquo;clusterProfiler\u0026rdquo; R package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 PheWAS analysis\u003c/h2\u003e \u003cp\u003eIn order to further evaluate the horizontal pleiotropy of candidate targets and possible side effects, a PheWAS was performed on the GWAS ATLAS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://atlas.ctglab.nl/\u003c/span\u003e\u003cspan address=\"https://atlas.ctglab.nl/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) database containing 4,756 GWAS from 473 unique studies across 3,302 unique traits and 28 domains. The significance level of PheWAS analysis was set at P\u0026thinsp;\u0026lt;\u0026thinsp;1.05e-5 (0.05/4,756).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical differences between groups were determined by Student's t-test for normally distributed variables, and for normally distributed variables, statistical differences between groups were determined by the Wilcoxon test. The R software (version 4.3.3) and the smr-1.3.1-win software were used in the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The candidate targets for CeD\u003c/h2\u003e \u003cp\u003eThe proteome-wide MR analysis for three cohorts displayed candidate targets related to CeD. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) A total of 22 proteins were found to have a significant association with CeD (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including 13 proteins from the UKB-PPP cohort, 6 proteins from the Iceland cohort, and 8 proteins from the Fenland cohort. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB \u003cb\u003eand STable 1\u003c/b\u003e) Among these, proteins BTN2A1, GNPDA1, IL-18R1, IL-1RL1, Park7, PTPRC, SH2B3, Trehalase, ULK3, OLFM2, ICOSLG, Colipase, B3galt6, COL11A2, and ALDH2 exhibited a positive correlation with CeD. Conversely, proteins CCL21, CD226, RABEP1, TNFRSF9, TNFSF11, BTN3A3, and CatH showed a negative correlation with CeD. Out of these proteins, B3galt6, BTN3A3, CCL21, IL-18R1, and IL-1RL1, their replication was confirmed in two distinct datasets.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNone of the candidate targets exhibited substantial heterogeneity and pleiotropy, as indicated in \u003cb\u003eSTable 2\u003c/b\u003e and \u003cb\u003eSTable 3\u003c/b\u003e. The F statistic of the IVs for each candidate target demonstrated a robust measure of tool efficacy (\u003cb\u003eSTable 4\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Conclusions of the colocalization analysis\u003c/h2\u003e \u003cp\u003eThe Bayesian colocalization analysis (\u003cb\u003eSTable 5\u003c/b\u003e) revealed a significant colocalization between ALDH2C, CL21, PTPRC, B3galt6, RABEP1, ICOSLG, IL-18R1, BTN2A1, IL-1RL1, and CeD, demonstrating that these proteins and CeD shared causal genetic drivers (PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.8). In addition, the findings suggested that Park7, TREH, OLFM2, BTN3A3, COL11A2, BTN3A3, CCL21, CatH, GNPDA1, IL-1RL1, and CeD may be influenced by two causal variants in the genome (PPH3\u0026thinsp;\u0026gt;\u0026thinsp;0.8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Associations of the protein-coding gene expression on CeD\u003c/h2\u003e \u003cp\u003eThe study mapped 22 proteins to 22 coding genes. The SMR analysis revealed that 9 genes exhibited consistent results for CeD with their respective proteins. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) The genes CTSH, CD226, TNFSF11, and ICOSLG were found to be related to a decreased risk of CeD in several blood samples or tissue-specific samples. Conversely, B3GALT6, ULK3, GNPDA1, BTN2A1, and PARK7 showed a notable correlation with an elevated susceptibility to CeD. The genes IL18R1, RABEP1, COL11A2, SH2B3, IL1RL1, and ALDH2 exhibited varying impacts on the risk of CeD in different blood or tissue-specific samples, which could indicate the presence of potential off-target effects.\u003c/p\u003e \u003cp\u003eFor instance, the upregulation of the COL11A2 gene in plasma is associated with an elevated risk of CeD, which aligns with the function of the COL11A2 protein in plasma. Nevertheless, the likelihood of developing CeD, as indicated by the presence of the COL11A2 gene, was diminished in brain tissue. The impact of ALDH2 gene expression on CeD varied in plasma and different tissues. Consequently, medications that specifically target certain genes in various organs may have unintended effects on other genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Conclusions of the MAGMA analysis\u003c/h2\u003e \u003cp\u003eFollowing the removal of missing information, a total of 18 protein-coding genes were enrolled in the MAGMA analysis. Out of the genes analyzed, 14 protein-coding genes showed a significant correlation with CeD (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with COL11A2 being the gene that showed the strongest correlation with CeD (\u003cb\u003eSTable 6\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Conclusions of the differential expression analysis\u003c/h2\u003e \u003cp\u003eThe differential expression analysis revealed substantial heterogeneity in the expression of nine genes in both RNAseq cohorts including CeD samples and healthy samples (\u003cb\u003eSFigure 1\u003c/b\u003e). It is worth noting that there is heterogeneity in the expression of the four genes and conclusions of Proteome-wide MR analysis, which may be caused by the different organizational sources of proteome data and RNAseq data. Ultimately, five genes (BTN2A1, PTPRC, SH2B3, IL18R1, and CTSH) passed bulk RNAseq-based differential expression analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.6 The druggability analysis of candidate targets\u003c/h2\u003e \u003cp\u003eAfter performing proteome-wide MR analysis, colocalization analysis, transcriptome-wide SMR analysis, MAGMA gene-based analysis, and bulk RNAseq-based differential expression analysis, the study divided the 22 identified proteins into four tiers based on the following criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) The direction of effects in primary MR analysis and SMR analysis are consistent (both ORs are greater than 1 or less than 1). (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) pQTLs of the protein demonstrate colocalization with CeD; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) The protein passes transcriptome-wide SMR analysis (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, without off-target effect) and HEIDI test (p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05); (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) The protein-coding gene is differentially expressed both RNAseq cohorts including CeD samples and healthy samples. Under the prerequisite of meeting the principle of directionality (criteria (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)) in the blood or small intestine tissue, proteins that pass 4 criteria are tier 1 targets, proteins that pass 3 criteria are tier 2 targets, proteins that pass 2 criteria are tier 3 targets, and the remaining proteins are tier 4 targets. (\u003cb\u003eSTable 7\u003c/b\u003e)\u003c/p\u003e \u003cp\u003ePPI analysis revealed the interaction between potential targets (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). For the major targets in Tier One and Tier Two, there is an interaction between BTN2A1 and BTN3A3, and simultaneously, PTPRC, IL-18R1, and CatH are interconnected indirectly. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB depicts the biological pathways associated with candidate target-coding genes. The majority of these genes are primarily focused on various immune responses and inflammatory responses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe study further evaluated whether the proposed protein targets had advantageous or harmful effects on other indications. Consequently, we conducted a PheWAS analysis on the primary and secondary targets (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The findings indicated that apart from CeD, BTN2A1 was also linked to primary sclerosing cholangitis, schizophrenia, and diabetes (\u003cb\u003eSTable 8\u003c/b\u003e). Furthermore, CatH is linked to both type 1 diabetes (T1D) and coronary artery disease (\u003cb\u003eSTable 9\u003c/b\u003e). The IL-18R1 exhibits a strong correlation with the occurrence of asthma (\u003cb\u003eSTable 10\u003c/b\u003e). Regarding PTPRC, there is a correlation between the target and the occurrence of vitiligo and male pattern baldness (\u003cb\u003eSTable 11\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eSubsequently, we conducted an analysis of the prospective pharmaceuticals for CeD. By utilizing the DGIbd database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dgidb.org/\u003c/span\u003e\u003cspan address=\"https://dgidb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), it was found that 13 out of the 22 candidate target coding genes were identified as targets of the 107 anticipated medicines (\u003cb\u003eSTable 12\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eCeD is a condition that is not life-threatening but significantly affects individuals' lives. It is crucial to discover new medications for the treatment of patients. The study employed a proteome-wide MR analysis of circulating plasma proteins to investigate their causal association with CeD. The candidate targets for CeD were further assessed by employing colocalization analysis, transcriptome-wide SMR analysis, MAGMA gene-based analysis, and bulk RNAseq-based differential expression analysis. Ultimately, the study identified 22 candidate targets with distinct dependability of the causal association. Furthermore, the development of a PPI network and the subsequent enrichment analysis provided insights into putative mechanisms for candidate targets with CeD.\u003c/p\u003e \u003cp\u003eFor Tier one, BTN2A1, a member of the butyrophilin (BTN) family, has been discovered as a new ligand for DC-SIGN1 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Altered expression of BTN and BTN-like proteins has been observed in individuals with known CeD. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) In addition, studies have suggested that BTN2A1 may affect γδ TCR repertoires in CeD, indicating a potential role in disease progression (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). We proposed that BTN2A1 may elevate the susceptibility to CeD (β\u0026thinsp;=\u0026thinsp;2.336, P\u0026thinsp;=\u0026thinsp;1.54e-198) in the study.\u003c/p\u003e \u003cp\u003eFurthermore, apart from the aforementioned crucial protein, the study has detected other proteins (CatH, IL-18R1, PTPRC) at the Tier two level that might play a role in the intricate network of the CeD.\u003c/p\u003e \u003cp\u003eCatH is a lysosomal cysteine protease with a unique aminopeptidase activity that is extensively expressed in the lung, pancreas, thymus, kidney, liver, skin, and brain. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) Several studies have highlighted the role of CatH in various autoimmune diseases such as CeD, multiple sclerosis (MS), and T1D (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Additionally, CatH has been identified as one of the shared genetic variants in both T1D and CeD, suggesting a potential common pathogenesis between the two autoimmune conditions (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). IL-18R1, a cytokine receptor, specifically binds to IL18, thereby exerting biological effects. According to Sharma et al., the association of IL-18R1 with the risk of CeD varies with geographical differences (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Additionally, the IL-18R1 locus contains genetic variants that may impact T cell function in CeD (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The leucocyte common antigen, protein tyrosine phosphatase receptor type C (PTPRC), also known as CD45, is a transmembrane glycoprotein, expressed on almost all hematopoietic cells except for mature erythrocytes, and is an essential regulator of T and B cell antigen receptor-mediated activation. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) Cording et al. provided fresh evidence supporting that PTPRC has been implicated in the oncogenetic landscape of lymphomagenesis in CeD.(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThere are undoubtedly unavoidable limitations to this study. Initially, the data utilized in this study predominantly originated from individuals of European descent. Therefore, additional verification is required to enhance the applicability of these findings to other ancestral groups. Furthermore, plasma proteins are subject to the influence of other variables outside of genetics. Considering the objectives of this study, future research needs to focus on converting the results of the proteins analyzed into targeted treatment strategies. This should be done while considering the intricate connections between genes, the environment, and the pathways that link proteins to diseases. Ultimately, despite the widespread use of bioinformatics analysis and computational approaches, experimental validation remains a crucial component of scientific research. Through the execution of experiments, we can acquire vital knowledge regarding the functional consequences of observed patterns and enhance the reliability of our discoveries. Hence, future research must prioritize the implementation of focused experiments to validate the biological role of candidate targets in CeD.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe study employed a combination of human genetics and proteomics data, employing several analytical approaches. As a result, 22 proteins were identified as prospective therapeutic targets for CeD. Furthermore, the study assessed the causal association, safety, and druggability of these proteins. The results of our research will contribute to the comprehension of the fundamental mechanisms of CeD and facilitate the progress of medication development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors would like to express our sincere thanks for sharing the online databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article. The code applied in the study is available on the https://github.com/sjz17/Sun04.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJS and YZ designed and conducted the entire study. JS performed the data collection, bioinformatics, and statistical data analysis. YZ was responsible for the integrity of the entire study and manuscript review. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003es note\u0026nbsp;\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 assessed in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCatassi C, Verdu EF, Bai JC, Lionetti E. Coeliac disease. Lancet. 2022;399(10344):2413\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAljada B, Zohni A, El-Matary W. The Gluten-Free Diet for Celiac Disease and Beyond. 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Nature. 2023;622(7982):329\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePietzner M, Wheeler E, Carrasco-Zanini J, Cortes A, Koprulu M, W\u0026ouml;rheide MA, et al. Mapping the proteo-genomic convergence of human diseases. Science. 2021;374(6569):eabj1541.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerkingstad E, Sulem P, Atlason BA, Sveinbjornsson G, Magnusson MI, Styrmisdottir EL, et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet. 2021;53(12):1712\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45(6):580\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrynka G, Hunt KA, Bockett NA, Romanos J, Mistry V, Szperl A, et al. Dense genotyping identifies and localizes multiple common and rare variant association signals in celiac disease. Nat Genet. 2011;43(12):1193\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbadie V, Kim SM, Lejeune T, Palanski BA, Ernest JD, Tastet O, et al. IL-15, gluten and HLA-DQ8 drive tissue destruction in coeliac disease. Nature. 2020;578(7796):600\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoberman-Nachum N, Sosnovski K, Di Segni A, Efroni G, Braun T, BenShoshan M, et al. Defining the Celiac Disease Transcriptome using Clinical Pathology Specimens Reveals Biologic Pathways and Supports Diagnosis. Sci Rep. 2019;9(1):16163.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden J, Del Greco MF, Minelli C, Zhao Q, Lawlor DA, Sheehan NA, et al. Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption. Int J Epidemiol. 2019;48(3):728\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32(5):377\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10(5):e1004383.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Y, Zeng J, Zhang F, Zhu Z, Qi T, Zheng Z, et al. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat Commun. 2018;9(1):918.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015;11(4):e1004219.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatanabe K, Stringer S, Frei O, Umićević Mirkov M, de Leeuw C, Polderman TJC, et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet. 2019;51(9):1339\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalcherek G, Mayr L, Roda-Navarro P, Rhodes D, Miller N, Trowsdale J. The B7 homolog butyrophilin BTN2A1 is a novel ligand for DC-SIGN. J Immunol. 2007;179(6):3804\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLebrero-Fern\u0026aacute;ndez C, Wenzel UA, Akeus P, Wang Y, Strid H, Simr\u0026eacute;n M, et al. Altered expression of Butyrophilin (BTN) and BTN-like (BTNL) genes in intestinal inflammation and colon cancer. Immun Inflamm Dis. 2016;4(2):191\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFichtner AS, Ravens S, Prinz I. Human γδ TCR Repertoires in Health and Disease. Cells. 2020;9(4):800.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Zhao J, Gu Y, Wang H, Jiang M, Zhao S, et al. Cathepsin H: Molecular characteristics and clues to function and mechanism. Biochem Pharmacol. 2023;212:115585.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuinn EM, Coleman C, Molloy B, Dominguez Castro P, Cormican P, Trimble V, et al. Transcriptome Analysis of CD4\u0026thinsp;+\u0026thinsp;T Cells in Coeliac Disease Reveals Imprint of BACH2 and IFNγ Regulation. PLoS ONE. 2015;10(10):e0140049.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmyth DJ, Plagnol V, Walker NM, Cooper JD, Downes K, Yang JHM, et al. Shared and distinct genetic variants in type 1 diabetes and celiac disease. N Engl J Med. 2008;359(26):2767\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma A, Liu X, Hadley D, Hagopian W, Liu E, Chen WM, et al. Identification of Non-HLA Genes Associated with Celiac Disease and Country-Specific Differences in a Large, International Pediatric Cohort. PLoS ONE. 2016;11(3):e0152476.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBakker OB, Ram\u0026iacute;rez-S\u0026aacute;nchez AD, Borek ZA, de Klein N, Li Y, Modderman R, et al. Potential impact of celiac disease genetic risk factors on T cell receptor signaling in gluten-specific CD4\u0026thinsp;+\u0026thinsp;T cells. Sci Rep. 2021;11(1):9252.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl Barashdi MA, Ali A, McMullin MF, Mills K. Protein tyrosine phosphatase receptor type C (PTPRC or CD45). J Clin Pathol. 2021;74(9):548\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCording S, Lhermitte L, Malamut G, Berrabah S, Trinquand A, Guegan N, et al. Oncogenetic landscape of lymphomagenesis in coeliac disease. Gut. 2022;71(3):497\u0026ndash;508.\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":"Celiac disease, Mendelian randomization, Proteome, Transcriptome, Therapeutic targets","lastPublishedDoi":"10.21203/rs.3.rs-5246482/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5246482/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eCeliac disease (CeD) is an autoimmune condition characterized by a reversible inflammatory reaction in the mucous membrane of the small intestine. Nevertheless, there is a limited availability of efficient control approaches. Prior research has demonstrated that pharmacological targets supported by genetic evidence can greatly enhance the efficacy of drug development. Hence, the study aims to integrate transcriptomic and proteomic information to identify candidate targets for CeD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study employed proteome-wide Mendelian randomization (MR) analysis of circulating plasma proteins to investigate their causal association with CeD. The candidate targets for CeD were further assessed employing colocalization analysis, transcriptome-wide summary-data-based Mendelian randomization (SMR) analysis, multimarker analysis of genomic annotation (MAGMA) gene-based analysis, and bulk RNAseq-based differential expression analysis. For the proteins that were identified, extended Phenome-wide association studies (PheWAS) were conducted to assess their side-effect profiles, while the DGIdb database provided information on the approved or investigated drugs for candidate targets.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSystematic MR analysis identified 22 candidate targets for CeD. Among the proteins analyzed, BTN2A1 passed all subsequent verification analyses. Additionally, three proteins, including CatH, IL-18R1, and PTPRC, passed the majority of the subsequent verification analyses. The other 18 proteins were also candidate targets (Trehalase, CD226, SH2B3, ICOSLG, ULK3, Park7, ALDH2, RABEP1, TNFRSF9, COL11A2, GNPDA1, IL-1RL1, B3galt6, TNFSF11, CCL21, BTN3A3, OLFM2 and Colipase).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe study employed a combination of human transcriptomic and proteomic information, employing several analytical methods. As a result, 22 proteins, divided into four tiers, were identified as prospective therapeutic targets for CeD.\u003c/p\u003e","manuscriptTitle":"Integrative multi-omic analyses identify candidate targets for celiac disease involving tissue-specific regulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-06 05:10:05","doi":"10.21203/rs.3.rs-5246482/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":"2533bf14-7cff-42ce-abf2-98489323c2ba","owner":[],"postedDate":"November 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-04T16:23:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-06 05:10:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5246482","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5246482","identity":"rs-5246482","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}