SPAG11B, a potential biomarker for rheumatoid arthritis: A two-sample bidirectional mendelian randomization analysis

SPAG11B, a potential biomarker for rheumatoid arthritis: A two-sample bidirectional mendelian randomization 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 SPAG11B, a potential biomarker for rheumatoid arthritis: A two-sample bidirectional mendelian randomization analysis Kun Lin, Qi Lin, Weifeng Lv, Yao Li, Rong Su This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6248365/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Jun, 2025 Read the published version in BMC Rheumatology → Version 1 posted 16 You are reading this latest preprint version Abstract Background: The incidence of rheumatoid arthritis (RA) is rising. However, its pathogenesis has not been fully understood, and the current therapeutic regimens are still limited. The aim of this study was to investigate the causal effect of plasma proteins on RA using Mendelian randomization (MR) analysis. Methods: We performed MR analysis with 4907 plasma protein genetic associations used for exposure and RA genome-wide association data used as outcomes. The method was dominated by Inverse Variance Weighting, in addition to MR-Egger and Weighted Median. Meanwhile, further external validation and reverse MR analysis were conducted to systematically assess the causal relationship between plasma proteins and RA. Result: Preliminary MR analysis identified two proteins (SPAG11B and DEFB135) associated with RA, and elevated plasma levels of both proteins would reduce the risk of RA (for SPAG11B, OR =0.49, 95% CI =0.40-0.61, p =1.19×10 -10 ; for DEFB135, OR =0.28, 95% CI =0.15-0.52, p =4.51×10 -5 , using the IVW method). In the external validation phase, the results were reproducible for SPAG11B, but not for DEFB135. Reverse MR analysis pointed out that RA exhibited reverse causality for plasma levels of SPAG11B (OR=0.93, 95% CI=0.89-0.98, p =0.004), but not for DEFB135 ( p =0.93). Conclusion: The results of MR analysis in this study supported that SPAG11B as a novel biomarker for RA was worthy of further investigation. Rheumatoid arthritis plasma protein SPAG11B DEFB135 Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Rheumatoid arthritis (RA) is a chronic autoimmune disease that can cause joint inflammation and structural damage such as joint swelling, deformity, and muscle atrophy, resulting in functional impairment 1 , 2 . However, the exact pathogenesis of RA is not yet fully understood and there is a lack of targeted therapeutic options, so the prevalence and incidence of RA are increasing year by year 3 , 4 . Exploring the etiology and potential therapeutic targets of RA is of great significance for developing therapeutic strategies to slow down its progression. Plasma proteins are major regulators mediating numerous signaling pathways and biological processes and are considered biomarkers or therapeutic targets for disease 5 . Recently, proteomics-based studies have shown new promise in predicting disease events and potential therapeutic targets 6 – 9 . Unfortunately, few studies have been conducted to study RA-related plasma proteins based on large-scale population protein quantitative trait loci (pQTL) 10 , 11 . Genetic variants are randomly classified at the time of conception, and the Mendelian randomization (MR) analysis is to minimize the effect of confounders by using genetic variants as instrumental variables (IVs) for exposure 12 – 14 . This solves the problem that traditional experimental methods are unable to efficiently account for the causality between exposure factors and outcome variables due to the presence of confounders 12 – 14 . MR can correlate plasma protein information obtained through high-throughput sequencing with genome-wide outcomes related to disease phenotypes 12 . Thus, the utilization of human blood proteomics data in MR studies will contribute to a deeper understanding of plasma proteins causally associated with RA, thereby identifying proteins associated with RA causality, improving our understanding of the genetic architecture of RA, and providing new evidence for the identification of drug targets. Methods Study design and data sources The analysis flowchart of this study was shown in Fig. 1 . Plasma proteomics data were obtained from the deCODE database ( https://www.decode.com/summarydata/ ), and RA data were collected from FinnGen ( https://r9.risteys.finngen.fi/ ), Integrative Epidemiology Unit OpenGWAS (IEU, https://gwas.mrcieu.ac.uk/ ) and UK biobank (UKB, https://www.ukbiobank.ac.uk ) databases. Genetic instruments selection In this study, pQTL data from Ferkingstad et al. were used as exposure 15 . This dataset was based on 35559 Icelanders who were tested for 4907 plasma proteins using the SomaScan version 4 assay (SomaLogic) and found 18084 correlations between sequence variations and plasma protein levels 15 . Detailed information on genotyping, interpolation, and quality control can be found in the original paper 15 . For genetic instrumental variables (IV), we selected single nucleotide polymorphisms (SNPs) with genome-wide significance ( p < 5×10 − 8 ). To minimize the associated horizontal pleiotropy, we needed to eliminate the effect of linkage disequilibrium (LD, LD windows 10000kb, clumping r 2 < 0.001). To quantify the potential of pQTL to predict RA, F -statistics was used as an assessment tool, and IVs with F -statistics ≥ 10 were considered to have sufficient predictive power to be included in the next analysis. Data sources for RA In the preliminary MR analysis phase, data for the RA cohort were obtained from the FinnGen database (M13_RHEUMA), which was harvested from 392423 participants (12555 cases and 379868 controls) and analyzed for 16380169 SNPs. The externally validated RA data were obtained from the IEU (ebi-a-GCST90013534) and UKB (ukb-d-M13_RHEUMA) databases. Among them, the IEU cohort included 14361 cases and 43923 controls, while UKB brought 1605 cases and 359589 controls into the cohort. MR analysis All MR analysis in this study were performed using the TwoSampleMR package in R ( https://github.com/MRCIEU/TwoSampleMR ). Analysis methods included Inverse Variance Weighting (IVW), MR-Egger, and Weighted Median, with IVW selected as the primary method to determine the causal effect of plasma proteins on RA. In addition, the Benjamini-Hochberg method was used to perform multiple test correction for all p -values, and multiple comparisons were corrected using the Benjamini-Hochberg false discovery rate (FDR), with the threshold set at 0.2. In sensitivity analysis, we used a marker of heterogeneity for the IVW method (Cochran Q-derived p < 0.05) to assess potential heterogeneity among IVs. The intercept obtained in MR-Egger regression is an indicator of directional pleiotropy ( p < 0.05 is considered to be present for directed pleiotropy) 12 . Therefore, MR-Egger regression was used to ensure the validity of MR analysis results. Leave-one-out analysis was used to evaluate whether a single SNP would drive or bias MR results. Reverse MR analysis Based on the same criteria for MR analysis as described above, we performed a reverse Mendelian randomization analysis using the FinnGen RA cohort data as the exposure and deCODE plasma proteins identified as being associated with RA in the preliminary MR analysis as an outcome to explore potential reverse causality. The analysis was carried out by IVW (primary method), MR-Egger and Weighted Median. Results Causal effects of plasma proteins on RA Preliminary MR analysis assessed 4907 plasma proteins for potential association with RA. After adjusting for FDR, two proteins significantly associated with RA were identified (Fig. 2 ), namely human sperm associated antigen 11 B (SPAG11B) and defensin beta 135 (DEFB135). In detail, elevated plasma levels of SPAG11B (OR = 0.49, 95% CI = 0.40–0.61, p = 1.19×10 − 10 , using the IVW method) and DEFB135 (OR = 0.28, 95% CI = 0.15–0.52, p = 4.51×10 − 5 , using the IVW method ) attenuated the risk of RA. The results of SPAG11B were replicated in MR-Egger and Weighted Median analysis methods (Fig. 3 a and b, genetic IVs shown in Figure S1 a). The results of DEFB135 in Weighted Median were consistent with those of IVW, but p > 0.05 in MR-Egger analysis (Fig. 3 c and d, genetic IVs shown in Figure S1 b). Analysis combined with intercept and p -value in MR-Egger regression analysis showed that any potential pleiotropy of a single SNP was balanced, which meant that the probability of MR results being biased was relatively low (for SPAG11B, intercept = 0.033, p = 0.40; for DEFB135, intercept = 0.091, p = 0.32). Furthermore, no heterogeneity was observed for SPAG11B ( p = 0.52). Although the Cochran's Q Test for DEFB135 showed the presence of heterogeneity ( p = 3.81×10 − 4 ) that may be caused by different analytical platforms, experiments and populations, this did not affect the reliability of IVW results, which were acceptable 16 . External Validation In the external validation phase, we selected two additional RA datasets and used the same analysis strategy to re-examine the two plasma proteins identified in the preliminary MR analysis as being associated with RA. Of these, the findings for SPAG11B in IEU (OR = 0.26, 95% CI = 0.14–0.51, p = 6.06×10 − 5 , using the IVW method) and UKB (OR = 0.99, 95% CI = 0.990–0.994, p = 3.17×10 − 10 , using the IVW method) were consistent with those of the preliminary MR analysis, with higher plasma level associated with a lower risk of RA (Fig. 4 a and 4 b). No bias was seen in the multiplicity test for SPAG11B in both IEU and UKB (Pleiotropy and heterogeneity analysis shown in supplementary Table 1a and b). However, the external validation results of DEFB135 showed no significant correlation with the occurrence of RA (for IEU, p = 0.10; for UKB, p = 0.06; detailed results shown in supplementary Table 2), which was inconsistent with the preliminary MR results. Reverse MR analysis To examine whether there is a reverse causality between RA and candidate proteins, we next performed reverse MR analysis of the two plasma proteins identified in the preliminary analysis. The MR results of IVW showed that the occurrence of RA caused a reduction in plasma levels of SPAG11B (OR = 0.93, 95% CI = 0.89–0.98, p = 0.004, Figure S2 a and b). For DEFB135, the reverse MR results showed that there was no reverse causal effect of RA on DEFB135, meaning that the occurrence of RA did not lead to changes in plasma levels of DEFB135 ( p = 0.93, supplementary Table 3). Discussion The incidence of RA is on the rise 3 , 4 . Exploring the exact etiology and potential pathogenic mechanisms of RA to provide more effective treatment options for patients and to improve the prognosis is a current scientific issue that needs to be addressed. In this study, we used GWAS summary statistics from FinnGen, IEU and UKB and further performed proteomics MR analysis based on a large pQTL population to systematically evaluated the causal relationship between 4907 plasma proteins and RA. Based on preliminary MR analysis of the FinnGen RA cohort, we found that SPAG11B was associated with the development of RA. Further external validation results based on the IEU and UKB RA cohorts were consistent. The higher the plasma level of SPAG11B, the lower the risk of RA. Reverse MR analysis revealed that SPAG11B plasma levels acquired changes when RA occurred. Specifically, the occurrence of RA caused a decrease in plasma levels of SPAG11B, suggesting its ability as a potential biomarker for predicting the occurrence of RA. The specific function of SPAG11B has not been clearly elucidated, but it is currently generally believed to be involved in sperm maturation 17 – 19 . A study by Alexandre et al. demonstrated that SPAG11B had trypsin-like inhibitory activity and showed significant inhibition of trypsin-like activity, joint edema formation, and release of IL-6 and CXCL1/KC inflammatory factors through lentivirus-mediated heterologous expression of endogenous hSPAG11B/C in an animal model of arthritis 20 . The MR results in this study supported this experimental conclusion, and the OR value also suggested that elevated plasma levels of SPAG11B can reduce the risk of RA. For DEFB135, the preliminary MR analysis found an association between it and the risk of RA. Elevated plasma levels of DEFB135 could reduce the odds of developing RA, and it may play a protective role in the development of RA. However, the results were not reproduced during the external validation phase. The MR results of the IEU and UKB RA cohorts did not show significant correlation with the development of RA. Notably, reverse MR results suggested that there was no reverse causality between DEFB135 and RA. In other words, the occurrence of RA did not cause changes in plasma levels of DEFB135. This finding suggested that DEFB135 may be an important molecular node affecting the pathogenesis of RA, which may bring new potential therapeutic targets and drug development ideas for RA. DEFB135 is a secreted antibacterial protein that is a member of the Beta defensin protein family 21 . The Beta defensin protein family is an important part of the mammalian innate defense system and plays an integral role in the specificity and reactivity of immune cells against inflammatory stimuli and pathogens 22 , 23 . It was noted that the inhibitory effect of rNOD1 on Escherichia coli was through the activation of NF-κB signaling to induce DEFB135 expression 21 . In addition to its antimicrobial activity, Beta defensin protein induced the expression of growth factors and stimulated the proliferation of endothelial cells, thus participating in the formation of new blood vessels 24 , 25 . By interacting with the chemokine receptor CCR6, it attracted immature dendritic cells and memory T cells, thereby establishing a link with the adaptive immune system 26 . All these processes are necessary for wound healing and tissue repair, demonstrating the multifunctional role of Beta defensing protein. Several members of the Beta defensin protein family have been shown to be associated with autoimmune diseases such as psoriasis, systemic lupus erythematosus, and Sjogren's syndrome 27 – 30 . The MR results in this study suggested that DEFB135 may be a link in the pathogenesis of RA, but more research is needed before DEFB135 can be identified as a drug target for RA. There were several limitations to this study. First, we only used a set of pQTL data to assess the association between plasma proteins and RA, and did not conduct external proteomics studies to validate these findings of ours. Secondly, we mainly investigated the effect of plasma causal proteins on the risk of RA in the population. However, the contribution of other components such as fatty acids 31 , hormones 32 and gut microbiota 33 should not be overlooked. In addition, the exposure and outcome data used in this study were obtained from European populations, and further studies are needed to confirm whether the results of this study are applicable to other regions, such as Asia, Africa and the Americas. Conclusion Overall, our study investigated the causal relationship between plasma proteins and RA through forward and reverse MR analysis, revealing that increased plasma levels of SPAG11B and DEFB135 reduced the risk of RA. In addition, SPAG11B showed reverse causality with RA, suggesting that it had the potential to be a novel biomarker for RA. On the other hand, DEFB135 did not show reverse causality with RA and might be a potential therapeutic target for RA. However, it is necessary to establish RA cell lines and animal models for in vivo and in vitro experiments in the future to clarify the role of SPAG11B and DEFB135 in the progression of RA, and verify their potential as diagnostic markers or drug targets. Declarations Ethics declarations All study data for this study are derived from established studies that have received ethical clearance from their respective institutions, so no informed consent or ethical approval is required for this study. Role of funding source No funding was received for study design, data collection, data analysis, interpretation or report writing. Data availability statement The data presented in this study are included in the article/supplementary materials/references, and further inquiries can contact the authors. Conflicts of interest The authors declare that the study was conducted in the absence of any business or financial relationship that could be perceived as a potential conflict of interest. Author Contribution KL designed the study. KL, QL and WLacquired and analyzed the data. KL, RS and YL drafted the article. All authors read and approved the final manuscript. Acknowledgement We acknowledge the participants and researchers of the deCODE Genetics, FinnGen, Integrative Epidemiology Unit OpenGWAS and UK Biobank studies. References McInnes IB, Schett G. Pathogenetic insights from the treatment of rheumatoid arthritis. Lancet. 2017;389(10086):2328–37. Hong LE, Wechalekar MD, Kutyna MM et al. IDH Mutant Myeloid Neoplasms are Associated with Seronegative Rheumatoid Arthritis and Innate Immune Activation. Blood 2024. 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Supplementary Files SupplementaryFigures.docx SupplementaryTables.xlsx Cite Share Download PDF Status: Published Journal Publication published 04 Jun, 2025 Read the published version in BMC Rheumatology → Version 1 posted Editorial decision: Revision requested 29 Apr, 2025 Reviews received at journal 28 Apr, 2025 Reviewers agreed at journal 28 Apr, 2025 Reviews received at journal 25 Apr, 2025 Reviewers agreed at journal 25 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviews received at journal 13 Apr, 2025 Reviews received at journal 12 Apr, 2025 Reviewers agreed at journal 11 Apr, 2025 Reviewers agreed at journal 10 Apr, 2025 Reviewers agreed at journal 02 Apr, 2025 Reviewers invited by journal 01 Apr, 2025 Editor invited by journal 01 Apr, 2025 Editor assigned by journal 29 Mar, 2025 Submission checks completed at journal 29 Mar, 2025 First submitted to journal 17 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-6248365","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":442588389,"identity":"ff6cc89c-5549-45bb-9b29-0e440be5691c","order_by":0,"name":"Kun Lin","email":"","orcid":"","institution":"The Eighth Clinical Medical College of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Lin","suffix":""},{"id":442588390,"identity":"bfbf1cb5-086a-4409-8bdf-8b6911795429","order_by":1,"name":"Qi Lin","email":"","orcid":"","institution":"The Eighth Clinical Medical College of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Lin","suffix":""},{"id":442588391,"identity":"d1877bf8-f246-480d-9cd6-d09a35982701","order_by":2,"name":"Weifeng Lv","email":"","orcid":"","institution":"The Eighth Clinical Medical College of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Weifeng","middleName":"","lastName":"Lv","suffix":""},{"id":442588392,"identity":"502f12f2-37cf-434a-9bd6-56d06e00d916","order_by":3,"name":"Yao Li","email":"","orcid":"","institution":"The Eighth Clinical Medical College of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Li","suffix":""},{"id":442588393,"identity":"3312f6eb-ef47-4bed-abee-c3de04f94894","order_by":4,"name":"Rong Su","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBAC9gYEm/FBQkUNYS08B5jhbGaDB2eOkaaFTfJhCzMetTAt/OcPfrrx53A+v3T7tYrEBjYG/vbuBPxaJJKZpXPbDlvOnHOm7EbiDhkGiTNnN+DVYi/BzMac23DYwOBGTtqNxDNsDAYSufi18PAfZmPO+QPRUpDYxkyEFoZkoBY2kJb0YwzEaZFINgb6Jd1AckYOs0TCmWM8BP3Cw3/w4eecP9YG/BLpDz/+qKiR42/vxa8FWbcBxKUkAPYHpKgeBaNgFIyCEQQA8ZVFuCHHazgAAAAASUVORK5CYII=","orcid":"","institution":"The Eighth Clinical Medical College of Guangzhou University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Rong","middleName":"","lastName":"Su","suffix":""}],"badges":[],"createdAt":"2025-03-18 01:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6248365/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6248365/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s41927-025-00521-y","type":"published","date":"2025-06-04T15:56:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81142979,"identity":"60103b2b-1a6f-45fe-a7a2-25a32abed702","added_by":"auto","created_at":"2025-04-22 17:10:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":298716,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of MR analysis revealing causality from plasma proteins on RA.\u003c/p\u003e\n\u003cp\u003eMR, Mendelian Randomization; FDR, false discovery rate; RA, rheumatoid arthritis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6248365/v1/f1783edf45372ac9989c2358.png"},{"id":81142972,"identity":"eb2aedf5-1d95-45a9-b207-3836c05b4bb8","added_by":"auto","created_at":"2025-04-22 17:10:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65715,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of MR analysis for 4907 plasma proteins on RA risk.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6248365/v1/5d9d60ad24fc31b8525d9c42.png"},{"id":81143291,"identity":"2384d059-666d-4ee1-bcc8-5aa41aadc3e0","added_by":"auto","created_at":"2025-04-22 17:18:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":155458,"visible":true,"origin":"","legend":"\u003cp\u003eMR analysis estimated two differentially expressed plasma proteins. Figure a and c respectively indicated SPAG11B and DEFB135 on RA. Scatterplot derived from MR on the potential of SPAG11B (b) and DEFB135 (d) to predict RA.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6248365/v1/f0b4dec1bcdc4a146c7bd0c1.png"},{"id":81143921,"identity":"73a963d7-e7ea-4db0-985d-da21faed6e5d","added_by":"auto","created_at":"2025-04-22 17:26:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":74654,"visible":true,"origin":"","legend":"\u003cp\u003eMR analysis estimated the association of SPAG11B with RA from IEU (a) and UKB (b).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6248365/v1/716fc3287d31ce4d27b412cc.png"},{"id":84242371,"identity":"9b726995-c960-41b8-ab83-67852a6236e1","added_by":"auto","created_at":"2025-06-09 16:06:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":995085,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6248365/v1/f7293dd2-0ddb-497e-bb74-af654ce4d382.pdf"},{"id":81142971,"identity":"fb3bf705-94b4-4851-8756-2e07c974f792","added_by":"auto","created_at":"2025-04-22 17:10:43","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":48512,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-6248365/v1/f25e9f5d40706a10004a2700.docx"},{"id":81142974,"identity":"29e61d51-e671-4cfc-b10d-d800c2110a93","added_by":"auto","created_at":"2025-04-22 17:10:43","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14013,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6248365/v1/fe08e1181aa755ba725dd846.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"SPAG11B, a potential biomarker for rheumatoid arthritis: A two-sample bidirectional mendelian randomization analysis ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRheumatoid arthritis (RA) is a chronic autoimmune disease that can cause joint inflammation and structural damage such as joint swelling, deformity, and muscle atrophy, resulting in functional impairment\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, the exact pathogenesis of RA is not yet fully understood and there is a lack of targeted therapeutic options, so the prevalence and incidence of RA are increasing year by year\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Exploring the etiology and potential therapeutic targets of RA is of great significance for developing therapeutic strategies to slow down its progression.\u003c/p\u003e \u003cp\u003ePlasma proteins are major regulators mediating numerous signaling pathways and biological processes and are considered biomarkers or therapeutic targets for disease\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Recently, proteomics-based studies have shown new promise in predicting disease events and potential therapeutic targets\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Unfortunately, few studies have been conducted to study RA-related plasma proteins based on large-scale population protein quantitative trait loci (pQTL)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGenetic variants are randomly classified at the time of conception, and the Mendelian randomization (MR) analysis is to minimize the effect of confounders by using genetic variants as instrumental variables (IVs) for exposure\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. This solves the problem that traditional experimental methods are unable to efficiently account for the causality between exposure factors and outcome variables due to the presence of confounders\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. MR can correlate plasma protein information obtained through high-throughput sequencing with genome-wide outcomes related to disease phenotypes\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Thus, the utilization of human blood proteomics data in MR studies will contribute to a deeper understanding of plasma proteins causally associated with RA, thereby identifying proteins associated with RA causality, improving our understanding of the genetic architecture of RA, and providing new evidence for the identification of drug targets.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and data sources\u003c/h2\u003e \u003cp\u003eThe analysis flowchart of this study was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Plasma proteomics data were obtained from the deCODE database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.decode.com/summarydata/\u003c/span\u003e\u003cspan address=\"https://www.decode.com/summarydata/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and RA data were collected from FinnGen (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://r9.risteys.finngen.fi/\u003c/span\u003e\u003cspan address=\"https://r9.risteys.finngen.fi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Integrative Epidemiology Unit OpenGWAS (IEU, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and UK biobank (UKB, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ukbiobank.ac.uk\u003c/span\u003e\u003cspan address=\"https://www.ukbiobank.ac.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenetic instruments selection\u003c/h3\u003e\n\u003cp\u003eIn this study, pQTL data from Ferkingstad et al. were used as exposure\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. This dataset was based on 35559 Icelanders who were tested for 4907 plasma proteins using the SomaScan version 4 assay (SomaLogic) and found 18084 correlations between sequence variations and plasma protein levels\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Detailed information on genotyping, interpolation, and quality control can be found in the original paper\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. For genetic instrumental variables (IV), we selected single nucleotide polymorphisms (SNPs) with genome-wide significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e). To minimize the associated horizontal pleiotropy, we needed to eliminate the effect of linkage disequilibrium (LD, LD windows 10000kb, clumping r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). To quantify the potential of pQTL to predict RA, \u003cem\u003eF\u003c/em\u003e-statistics was used as an assessment tool, and IVs with \u003cem\u003eF\u003c/em\u003e-statistics\u0026thinsp;\u0026ge;\u0026thinsp;10 were considered to have sufficient predictive power to be included in the next analysis.\u003c/p\u003e\n\u003ch3\u003eData sources for RA\u003c/h3\u003e\n\u003cp\u003eIn the preliminary MR analysis phase, data for the RA cohort were obtained from the FinnGen database (M13_RHEUMA), which was harvested from 392423 participants (12555 cases and 379868 controls) and analyzed for 16380169 SNPs. The externally validated RA data were obtained from the IEU (ebi-a-GCST90013534) and UKB (ukb-d-M13_RHEUMA) databases. Among them, the IEU cohort included 14361 cases and 43923 controls, while UKB brought 1605 cases and 359589 controls into the cohort.\u003c/p\u003e\n\u003ch3\u003eMR analysis\u003c/h3\u003e\n\u003cp\u003eAll MR analysis in this study were performed using the TwoSampleMR package in R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/MRCIEU/TwoSampleMR\u003c/span\u003e\u003cspan address=\"https://github.com/MRCIEU/TwoSampleMR\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Analysis methods included Inverse Variance Weighting (IVW), MR-Egger, and Weighted Median, with IVW selected as the primary method to determine the causal effect of plasma proteins on RA. In addition, the Benjamini-Hochberg method was used to perform multiple test correction for all \u003cem\u003ep\u003c/em\u003e-values, and multiple comparisons were corrected using the Benjamini-Hochberg false discovery rate (FDR), with the threshold set at 0.2. In sensitivity analysis, we used a marker of heterogeneity for the IVW method (Cochran Q-derived \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) to assess potential heterogeneity among IVs. The intercept obtained in MR-Egger regression is an indicator of directional pleiotropy (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is considered to be present for directed pleiotropy)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Therefore, MR-Egger regression was used to ensure the validity of MR analysis results. Leave-one-out analysis was used to evaluate whether a single SNP would drive or bias MR results.\u003c/p\u003e\n\u003ch3\u003eReverse MR analysis\u003c/h3\u003e\n\u003cp\u003eBased on the same criteria for MR analysis as described above, we performed a reverse Mendelian randomization analysis using the FinnGen RA cohort data as the exposure and deCODE plasma proteins identified as being associated with RA in the preliminary MR analysis as an outcome to explore potential reverse causality. The analysis was carried out by IVW (primary method), MR-Egger and Weighted Median.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCausal effects of plasma proteins on RA\u003c/h2\u003e \u003cp\u003ePreliminary MR analysis assessed 4907 plasma proteins for potential association with RA. After adjusting for FDR, two proteins significantly associated with RA were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), namely human sperm associated antigen 11 B (SPAG11B) and defensin beta 135 (DEFB135). In detail, elevated plasma levels of SPAG11B (OR\u0026thinsp;=\u0026thinsp;0.49, 95% CI\u0026thinsp;=\u0026thinsp;0.40\u0026ndash;0.61, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.19\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e, using the IVW method) and DEFB135 (OR\u0026thinsp;=\u0026thinsp;0.28, 95% CI\u0026thinsp;=\u0026thinsp;0.15\u0026ndash;0.52, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.51\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, using the IVW method ) attenuated the risk of RA. The results of SPAG11B were replicated in MR-Egger and Weighted Median analysis methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and b, genetic IVs shown in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea). The results of DEFB135 in Weighted Median were consistent with those of IVW, but \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 in MR-Egger analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec and d, genetic IVs shown in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb). Analysis combined with intercept and \u003cem\u003ep\u003c/em\u003e-value in MR-Egger regression analysis showed that any potential pleiotropy of a single SNP was balanced, which meant that the probability of MR results being biased was relatively low (for SPAG11B, intercept\u0026thinsp;=\u0026thinsp;0.033, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.40; for DEFB135, intercept\u0026thinsp;=\u0026thinsp;0.091, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.32). Furthermore, no heterogeneity was observed for SPAG11B (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.52). Although the Cochran's Q Test for DEFB135 showed the presence of heterogeneity (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.81\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) that may be caused by different analytical platforms, experiments and populations, this did not affect the reliability of IVW results, which were acceptable\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExternal Validation\u003c/h3\u003e\n\u003cp\u003eIn the external validation phase, we selected two additional RA datasets and used the same analysis strategy to re-examine the two plasma proteins identified in the preliminary MR analysis as being associated with RA. Of these, the findings for SPAG11B in IEU (OR\u0026thinsp;=\u0026thinsp;0.26, 95% CI\u0026thinsp;=\u0026thinsp;0.14\u0026ndash;0.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.06\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, using the IVW method) and UKB (OR\u0026thinsp;=\u0026thinsp;0.99, 95% CI\u0026thinsp;=\u0026thinsp;0.990\u0026ndash;0.994, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.17\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e, using the IVW method) were consistent with those of the preliminary MR analysis, with higher plasma level associated with a lower risk of RA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). No bias was seen in the multiplicity test for SPAG11B in both IEU and UKB (Pleiotropy and heterogeneity analysis shown in supplementary Table\u0026nbsp;1a and b). However, the external validation results of DEFB135 showed no significant correlation with the occurrence of RA (for IEU, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.10; for UKB, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06; detailed results shown in supplementary Table\u0026nbsp;2), which was inconsistent with the preliminary MR results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eReverse MR analysis\u003c/h2\u003e \u003cp\u003eTo examine whether there is a reverse causality between RA and candidate proteins, we next performed reverse MR analysis of the two plasma proteins identified in the preliminary analysis. The MR results of IVW showed that the occurrence of RA caused a reduction in plasma levels of SPAG11B (OR\u0026thinsp;=\u0026thinsp;0.93, 95% CI\u0026thinsp;=\u0026thinsp;0.89\u0026ndash;0.98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004, Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ea and b). For DEFB135, the reverse MR results showed that there was no reverse causal effect of RA on DEFB135, meaning that the occurrence of RA did not lead to changes in plasma levels of DEFB135 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.93, supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe incidence of RA is on the rise\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Exploring the exact etiology and potential pathogenic mechanisms of RA to provide more effective treatment options for patients and to improve the prognosis is a current scientific issue that needs to be addressed. In this study, we used GWAS summary statistics from FinnGen, IEU and UKB and further performed proteomics MR analysis based on a large pQTL population to systematically evaluated the causal relationship between 4907 plasma proteins and RA.\u003c/p\u003e \u003cp\u003eBased on preliminary MR analysis of the FinnGen RA cohort, we found that SPAG11B was associated with the development of RA. Further external validation results based on the IEU and UKB RA cohorts were consistent. The higher the plasma level of SPAG11B, the lower the risk of RA. Reverse MR analysis revealed that SPAG11B plasma levels acquired changes when RA occurred. Specifically, the occurrence of RA caused a decrease in plasma levels of SPAG11B, suggesting its ability as a potential biomarker for predicting the occurrence of RA. The specific function of SPAG11B has not been clearly elucidated, but it is currently generally believed to be involved in sperm maturation\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. A study by Alexandre et al. demonstrated that SPAG11B had trypsin-like inhibitory activity and showed significant inhibition of trypsin-like activity, joint edema formation, and release of IL-6 and CXCL1/KC inflammatory factors through lentivirus-mediated heterologous expression of endogenous hSPAG11B/C in an animal model of arthritis\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The MR results in this study supported this experimental conclusion, and the OR value also suggested that elevated plasma levels of SPAG11B can reduce the risk of RA.\u003c/p\u003e \u003cp\u003eFor DEFB135, the preliminary MR analysis found an association between it and the risk of RA. Elevated plasma levels of DEFB135 could reduce the odds of developing RA, and it may play a protective role in the development of RA. However, the results were not reproduced during the external validation phase. The MR results of the IEU and UKB RA cohorts did not show significant correlation with the development of RA. Notably, reverse MR results suggested that there was no reverse causality between DEFB135 and RA. In other words, the occurrence of RA did not cause changes in plasma levels of DEFB135. This finding suggested that DEFB135 may be an important molecular node affecting the pathogenesis of RA, which may bring new potential therapeutic targets and drug development ideas for RA. DEFB135 is a secreted antibacterial protein that is a member of the Beta defensin protein family\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The Beta defensin protein family is an important part of the mammalian innate defense system and plays an integral role in the specificity and reactivity of immune cells against inflammatory stimuli and pathogens\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. It was noted that the inhibitory effect of rNOD1 on Escherichia coli was through the activation of NF-κB signaling to induce DEFB135 expression\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In addition to its antimicrobial activity, Beta defensin protein induced the expression of growth factors and stimulated the proliferation of endothelial cells, thus participating in the formation of new blood vessels\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. By interacting with the chemokine receptor CCR6, it attracted immature dendritic cells and memory T cells, thereby establishing a link with the adaptive immune system\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. All these processes are necessary for wound healing and tissue repair, demonstrating the multifunctional role of Beta defensing protein. Several members of the Beta defensin protein family have been shown to be associated with autoimmune diseases such as psoriasis, systemic lupus erythematosus, and Sjogren's syndrome\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The MR results in this study suggested that DEFB135 may be a link in the pathogenesis of RA, but more research is needed before DEFB135 can be identified as a drug target for RA.\u003c/p\u003e \u003cp\u003eThere were several limitations to this study. First, we only used a set of pQTL data to assess the association between plasma proteins and RA, and did not conduct external proteomics studies to validate these findings of ours. Secondly, we mainly investigated the effect of plasma causal proteins on the risk of RA in the population. However, the contribution of other components such as fatty acids\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, hormones\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e and gut microbiota\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e should not be overlooked. In addition, the exposure and outcome data used in this study were obtained from European populations, and further studies are needed to confirm whether the results of this study are applicable to other regions, such as Asia, Africa and the Americas.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, our study investigated the causal relationship between plasma proteins and RA through forward and reverse MR analysis, revealing that increased plasma levels of SPAG11B and DEFB135 reduced the risk of RA. In addition, SPAG11B showed reverse causality with RA, suggesting that it had the potential to be a novel biomarker for RA. On the other hand, DEFB135 did not show reverse causality with RA and might be a potential therapeutic target for RA. However, it is necessary to establish RA cell lines and animal models for in vivo and in vitro experiments in the future to clarify the role of SPAG11B and DEFB135 in the progression of RA, and verify their potential as diagnostic markers or drug targets.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEthics declarations\u003c/h2\u003e \u003cp\u003eAll study data for this study are derived from established studies that have received ethical clearance from their respective institutions, so no informed consent or ethical approval is required for this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRole of funding source\u003c/h2\u003e \u003cp\u003eNo funding was received for study design, data collection, data analysis, interpretation or report writing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003eThe data presented in this study are included in the article/supplementary materials/references, and further inquiries can contact the authors.\u003c/p\u003e \u003c/div\u003e\u003ch2\u003eConflicts of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that the study was conducted in the absence of any business or financial relationship that could be perceived as a potential conflict of interest.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eKL designed the study. KL, QL and WLacquired and analyzed the data. KL, RS and YL drafted the article. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe acknowledge the participants and researchers of the deCODE Genetics, FinnGen, Integrative Epidemiology Unit OpenGWAS and UK Biobank studies.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMcInnes IB, Schett G. Pathogenetic insights from the treatment of rheumatoid arthritis. Lancet. 2017;389(10086):2328\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong LE, Wechalekar MD, Kutyna MM et al. IDH Mutant Myeloid Neoplasms are Associated with Seronegative Rheumatoid Arthritis and Innate Immune Activation. Blood 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinckh A, Gilbert B, Hodkinson B, et al. Global epidemiology of rheumatoid arthritis. Nat Rev Rheumatol. 2022;18(10):591\u0026ndash;602.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Matteo A, Bathon JM, Emery P. Rheumatoid arthritis. Lancet. 2023;402(10416):2019\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen J, Xu F, Ruan X, et al. Therapeutic targets for inflammatory bowel disease: proteome-wide Mendelian randomization and colocalization analyses. EBioMedicine. 2023;89:104494.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng X, Wu WY, Onwuka JU, et al. Lung cancer risk discrimination of prediagnostic proteomics measurements compared with existing prediction tools. J Natl Cancer Inst. 2023;115(9):1050\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan S, Xu F, Zhang H, et al. Proteomic insights into modifiable risk of venous thromboembolism and cardiovascular comorbidities. J Thromb Haemost. 2024;22(3):738\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang C, Fagan AM, Perrin RJ, Rhinn H, Harari O, Cruchaga C. Mendelian randomization and genetic colocalization infer the effects of the multi-tissue proteome on 211 complex disease-related phenotypes. Genome Med. 2022;14(1):140.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo H, Huemer MT, Petrera A, et al. Association of plasma proteomics with incident coronary heart disease in individuals with and without type 2 diabetes: results from the population-based KORA study. Cardiovasc Diabetol. 2024;23(1):53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerreira MB, Kobayashi M, Costa RQ, et al. Unsupervised clustering to differentiate rheumatoid arthritis patients based on proteomic signatures. Scand J Rheumatol. 2023;52(6):619\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEscal J, Neel T, Hodin S et al. Proteomics analyses of human plasma reveal triosephosphate isomerase as a potential blood marker of methotrexate resistance in rheumatoid arthritis. Rheumatology (Oxford). 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA. 2021;326(16):1614\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuan QQ, Wang H, Su WM, et al. TBK1, a prioritized drug repurposing target for amyotrophic lateral sclerosis: evidence from druggable genome Mendelian randomization and pharmacological verification in vitro. BMC Med. 2024;22(1):96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobinson PC, Choi HK, Do R, Merriman TR. Insight into rheumatological cause and effect through the use of Mendelian randomization. Nat Rev Rheumatol. 2016;12(8):486\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerkingstad E, Sulem P, Atlason BA, 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\u003eYuan S, Carter P, Mason AM, Burgess S, Larsson SC. Coffee Consumption and Cardiovascular Diseases: A Mendelian Randomization Study. Nutrients. 2021;13:7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRibeiro CM, Queiroz DB, Patrao MT, et al. Dynamic changes in the spatio-temporal expression of the beta-defensin SPAG11C in the developing rat epididymis and its regulation by androgens. Mol Cell Endocrinol. 2015;404:141\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu SG, Zou M, Yao GX, et al. Androgenic regulation of beta-defensins in the mouse epididymis. Reprod Biol Endocrinol. 2014;12:76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrabovich AP, Jarvi K, Diamandis EP. Verification of male infertility biomarkers in seminal plasma by multiplex selected reaction monitoring assay. Mol Cell Proteom. 2011;10(12):M110004127.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDenadai-Souza A, Ribeiro CM, Rolland C, et al. Effect of tryptase inhibition on joint inflammation: a pharmacological and lentivirus-mediated gene transfer study. Arthritis Res Ther. 2017;19(1):124.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo M, Wu F, Zhang Z, et al. Characterization of Rabbit Nucleotide-Binding Oligomerization Domain 1 (NOD1) and the Role of NOD1 Signaling Pathway during Bacterial Infection. Front Immunol. 2017;8:1278.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatil AA, Cai Y, Sang Y, Blecha F, Zhang G. Cross-species analysis of the mammalian beta-defensin gene family: presence of syntenic gene clusters and preferential expression in the male reproductive tract. Physiol Genomics. 2005;23(1):5\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchutte BC, Mitros JP, Bartlett JA, et al. Discovery of five conserved beta -defensin gene clusters using a computational search strategy. Proc Natl Acad Sci U S A. 2002;99(4):2129\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSorensen OE, Cowland JB, Theilgaard-Monch K, Liu L, Ganz T, Borregaard N. Wound healing and expression of antimicrobial peptides/polypeptides in human keratinocytes, a consequence of common growth factors. J Immunol. 2003;170(11):5583\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoczulla R, von Degenfeld G, Kupatt C, et al. An angiogenic role for the human peptide antibiotic LL-37/hCAP-18. J Clin Invest. 2003;111(11):1665\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang D, Chertov O, Bykovskaia SN, et al. Beta-defensins: linking innate and adaptive immunity through dendritic and T cell CCR6. Science. 1999;286(5439):525\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCardner M, Tuckwell D, Kostikova A et al. Analysis of serum proteomics data identifies a quantitative association between beta-defensin 2 at baseline and clinical response to IL-17 blockade in psoriatic arthritis. RMD Open 2023;9(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKolbinger F, Loesche C, Valentin MA, et al. beta-Defensin 2 is a responsive biomarker of IL-17A-driven skin pathology in patients with psoriasis. J Allergy Clin Immunol. 2017;139(3):923\u0026ndash;e932928.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVordenbaumen S, Fischer-Betz R, Timm D, et al. Elevated levels of human beta-defensin 2 and human neutrophil peptides in systemic lupus erythematosus. Lupus. 2010;19(14):1648\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaneda Y, Yamaai T, Mizukawa N, et al. Localization of antimicrobial peptides human beta-defensins in minor salivary glands with Sjogren's syndrome. Eur J Oral Sci. 2009;117(5):506\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao X, Yang Y, Jiang Z, Ma W, Yao X. The causal impact of saturated fatty acids on rheumatoid arthritis: a bidirectional Mendelian randomisation study. Front Nutr. 2024;11:1337256.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalik S, Chakraborty D, Agnihotri P, Sharma A, Biswas S. Mitochondrial functioning in Rheumatoid arthritis modulated by estrogen: Evidence-based insight into the sex-based influence on mitochondria and disease. Mitochondrion. 2024;76:101854.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao X, Zhang R, Wang X. The gut-joint axis: Genetic evidence for a causal association between gut microbiota and seropositive rheumatoid arthritis and seronegative rheumatoid arthritis. Med (Baltim). 2024;103(8):e37049.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-rheumatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brhm","sideBox":"Learn more about [BMC Rheumatology](http://bmcrheumatol.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/brhm/default.aspx","title":"BMC Rheumatology","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rheumatoid arthritis, plasma protein, SPAG11B, DEFB135, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-6248365/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6248365/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe incidence of rheumatoid arthritis (RA) is rising. However, its pathogenesis has not been fully understood, and the current therapeutic regimens are still limited. The aim of this study was to investigate the causal effect of plasma proteins on RA using Mendelian randomization (MR) analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe performed MR analysis with 4907 plasma protein genetic associations used for exposure and RA genome-wide association data used as outcomes. The method was dominated by Inverse Variance Weighting, in addition to MR-Egger and Weighted Median. Meanwhile, further external validation and reverse MR analysis were conducted to systematically assess the causal relationship between plasma proteins and RA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult: \u003c/strong\u003ePreliminary MR analysis identified two proteins (SPAG11B and DEFB135) associated with RA, and elevated plasma levels of both proteins would reduce the risk of RA (for SPAG11B, OR =0.49, 95% CI =0.40-0.61, \u003cem\u003ep\u003c/em\u003e =1.19×10\u003csup\u003e-10\u003c/sup\u003e; for DEFB135, OR =0.28, 95% CI =0.15-0.52, \u003cem\u003ep\u003c/em\u003e =4.51×10\u003csup\u003e-5\u003c/sup\u003e, using the IVW method). In the external validation phase, the results were reproducible for SPAG11B, but not for DEFB135. Reverse MR analysis pointed out that RA exhibited reverse causality for plasma levels of SPAG11B (OR=0.93, 95% CI=0.89-0.98, \u003cem\u003ep\u003c/em\u003e=0.004), but not for DEFB135 (\u003cem\u003ep\u003c/em\u003e=0.93).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe results of MR analysis in this study supported that SPAG11B as a novel biomarker for RA was worthy of further investigation.\u003c/p\u003e","manuscriptTitle":"SPAG11B, a potential biomarker for rheumatoid arthritis: A two-sample bidirectional mendelian randomization analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-22 17:10:38","doi":"10.21203/rs.3.rs-6248365/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-29T06:01:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-29T03:34:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324423531780788205912719293583489898173","date":"2025-04-29T03:06:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-25T20:29:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316396935205862250997907240143285909902","date":"2025-04-25T19:49:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166676827270235989545993554649278070330","date":"2025-04-23T06:12:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-13T16:06:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-12T19:57:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162543190718093670454345969596241707062","date":"2025-04-11T12:39:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336274696786060616336878152998630472189","date":"2025-04-10T18:58:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336562128272885117366153439326692510812","date":"2025-04-02T05:14:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-02T00:04:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-01T20:03:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-29T06:28:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-29T06:28:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Rheumatology","date":"2025-03-18T01:28:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-rheumatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brhm","sideBox":"Learn more about [BMC Rheumatology](http://bmcrheumatol.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/brhm/default.aspx","title":"BMC Rheumatology","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"049f517c-5c81-4948-b454-da41c419b2aa","owner":[],"postedDate":"April 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T15:59:00+00:00","versionOfRecord":{"articleIdentity":"rs-6248365","link":"https://doi.org/10.1186/s41927-025-00521-y","journal":{"identity":"bmc-rheumatology","isVorOnly":false,"title":"BMC Rheumatology"},"publishedOn":"2025-06-04 15:56:52","publishedOnDateReadable":"June 4th, 2025"},"versionCreatedAt":"2025-04-22 17:10:38","video":"","vorDoi":"10.1186/s41927-025-00521-y","vorDoiUrl":"https://doi.org/10.1186/s41927-025-00521-y","workflowStages":[]},"version":"v1","identity":"rs-6248365","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6248365","identity":"rs-6248365","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}