Integrative Analysis of Genetic, Proteomic, and Transcriptomic Data Reveals Novel Therapeutic Targets for Rheumatoid Arthritis

Integrative Analysis of Genetic, Proteomic, and Transcriptomic Data Reveals Novel Therapeutic Targets for Rheumatoid Arthritis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrative Analysis of Genetic, Proteomic, and Transcriptomic Data Reveals Novel Therapeutic Targets for Rheumatoid Arthritis Wei Yang, chenlin liu, Zhenhua Li, Miao Cui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5510112/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Currently, the treatment and prevention of rheumatoid arthritis (RA) face significant challenges. In the pursuit of new therapeutic avenues, Mendelian randomization (MR) analysis has emerged as a crucial research method. Building on this, we conducted a comprehensive genome-wide analysis of MR of drug targets to identify potential therapeutic intervention points for RA. MethodS In this study, we constructed a comprehensive analytical framework aimed at identifying and validating potential biomarkers for RA. The framework begins with a two-sample MR study utilizing two large plasma protein datasets. Building upon this foundation, we conducted an in-depth exploration of the identified positive proteins using the summary data-based Mendelian randomization (SMR) method, combined with Bayesian co-localization analysis of coding genes. This approach allowed us to reveal RA multi-omics biomarkers, and we employed the LDSC analysis method to investigate the genetic correlation between the identified genes and complex diseases. Additionally, a phenome-wide association study (PheWAS) was performed on the positive genes mapped by the identified proteins, alongside an exploration of their expression in various tissues. Subsequently, we expanded our analysis to include protein-protein interaction (PPI) network analysis, gene ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Finally, we conducted drug prediction and molecular docking studies. The purpose of these comprehensive analytical methods is to thoroughly investigate the biological functions and mechanisms of action of these biomarkers, thereby providing a scientific basis for the development of more effective and targeted therapeutic drugs. Our findings encompass RA and its multiple subtypes, including seropositive RA, seronegative RA, and juvenile RA. Results This study presents a multidimensional analysis of plasma proteins in relation to RA and its subtypes. In the MR analysis of Icelandic plasma protein - Quantitative Trait Loci(pQTLs) associated with RA, the findings revealed 137, 150, 95, and 69 positive associations for RA, seropositive RA, seronegative RA, and juvenile RA, respectively. Additionally, the MR analysis of plasma pQTLs from the UK Biobank database identified 156, 167, 106, and 81 positive plasma proteins for the same conditions. After applying false discovery rate (FDR) correction, the MR analysis of plasma pQTLs and RA in Iceland identified PPA2, JUND, AGER, F2, and PMEL as significantly positive proteins. In the MR analysis of plasma pQTLs and RA within the UK Biobank database, the significantly positive proteins included AIF1, ARG2, ATP5IF1, CCL19, CDSN, CEP43, MXRA8, PADI2, RPA2, SLC16A1, TNF, and TNFRSF14. For the MR analysis of plasma pQTLs and seropositive RA in Iceland, TGFBR3, FCGR3B, TIMP4, and PMEL were identified as significantly positive proteins. In the UK Biobank MR analysis of plasma pQTLs and seropositive RA, the following proteins were significantly positive: AIF1, APOBR, ATP6V1G2, BCL2L15, C1QTNF6, CCL19, CD40, CDSN, CEP43, CX3CL1, FCGR2B, FCRL1, IL6R, MXRA8, TGFBR3, TNF, and TNFRSF14. The MR analysis of plasma pQTLs and seronegative RA in the UK Biobank identified AIF1, CEP43, and TNF as significantly positive proteins. Following Bonferroni correction, the MR analysis of UK Biobank plasma pQTLs and RA highlighted AIF1, CCL19, CDSN, CEP43, and TNF as significantly positive proteins. For seropositive RA in the UK Biobank, AIF1, ATP6V1G2, BCL2L15, CCL19, CDSN, CEP43, IL6R, and TNF were identified as significantly positive proteins. Lastly, the MR analysis of plasma pQTLs and seronegative RA in the UK Biobank confirmed AIF1 and TNF as significantly positive proteins.In the context of single-gene SMR analysis, the examination of Icelandic plasma pQTLs in relation to RA—specifically seropositive RA, seronegative RA, and juvenile RA—identified 28, 34, 21, and 15 positive plasma associations, respectively. For proteins, MR analysis of UK Biobank plasma pQTLs revealed 38, 37, 21, and 12 positive plasma proteins, respectively. Building on the findings from the previous two-sample MR analysis, Bayesian co-localization was subsequently performed. Among the Icelandic plasma pQTLs, F2 emerged as a significantly positive gene associated with RA. In the UK Biobank plasma pQTLs, the genes ATP5IF1, CCL19, CX3CL1, HDGF, MXRA8, and TNFRSF14 were identified as significantly positive. LDSC analysis demonstrated a significant positive genetic correlation between CCL19 and both RA and seropositive RA, as well as a significant positive genetic correlation between TNFRSF14 and both RA and seropositive RA. These results suggest that FCGR3A, ADAM15, CCL19, CX3CL1, NFKBIE, TNFRSF14, F2, ATP5IF1, HDGF, and MXRA8 may serve as key therapeutic targets for RA. Notably, TNFRSF14 and CCL19 warrant further investigation as important genes for understanding the pathogenesis and potential therapeutic strategies for RA and its subtypes. Conclusion Through a comprehensive analysis of plasma proteomic and transcriptomic data, we successfully identified key therapeutic targets for RA and its three clinical subtypes. Specifically, we identified FCGR3A, ADAM15, CCL19, CX3CL1, NFKBIE, TNFRSF14, F2, ATP5IF1, HDGF, and MXRA8 as potential therapeutic targets for RA. By integrating genetic relatedness scores, we further elucidated the significance of these findings. Notably, TNFRSF14 and CCL19 emerged as critical genes warranting in-depth exploration regarding the pathogenesis of RA and its subtypes, as well as their potential as therapeutic targets. These results provide a scientific basis for the development of new immunotherapy approaches, combination treatment regimens, or targeted intervention strategies, and are anticipated to advance research progress in the treatment of RA. Biological sciences/Genetics Biological sciences/Immunology Health sciences/Diseases rheumatoid arthritis Mendelian randomization druggable target genes plasma proteins genetic correlation Background Rheumatoid arthritis (RA) is a prevalent autoimmune disease characterized by progressive joint damage and various extra-articular manifestations that can lead to permanent disability and often affect multiple organs throughout the body. The United States Coordinating Committee on Autoimmune Diseases reported in 2009 that approximately 20 million Americans suffer from autoimmune diseases( 1 ). According to the 2010 Global Burden of Disease (GBD) data, the global prevalence of RA is approximately 0.24%( 2 ), with the highest incidence rates observed in the United States and Nordic countries, ranging from 0.5–1%( 3 , 4 ). The 2017 GBD data indicates an increase in global prevalence to 0.27%. Notably, North America exhibits the highest prevalence of RA at 0.38%, followed closely by Western Europe at 0.35%( 5 ). Additionally, World Health Organization (WHO) data from 2019 reports that 18 million people worldwide were affected by RA, with women being the predominantly affected demographic( 6 ). The variations in incidence across different countries, along with evidence of population migration, suggest that the pathogenesis of RA is highly complex and may involve multiple factors, including genetics, diet, immunity, and environmental influences. The treatment and prevention of RA represents a complex medical challenge. Although the introduction of novel therapies, including tumor necrosis factor (TNF) antagonists, interleukin-17 inhibitors, interleukin-1 antagonists, B cell-depleting agents, and other biological agents, has been shown to alleviate symptoms and enhance functional outcomes, significant obstacles remain in the management of this condition. One major concern is the side effects associated with these medications, which can include increased neurological complications( 7 ), a heightened risk of infections( 8 ), and potential damage to liver and kidney functions( 9 ). Furthermore, prolonged use of immunosuppressants can compromise the immune system( 10 ). Another challenge is the variability in treatment response( 11 ), some patients experience limited symptom relief, and their conditions may even deteriorate. This variability is influenced by individual differences, disease heterogeneity, and other factors. To improve the efficacy of RA treatment, it is essential to continue exploring new therapeutic targets. Genome-wide association studies (GWAS) can identify single nucleotide polymorphisms (SNPs) linked to RA risk. However, the findings from GWAS must be integrated with comprehensive analyses to pinpoint causative genes and facilitate drug development. Without this integration, it will be challenging to accurately identify disease-causing genes or streamline the drug development process. This difficulty arises because the associated loci identified through GWAS may reside in intergenic regions, or their mechanisms of action on gene expression and function remain poorly understood. Therefore, further in-depth research is necessary to clarify the intrinsic relationship between these loci and the pathogenesis of RA. Mendelian randomization (MR) is a methodological approach that employs genetic variations as instrumental variables (IVs) to elucidate causal relationships between exposure factors and disease outcomes( 12 ). Recent technological advancements, particularly in aptamer and immunoassay platforms such as SomaScan and Olink, are enhancing MR analyses by facilitating the integration of genome-wide association study (GWAS) data with pooled results from pQTL studies, thereby revealing novel therapeutic targets. This approach holds significant promise, especially as protein - Quantitative Trait Loci (pQTLs) situated within drug-action gene regions are considered surrogate biomarkers, effectively reflecting gene expression levels during prolonged exposure states. In this context, we undertook a comprehensive, genome-wide druggability-based MR study aimed at identifying potential therapeutic strategies for RA. The study commenced with the collection of data on druggable genes, followed by the screening of genes associated with blood pQTLs. We subsequently analyzed the GWAS data for these genes in relation to RA using a two-sample MR analysis approach to pinpoint genes with strong associations to the disease. To bolster the robustness of our findings and further investigate the relationships between potential therapeutic targets and their phenotypes, we conducted single-gene Mendelian randomization studies (SMR) and Bayesian colocalization analyses. Additionally, we employed the LDSC analysis method to explore the genetic correlation between significant genes and complex diseases, performed phenotype-wide association (pheWAS) studies on noteworthy genes, and examined their expression across various tissues.We slao constructed a protein-protein interaction network for significant genes, conducted Gene Ontology (GO) enrichment analysis, and performed KEGG pathway analysis, along with drug prediction and molecular docking simulations. This comprehensive series of analyses offers valuable insights and guidance for the development of more efficient and targeted treatments for RA, which is anticipated to advance progress in this field. Methods Research design Figure 1 illustrates the analytical process employed in this study. We established an analytical framework that began with the integration of a plasma proteome-wide association study (PWAS) to derive data from two extensive plasma protein quantitative trait loci (pQTL) datasets. This was achieved through a two-sample Mendelian randomization (MR) analysis to identify potential positive proteins associated with RA. Subsequently, we conducted sensitivity analyses and reproducibility tests to thoroughly investigate the functional roles of these candidate plasma protein targets in the context of RA. We validated the identified coding gene loci shared by plasma proteins and RA through single-gene Mendelian randomization (SMR) analysis and Bayesian colocalization analysis. Concurrently, we employed the linkage disequilibrium score regression (LDSC) method to explore the genetic correlations between the identified positive genes and RA. For the significant positive genes identified, we performed a phenotype-wide association study (PheWAS) utilizing all genome-wide association studies (GWAS) from the latest version 11 data of the FinnGen database, examining their expression across various tissues to comprehensively investigate their potential functions. Additionally, we explored the underlying biological mechanisms of the putative protein targets through PPI analysis, Gene Ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. We also conducted drug prediction and molecular docking studies on the positive genes, providing a scientific basis for the development of more effective and targeted therapeutic drugs. Finally, we classified and evaluated the evidence from this study based on the results of the MR, SMR, colocalization analysis, and LDSC analysis, integrating findings from previous studies. Data sources for plasma proteomics In a comprehensive study, Egil Ferkingstad and colleagues utilized the SomaScan platform to investigate a cohort of 35,559 Icelanders, identifying 28,191 genetic associations with 4,907 proteins, all of which fell below their predetermined significance threshold(13). The majority of data for this research was sourced from the Icelandic Cancer Project (ICP) and the genetics program at deCODE Genetics in Reykjavik, contributing 52% and 48% of the total participants, respectively. The researchers employed a recursive conditional analysis method to pinpoint the most significant variation within each genomic region (±1Mb), designating this as the primary indicator of the plasma protein quantitative trait locus (n = 18,084), while other variations were categorized as secondary indicators (n = 10,107). The study yielded impressive results, successfully replicating findings from previous research, including an 83% replication rate of the SomaScan-based plasma protein quantitative trait loci (pQTL) in the INTERVAL study and a 64% replication of the Olink-based plasma protein quantitative trait loci (pQTL) in the SCALLOP consortium. Detailed genome-wide association study (GWAS) data have been made publicly available at (https://www.nature.com/articles/s41588-021-00978-w#Sec36). It is important to note that all participants in this study were of European ancestry. In October 2023, the UK Biobank released a genome-wide association study (GWAS) dataset focused on plasma proteins(14). The research team utilized the Olink platform to analyze 2,923 proteins as part of the UK Biobank Pharmaceutical Proteomics Project (UKB-PPP), resulting in the identification of 23,588 preliminary genetic associations. All P-values were below the significance threshold, and these associations were located within a ±1 Mb interval, with linkage disequilibrium (LD) r² values of less than 0.8. The study successfully validated 84% of the known protein quantitative trait loci (pQTL) identified in the antibody study, as well as 38% of the pQTL found in the aptamer study. The complete GWAS dataset is accessible via the following link: s3://ukbiobank.opendata.sagebase.org/. It is important to note that all participants in this study were of European ancestry. Data sources for rheumatoid arthritis The GWAS data for RA originates from the 11th version of the GWAS dataset released in the FinnGen database on June 24, 2024. This dataset includes data on 453,626 individuals, comprising 4,589 patients with RA and 449,037 healthy controls. Additionally, the GWAS data for seropositive RA encompasses 448,721 individuals, which includes 5,426 cases of seropositive RA and 443,295 healthy controls. Furthermore, the GWAS data for seronegative RA involves 453,733 individuals, consisting of 7,314 seropositive RA patients and 446,419 healthy controls. The complete GWAS dataset can be accessed and downloaded via the following link: [FinnGen Database](https://www.finngen.fi/en). The GWAS data for juvenile RA is derived from the genetic association map released on September 30, 2021, which covers 409,217 individuals, including 216 juvenile RA patients and 409,001 healthy controls(15). This complete dataset is also available for download at [Nature](https://www.nature.com/articles/s41588-021-00931-x). All samples included in this study are of European ancestry. Mendelian randomization (MR) analysis MR analysis is predicated on three fundamental assumptions that ensure the validity of instrumental variables in causal inference(16). First, the exposure factor must be directly related to the genetic variation. Second, the genetic variation must not be associated with any confounding factors that could obscure the relationship between the exposure and the outcome. Third, the effect of the genetic variation on the outcome should be transmitted solely through the exposure factor. This study utilized the "TwoSampleMR" software package (version 0.6.8) in R to conduct the MR analysis. We employed pQTLs from the pharmacogenome as exposure data, establishing a significance threshold at P < 5×10^-8. The significance threshold for both correlations was determined based on a P value of less than 0.05 for SNPs located within ±10,000 kb of the transcription start site (TSS) of each gene(17). SNPs were screened using a linkage disequilibrium coefficient (r²) criterion of less than 0.001, based on European samples from the 1000 Genomes Project. Phenotypes associated with the IVs were identified using the R package "phenoscanner" (version 1.0). We excluded SNPs that were directly associated with RA, as well as SNPs for traits directly linked to the disease. The screened SNP data were harmonized and subsequently subjected to MR analysis. For analyses involving a single SNP, we employed the Wald ratio method(18); in cases where multiple SNPs were available, we applied the random effects inverse variance weighting (IVW) method(19). We assessed heterogeneity among the individual causal effects of SNPs using Cochran's Q test and evaluated pleiotropy through the MR Egger intercept. Finally, we conducted false discovery rate (FDR) and Bonferroni corrections on the resulting P values, with a significance threshold set at 0.05.To control for linkage disequilibrium, we clustered cis-pQTLs using the "clump_data" function with parameters set to clump_kb = 10,000 and clump_r2 = 0.001. To assess the potential for weak instrumental variable bias, we calculated the F statistic, which quantifies the strength of the instrumental variable. The formula employed for this calculation is: F = R²(NK-1)/[K(1-R²)], where R² represents the estimated exposure variance explained by the instrumental variable, N denotes the sample size, and K indicates the number of IVs. If the F statistic is found to be less than 10, the corresponding SNP is classified as a weak instrumental variable and is subsequently excluded from the analysis to mitigate bias arising from weak instruments(20, 21). Single-gene SMR analysis In the process of validating plasma protein targets, we employed the SMR(22) and HEIDI methods(23) developed by Yang Jian's research group at West Lake University. These methodologies were utilized to assess the relationship between the expression of relevant protein-coding genes in pQTLGen blood samples and the risk of RA. The SMR analysis identified a single pQTL SNP that exhibited a strong correlation with the target gene region, which was utilized as an instrumental variable. The default P value for selecting the most correlated pQTL was established at 5 × 10⁻⁸. Furthermore, the SMR tool integrates the Heterogeneity in Dependency Tool (HEIDI) test to determine whether the association between gene expression and the outcome arises from linkage, rather than from the influence of SNPs on disease through the regulation of gene expression. A P value of less than 0.01 in the HEIDI test suggests that the association is attributable to linkage. Subsequently, we utilized the merged cis-eQTL and mQTL summary data as IVs to re-conduct the primary analysis. The main outcome of the study is presented as the change in disease odds ratio (OR) for each standard deviation (SD) increase in gene expression. Bayesian colocalization analysis This study aimed to investigate whether multiple genetic associations indicate a single causal variant located within the same genomic region. To achieve this objective, we employed colocalization analysis to assess whether the associations between positive proteins and gene loci identified through MR analysis are attributable to the same causal variant in the context of RA. This analysis utilizes a Bayesian model to infer the relationships among traits by calculating the posterior probabilities (PPH) associated with five hypotheses(24): (1) H0: There is no association between RA and any trait; (2) H1: RA is solely related to trait 1; (3) H2: RA is solely related to trait 2; (4) H3: RA is associated with both traits, but the associations arise from different causal variants; (5) H4: RA and the two traits are related and stem from the same causal variant. For the analysis, we implemented the "coloc.abf" algorithm with its default parameter settings. Specifically, the prior probability p1 that the SNP is associated with trait 1 was set to 1×10⁻⁴, the prior probability p2 that the SNP is associated with trait 2 was also set to 1×10⁻⁴, and the prior probability p12 for the correlation between both traits was set to 1×10⁻⁵. Our criteria for analysis are as follows: if PPH4 (the posterior probability of hypothesis H4) exceeds 0.75, we classify the association between plasma proteins and RA, as well as its subtypes, as significant colocalization; if PPH4 exceeds 0.5 or if the combined relationship of PPH3 and PPH4 exceeds 0.7, we consider moderate colocalization to be present. LDSC analysis In this study, we employed the Linkage Disequilibrium Score Regression (LDSC) analysis method to investigate the potential genetic association between positive genes and RA(25). The LDSC tool leverages genetic linkage disequilibrium (LD) to evaluate the strength of association with complex traits by estimating the LD score for each single nucleotide polymorphism (SNP). The scores range from -1 to 1, where -1 represents a complete negative genetic association and 1 denotes a complete positive genetic association. Furthermore, we performed a comprehensive analysis of the interaction between test statistics and linkage disequilibrium using the LDSC method to assess the potential influence of polygenic effects or biases on the statistical outcomes. Notably, LDSC effectively mitigates the issue of sample overlap during data processing, thereby enabling accurate and thorough evaluations of genetic correlations using genome-wide association study (GWAS) data. Phenogroup-wide association analysis We identified SNPs associated with significant druggable genes in blood samples through Bayesian colocalization analysis and compared these findings with the latest version 11 data from the FinnGen database (https://www.finngen.fi/en). Subsequently, we initiated a PheWAS to investigate the associations between positive druggable gene-related SNP variants and a diverse array of phenotypes(26). Expression status of genes in different tissues Investigating the expression levels of positively regulated genes in specific human tissues may elucidate their potential mechanisms as therapeutic targets for RA. To achieve this, we employed the Human Protein Atlas (https://www.proteinatlas.org), a comprehensive resource that offers detailed insights into the mRNA and protein levels of genes across human tissues(27). The atlas includes protein expression data from 44 normal human tissues, encompassing 76% of human genes, which amounts to a total of 15,323 genes. Protein-protein interaction network construction To visualize the interactions of genetically encoded proteins that may be significant for drug development, we utilized the PPI Networks tool. We selected the STRING database (https://string-db.org/) as our data source and established a confidence score threshold of 0.4 to filter valid interactions(28). Throughout this process, we maintained the default parameters of the STRING database to ensure the consistency and reliability of the analytical results. GO enrichment analysis and KEGG pathway analysis To enhance our understanding of the functional properties and biological connections of the pre-screened potential drug target genes, we employed the "clusterProfiler" package (version 4.12.6) in R software to conduct GO(29) and KEGG enrichment analyses(30). Our GO analysis encompasses three fundamental dimensions: biological process (BP), molecular function (MF), and cellular composition (CC). The objective is to elucidate the activity patterns and functional roles of these genes within biological systems and the specific cellular environments they inhabit. Furthermore, KEGG pathway analysis offers detailed insights into the metabolic pathways and signaling networks associated with these genes, which is essential for a comprehensive understanding of their biological roles and potential mechanisms of drug action. Through these analyses, we systematically delineated the functional networks of these genes within cells, thereby providing a robust biological foundation for drug development. Drug candidate prediction The DSigDB (http://dsigdb.tanlab.org/DSigDBv1.0/) is a comprehensive database comprising 22,527 gene sets and 17,389 drug compounds, which are associated with 19,531 genes(31). In our study, we employed SMR analysis and Bayesian co-localization analysis to identify positive proteins linked to significant druggable genes associated with RA. We subsequently conducted mapping analyses with compounds from the DSigDB database to predict potential drug candidates and assess the pharmacological activity of the target genes. This approach enables us to leverage the gene-compound information within the DSigDB database to investigate prospective drugs related to these druggable genes, thereby enhancing our understanding of the pharmacological properties of these targets. Ultimately, this provides a robust data foundation for drug development and research into disease treatment. Molecular docking Following a comprehensive analysis of the DSigDB database, we proceeded with the molecular docking process. This step aims to accurately assess the binding energy and interaction profiles of candidate drugs with their corresponding targets. Through rigorous screening and analysis of the docking results, we identified ligands exhibiting higher binding affinity and favorable interaction characteristics. Protein structure data were sourced from the Protein Data Bank (PDB, http://www.rcsb.org/). In instances where specific protein structure data were unavailable, we utilized an online tool developed by Yang Jianyi's research group at Shandong University (https://yanglab.qd.sdu.edu.cn/) to construct the PDB format of the missing data. Drug structure data were acquired from the U.S. National Library of Medicine Chemical Substances Database (https://pubchem.ncbi.nlm.nih.gov/), where the required data was downloaded in SDF format(32). Subsequently, we employed the OpenBabel 3.1.1 software tool (https://github.com) to convert the data into PDB format. During the molecular docking phase, we selected key drugs with significant p-values along with the proteins encoded by their respective target genes, utilizing the computerized protein-ligand docking software AutoDock 4.2.6 (http://autodock.scripps.edu/) for the docking procedure. The binding energy was then analyzed and calculated. To provide a visual representation of the docking results, we employed PyMol 3.0.5 software (https://www.pymol.org/). Results Mendelian randomization analysis results We initially conducted a two-sample MR analysis. The results of the MR analysis concerning plasma proteins and the four distinct types of RA are presented in Appendix Tables S1-4.Figures 2-5 shows volcano plots of MR analysis of plasma proteins with four different types of RA.In our MR analysis of Icelandic plasma pQTLs and RA, we identified a total of 137 positive plasma proteins. In the MR analysis of Icelandic plasma pQTLs and seropositive RA, we found 150 positive plasma proteins. For seronegative RA, the MR analysis revealed 95 positive plasma proteins, while the analysis of juvenile RA identified 69 positive plasma proteins. In the UK Biobank MR analysis of plasma pQTLs and RA, we detected 156 positive plasma proteins. The analysis of seropositive RA in the UK Biobank revealed 167 positive plasma proteins. In contrast, the MR analysis of UK Biobank plasma pQTLs and seronegative RA identified 106 positive plasma proteins. Similarly, the analysis of juvenile RA in the UK Biobank also found 106 positive plasma proteins. Overall, we identified 81 positive plasma proteins.Figures 6-9 present circular heatmaps of plasma proteins and the positive results of four RA MR assays. A comparison of the positive MR results across the two datasets reveals that, in RA, the following proteins are common to both datasets: INSL5, SERPINB1, USP8, RELT, MSR1, ENPP6, GRAP2, CRYBB1, ST13, UROD, TGFBR3, SELE, MAPKAPK2, NSFL1C, CCL19, F2, CD86, IL1RN, FCRL1, and GFRAL. In seropositive RA, the common positive proteins identified include ENO2, GLB1, ANGPTL1, ARHGAP25, MSR1, GRAP2, RNASET2, ST13, BDNF, IL10RB, IL1R1, TGFBR3, SELE, FCRL3, CCL19, DBNL, MAPK13, F2, IL1RN, C3, TIMP4, SERPINA12, CD72, and CXCL9. For seronegative RA, the common positive proteins include PCOLCE, SMOC1, CRYBB1, ST13, RBP5, TNFRSF1A, KLK8, CHIT1, MAPKAPK2, NSFL1C, F2, SERPINF1, MAN2B2, FKBP7, and DNAJA4. In juvenile RA, the common positive proteins identified are STX8, CD59, MATN3, SERPINA3, CLEC1B, AFM, CD55, and SLAMF6. When comparing the different types of RA, we found that ST13 and F2 are common positive proteins across RA, seropositive RA, and seronegative RA. Additionally, MSR1, GRAP2, CRYBB1, MAPKAPK2, NSFL1C, and IL1RN are co-positive proteins found in both RA and seropositive RA. After applying FDR correction, we identified several significant positive proteins in the MR analysis of plasma pQTLs related to RA in Iceland and the UK Biobank. Specifically, in the MR analysis for both populations, PPA2, JUND, AGER, F2, and PMEL emerged as significant positive proteins. Additionally, the following proteins were found to be significantly positive: AIF1, ARG2, ATP5IF1, CCL19, CDSN, CEP43, MXRA8, PADI2, RPA2, SLC16A1, TNF, and TNFRSF14. In the MR analysis focusing on plasma pQTLs and seropositive RA in Iceland, TGFBR3, FCGR3B, TIMP4, and PMEL were identified as significant positive proteins. For the UK Biobank's MR analysis of plasma pQTLs and seropositive RA, the proteins AIF1, APOBR, ATP6V1G2, BCL2L15, C1QTNF6, CCL19, CD40, CDSN, CEP43, CX3CL1, FCGR2B, FCRL1, IL6R, MXRA8, TGFBR3, TNF, and TNFRSF14 were significantly positive. Lastly, in the MR analysis of plasma pQTLs associated with seronegative RA in the UK Biobank, AIF1, CEP43, and TNF were identified as significant positive proteins. After applying the Bonferroni correction, AIF1, CCL19, CDSN, CEP43, and TNF emerged as significant positive proteins in the MR analysis of plasma pQTLs associated with RA in the UK Biobank. Furthermore, in the MR analysis of plasma pQTLs related to seropositive RA, AIF1, ATP6V1G2, BCL2L15, CCL19, CDSN, CEP43, IL6R, and TNF were identified as significantly positive proteins. In contrast, the MR analysis of plasma pQTLs concerning seronegative RA highlighted AIF1 and TNF as the only significantly positive proteins. Single-gene SMR analysis results In our analysis of the SMR concerning Icelandic plasma pQTLs and RA, we identified a total of 28 positively associated genes. These genes include ACADVL, AGER, AP2A2, ARPC1B, B3GALT6, CBL, CFL2, CHI3L2, CRAT, FCRL1, GHR, HIBCH, JUND, KLKB1, LRP4, NMB, OAF, PDE4A, PMM1, PPID, RELT, SERPINB1, ST13, TAGLN2, TCEA2, TNFAIP3, UROD, and USP8.In our analysis of plasma pQTLs and serum-positive RA in Iceland, we identified a total of 34 positive genes, which include ACADVL, AGER, AP2A2, C5, CD14, CD48, CD72, CHI3L2, CYTL1, FCRL3, GHR, HIBCH, IL15RA, ISOC1, LRP4, MDH1, OAF, PDE4A, PMM1, POMGNT2, PPID, PSMB4, QPCTL, RNASET2, ROBO1, SERPINB1, SH3BGRL2, ST13, TAGLN2, TCEA2, TNFAIP3, TPI1, ULK3, and ZG16B.In our SMR analysis of plasma pQTLs and seronegative RA in Iceland, we identified a total of 21 positive genes, including AP2A2, B3GNT8, C4BPA, CBL, CHI3L2, CRAT, DLK2, ERBB3, G3BP1, KLKB1, LRP4, MAPKAPK2, MTHFD1, NDE1, OAF, PPID, SARS2, SNUPN, ST13, TAGLN2, and TCEA2.In the SMR analysis of Icelandic plasma pQTLs associated with juvenile RA, we identified a total of 15 positively correlated genes: ABLIM3, BCL10, BPNT1, CCS, CD59, HEXIM2, JUND, PTPN7, SLAMF6, SPOCK2, STX8, TBCE, TNFSF8, TXNDC12, and UBE2F.In the SMR analysis of plasma pQTLs and RA within the UK Biobank, we identified a total of 38 positive genes, which include ACOT13, ADAM15, AIF1, APOBR, ATP6V1G2, ATXN2L, BCL2L15, CD6, CD72, CHCHD6, DPP4, EPHB6, FCGR2B, FCRL1, HCG22, HDGF, IL6R, LSP1, PADI2, PARK7, PFKFB2, RABEP1, RARRES2, RELT, RNASET2, RPA2, SEMA4D, SERPINB1, SLC16A1, ST13, STX4, TEF, TGFB2, TNF, TNFRSF4, TNFRSF14, UROD, and USP8.In the SMR analysis of plasma pQTLs and serum-positive RA within the UK Biobank, we identified a total of 37 positive genes, which include ADAM15, APOBR, ATP6V1G2, BCL2L15, CCL5, CD6, CD72, DOK2, DTNB, FCGR2B, FCRL1, FCRL2, FCRL3, FDX1, FKBPL, IL6R, IMMT, KIAA0319, LSP1, PADI2, PARK7, PBXIP1, PFKFB2, PMVK, RABEP1, RNASET2, RPA2, SH2B3, SLC16A1, SPAG1, ST13, STX4, TEF, TGFB2, TNF, TNFRSF14, and TOR1AIP1.In the SMR analysis of plasma pQTLs and seronegative RA within the UK Biobank, we identified a total of 21 positive genes: AIF1, APOBR, BAG3, BCL2L15, CASP10, CD300E, FCRL1, FCRL3, GZMB, LSP1, LY75, MAPKAPK2, PDCD1, PFKFB2, PKD1, RNASET2, SEMA4D, SERPINB1, SHISA5, ST13, and TNF.In the UK Biobank plasma pQTLs and SMR analysis of juvenile RA, we identified a total of 12 positively associated genes: AIF1, ASAH2, BCAM, CD59, COQ7, HS6ST1, PFKFB2, SLAMF6, SORBS1, SPINK8, STX4, and STX8.Appendix Table S5-8 presents the results of the single-gene SMR analysis of plasma proteins across four distinct types of RA, while Figures 10-13 illustrates the forest plot depicting the positive outcomes of the single-gene SMR analysis. Upon comparing the positive SMR results from the two datasets, we identified several common positive genes in RA, namely FCRL1, RELT, SERPINB1, ST13, UROD, and USP8. In the case of seropositive RA, CD72, FCRL3, RNASET2, and ST13 emerged as common positive genes across both datasets. For seronegative RA, MAPKAPK2 and ST13 were identified as common positive genes in both datasets. Lastly, in juvenile RA, CD59, SLAMF6, and STX8 were found to be common positive genes across the datasets. Bayesian colocalization analysis results In our previous MR analysis of Icelandic plasma pQTLs associated with RA and its three subtypes, we identified several positive genes through Bayesian colocalization analysis. Notably, F2 (PPH4 = 0.7678) was found to be significantly positively colocalized with RA. Additionally, FCGR3A (PPH4 = 0.9822) emerged as a significantly positively colocalized gene specifically in seropositive RA. Other genes, including AGER, ALDH2, C3, CCL19, DEF6, GRAMD1C, HIBCH, MLN, MTHFD1, PDE4A, and TNFSF14, exhibited moderate colocalization in RA. In the context of seropositive RA, AGER, CCL19, CCL22, F2, FCGR3B, HIBCH, IL15RA, LRP4, PDE4A, and TIMP4 were identified as moderately colocalized genes. Furthermore, ALDH2 and FCRLB were found to be moderately colocalized in seronegative RA. In the previous MR analysis of plasma pQTLs and RA within the UK Biobank, we identified several positive genes through Bayesian co-localization analysis. Notably, ATP5IF1 (PPH4 = 0.9298), CCL19 (PPH4 = 0.9762), CX3CL1 (PPH4 = 0.9463), HDGF (PPH4 = 0.8426), MXRA8 (PPH4 = 0.7889), and TNFRSF14 (PPH4 = 0.8016) emerged as significant positive co-localized genes associated with RA. Additionally, in the context of seropositive RA, we found ADAM15 (PPH4 = 0.9721), CCL19 (PPH4 = 0.9766), CX3CL1 (PPH4 = 0.9837), NFKBIE (PPH4 = 0.7643), and TNFRSF14 (PPH4 = 0.9552) to be significant positive co-localized genes. Furthermore, we identified a group of moderately co-localized genes in RA, which includes AIF1, ATP6V1G2, CCL21, CD72, CDSN, CEP43, COMMD1, CPTP, HCG22, ICAM3, MYDGF, PHLDB1, and TNF. In seropositive RA, the moderately co-localized genes are AIF1, ATP6V1G2, CCL21, CD72, CDSN, CEP43, F2, FCGR2B, FKBPL, ICAM3, KIAA0319, MXRA8, PHLDB1, SCGN, SH2B3, TIMP4, and TNF. In the seronegative category, AIF1, CDSN, CEP43, COMMD1, and TNF were identified as moderately co-localized genes. The results of the Bayesian co-localization analysis are presented in Appendix Tables S9-12, while Figures 14-27 illustrates the scatter plot of significant positive genes from the analysis. LDSC analysis results To assess the genetic correlates of complex diseases and intricate gene signatures, we conducted a Bayesian colocalization analysis of significantly associated genes with RA, including seropositive RA, seronegative RA, and juvenile RA. Linkage Disequilibrium Score Regression (LDSC) analysis was performed, revealing significant genetic correlations between CCL19 (h2_p=2.37E-07) and RA (h2_p=1.14E-05), with a notable positive genetic correlation (rg = 0.4553, rg_se = 0.1085, rg_p = 0.0000). Similarly, the genetic correlations between TNFRSF14 (h2_p=7.34E-14) and RA were also significant, indicating a positive genetic correlation (rg = 0.1546, rg_se = 0.0694, rg_p = 0.0258). Furthermore, the genetic correlations between CCL19 (h2_p=2.37E-07) and seropositive RA (h2_p=9.32E-05) were significant, demonstrating a strong positive genetic correlation (rg = 0.5660, rg_se = 0.1149, rg_p = 0.0000). The genetic correlations between TNFRSF14 (h2_p=7.34E-14) and seropositive RA (h2_p=9.32E-05) were also significant, with a positive correlation noted (rg = 0.1878, rg_se = 0.0734, rg_p = 0.0105). The results of the LDSC analysis are detailed in Appendix Table S13. Results of whole-phenome group association analysis We conducted a phenotype-wide association analysis between Bayesian co-localized significantly positive genes and the latest version 11 GWAS data released by the Finngen database. The results indicated that the F2 gene was associated with 172 diseases or phenotypes. Following FDR correction, we identified that the F2 gene is associated with venous, lymphatic, and lymph node diseases, including deep vein thrombosis of the lower extremities, pulmonary embolism, other forms of embolism and thrombosis, venous thromboembolism, non-infectious enteritis and colitis, RA, menorrhagia, and frequently occurring menstruation, which is significantly related to irregular menstruation. The ATP5IF1 gene was found to be associated with 203 diseases or phenotypes. After FDR correction, we determined that ATP5IF1 is strongly related to autoimmune diseases, hypertension, and RA. The CCL19 gene showed correlation with 195 diseases or phenotypes, and after FDR correction, we found that CCL19 is significantly correlated with RA and seropositive RA. The CX3CL1 gene was associated with 98 diseases or phenotypes, and after FDR correction, we observed that CX3CL1 is highly associated with systemic connective tissue diseases. Lastly, the HDGF gene was related to 120 diseases or phenotypes, and following FDR correction, we found that HDGF is significantly associated with cranial nerve diseases and hypertensive heart disease, among other conditions.The gene MXRA8 is associated with 230 diseases or phenotypes. Following false discovery rate (FDR) correction, we found that MXRA8 is significantly linked to allergic rhinitis, benign lipoma, Crohn's disease, thyroid poisoning with diffuse goiter, blepharoptosis, and various eyelid diseases. Additionally, it shows strong associations with hypothyroidism, chronic sinusitis, nasal polyps, inflammatory bowel disease, ulcerative colitis, polyarthritis, RA, and pollen allergies. Similarly, the gene TNFRSF14 is associated with 203 diseases or phenotypes. After FDR correction, we identified that TNFRSF14 is significantly related to eosinophilic asthma, autoimmune diseases, body mass index, follicular lymphoma, primary lymphatic and hematopoietic malignancies, nasal polyps, dermatitis, eczema, and RA. The gene FCGR3A is associated with 87 diseases or phenotypes, while ADAM15 is correlated with 126 diseases or phenotypes. After FDR correction, we found that ADAM15 is highly correlated with benign tumors of the parotid gland, benign tumors of the major salivary gland, benign salivary gland tumors, and Sjögren's syndrome. The gene NFKBIE is related to 121 diseases or phenotypes, and after FDR correction, it was found to be highly related to skin atrophic diseases and RA. Appendix Table S14 presents the MR-PheWas analysis results for significantly positive genes, and Figure 28 illustrates the Manhattan plot of the MR-PheWas analysis for these genes. Expression status of genes in different tissues We utilized the Human Protein Atlas to identify differences in the expression of significantly positive genes across human tissues. Previous studies have demonstrated that CCL19 is predominantly expressed in lymph nodes, with notable expression also observed in bone marrow, lymphoid tissues, tonsils, thymus, and adipose tissue. Conversely, TNFRSF14 is primarily expressed in the duodenum, while also showing significant expression in the small intestine, fallopian tube, lymph nodes, and liver. Figure 29 illustrates the chromosomal locations of the significantly positive genes identified in our analysis, whereas Figures 30 and 31 depict the tissue expression profiles of these genes. Protein-protein interaction network construction results We summarized the results of the SMR analysis and co-localization analysis of plasma proteins in relation to RA, including seropositive RA, seronegative RA, and juvenile RA, along with 153 drug target genes identified through STRING database analysis, to construct a PPI network. This network comprises 151 nodes and 289 edges representing the protein interaction pathways. The PPI network is illustrated in Appendix Table S15 and Figures 32-33. GO enrichment analysis and KEGG pathway analysis results In our investigation of the characteristics of 153 potential drug targets identified through SMR analysis and co-localization analysis concerning the association between plasma proteins and RA, we employed the GO framework for a systematic analysis. The results indicated that these targets are extensively distributed across 353 biological pathways and are primarily involved in several critical biological processes (BP). Notably, they were significantly associated with the positive regulation of cytokine production (GO:0001819), chemotaxis (GO:0006935), tropism (GO:0042330), and leukocyte migration (GO:0050900). Furthermore, at the molecular function (MF) level, these targets displayed specific pathways, including cytokine receptor binding (GO:0005126), cytokine activity (GO:0005125), and G protein-coupled receptor binding (GO:0001664), among others. In terms of cellular component (CC) localization, they are predominantly found on the exterior of the plasma membrane (GO:0009897), within membrane rafts (GO:0045121), and in membrane microdomains (GO:0098857). The results of the GO enrichment analysis are presented in Appendix Table S16 and Figure 34. To further elucidate the potential link between these drug targets and the treatment of RA, we conducted a KEGG pathway analysis. The results indicated that these targets were significantly enriched in specific core pathways, including cytokine-cytokine receptor interaction (hsa04060), viral protein interaction with cytokines and cytokine receptors (hsa04061), and tuberculosis (hsa05152). Appendix Table S17 and Figure 35 present the results of the KEGG pathway analysis, while Figure 36 illustrates the significantly correlated pathway diagrams derived from this analysis. Candidate drug prediction outcomes To predict potentially effective intervention drugs, we utilized DSigDB to generate a list of the top ten candidate drugs based on their P values and adjusted P values. The results indicated that methotrexate (CTD 00006299), Chongsulide (CTD 00005665), nitroglycerin (CTD 00006039), antimycin A (CTD 00005427), and fluoride (CTD 00005982) emerged as the five most significant drugs. Specifically, methotrexate showed a correlation with the genes IL15RA, ACADVL, CCL21, LSP1, F2, TNF, C3, C5, FCGR3B, CCL5, MAPKAPK2, CD48, CCL19, FCGR2B, KLKB1, PPID, and HIBCH. Chongsulide correlated with the genes C3, C5, TNF, and KLKB1; nitroglycerin was associated with the genes C3, ALDH2, CASP10, and TNF; antimycin A correlated with the genes CCL5, CD14, TNF, and IL6R; and fluoride was linked to the genes CCL21, TNFRSF14, TNFSF8, CCL19, and TNF. Appendix Table S18 presents the prediction results of drug candidates for significantly positive genes, while Appendix Table S19 displays the prediction results of drug candidates for RA. Molecular docking results In this study, we utilized positive genes identified through Bayesian co-localization as significantly positive target genes and employed AutoDock 4.2.6 software to analyze the top five candidate drugs. The candidate drugs derived from the significant positive gene analysis were associated with their respective binding sites and interactions between the proteins encoded by the corresponding genes. We calculated the binding energy for each interaction and successfully obtained effective docking results involving nine genes and drugs. Among all the docking results, the combination of ADAM15 with camptothecin exhibited the lowest binding energy value of -10.0 kcal/mol, while the pairing of TNFRSF14 with 7,12-dimethylbenzanthracene also demonstrated a binding energy value of -10.0 kcal/mol. Additionally, the combination of NFKBIE with Trichogenin showed a binding energy value of -8.8 kcal/mol, MXRA8 with 2,3,7,8-tetrachlorodibenzo-p-dioxin had a binding energy of -7.7 kcal/mol, and the combination of HDGF with troglitazone displayed a binding energy value of -7.5 kcal/mol. Furthermore, the pairing of F2 with hydrocortisone succinate resulted in a binding energy value of -7.2 kcal/mol, indicating the stability of its binding efficiency. Appendix Table S20 presents the binding energy scores from the molecular docking analysis, while Figures 37-58 illustrate the molecular docking diagrams corresponding to the higher binding energies. Discussion Rheumatoid arthritis (RA) is a chronic autoimmune disease that affects multiple joints in the body. In this condition, an abnormal immune response leads to inflammation of the joint synovium, resulting in joint swelling and pain. Over time, this inflammation may also damage the cartilage and bone, contributing to joint dysfunction. Despite the availability of various treatments, patients still face challenges related to individual differences, uncertainty regarding treatment efficacy, side effects, and economic burdens. Looking ahead, advancements in genomics and biomarker research are anticipated to yield more precise treatment options for patients with RA. Our in-depth analysis has identified several genes, including FCGR3A, ADAM15, CCL19, CX3CL1, NFKBIE, TNFRSF14, F2, ATP5IF1, HDGF, and MXRA8, as potential key targets for RA treatment. Notably, the genes TNFRSF14 and CCL19, due to their high genetic correlation, may play significant roles in the pathogenesis of RA and its subtypes, making them promising areas for future research and therapeutic focus. The relationship between TNFRSF14 and rheumatoid arthritis The gene encoding tumor necrosis factor receptor superfamily member 14 (TNFRSF14), also known as HVEM, encodes a protein that plays a crucial role in immune responses. This protein is essential for the activation and proliferation of lymphocytes. Studies have demonstrated that HVEM effectively regulates T cell immune responses by promoting inflammatory responses and transmitting inhibitory signals( 33 , 34 ). The function of HVEM extends beyond serving as a receptor for LIGHT (lymphotoxin-like molecule) and lymphotoxin-alpha; it also interacts with immunoglobulin superfamily members BTLA and CD160, highlighting its versatile role in immune regulation. HVEM can bind to multiple ligands in various conformations, forming complex signaling networks that jointly regulate inflammatory and inhibitory responses. Abnormal expression of TNFRSF14 has been observed in approximately 40% of follicular lymphoma (FL) patients. HVEM inhibits T cell activation by interacting with receptors on both B cells and T cells, which is crucial for maintaining immune balance and preventing immune overreactions( 35 ). Furthermore, the influence of HVEM extends beyond T cells and B cells to encompass a broader range of immune regulatory mechanisms. For instance, the interaction of HVEM with BTLA represents a novel link between immunoglobulin domains and TNFR family members, inhibiting T and B cell activation, proliferation, and cytokine production. As a transmembrane protein, HVEM contains multiple extracellular cysteine-rich domains (CRDs) that are essential for its function, enabling HVEM to bind to various ligands in different conformations and form functionally diverse and interconnected signaling pathways. These pathways collaboratively regulate inflammatory and inhibitory responses, thereby playing a pivotal role in the immune response.In summary, TNFRSF14 (HVEM) plays a crucial role in immune regulation. It participates in the regulation of T cell and B cell activation and proliferation, and also influences inflammatory responses and immunity through interactions with other immune molecules. These functions underscore the significant research value of TNFRSF14 in immunological studies and its potential clinical applications. RA is a disease characterized by autoimmune features. The abnormal activation of B cells and T cells plays a critical role in the susceptibility to and development of the disease( 36 ). A related study indicates that the role of B cells in RA extends beyond the production of autoantibodies; they also activate T cells through antigen presentation and are directly involved in the production of cytokines, which are integral to the inflammatory response. These processes are closely linked to the onset and progression of RA. Concurrently, T cells are also pivotal in RA, participating in its pathological processes by releasing pro-inflammatory cytokines or directly causing joint damage. The function of TNFRSF14 in regulating immune responses is likely related to the pathogenesis of RA, particularly in the activation of T and B cells and their immune responses. A study reveals that both HVEM and BTLA exhibit characteristic expression and distribution in the synovium of RA, suggesting that their signaling may influence the disease's pathogenesis( 36 ). Furthermore, a bioinformatics study identified TNFRSF14-MMEL1 at the 1p36 locus and IKZF3-ORMDL3-GSDMB at the 17q12 locus as genes most likely associated with RA( 37 ). Genetic case-control studies also indicate that MMEL1/TNFRSF14 is significant in genetic susceptibility to RA( 38 ). These results underscore the importance of TNFRSF14 in RA. Overall, these findings contribute valuable insights into the immunological basis of RA and may inform future therapeutic strategies targeting these immune cells and their interactions. We found that 7,12-dimethylbenzo[a]anthracene (DMBA) exhibits a strong binding affinity for TNFRSF14. As a potent carcinogen, DMBA not only directly damages genes but also regulates cellular proliferation, thereby promoting tumor growth. This process involves complex immune responses, including the proliferation of regulatory T cells, humoral immune responses, and cellular immune responses. One study demonstrated that DMBA can induce an increase in regulatory T cells. As the dosage of DMBA increases, the immune response weakens, and the proliferation of Treg cells leads to the establishment of an immunosuppressive state( 39 ). Furthermore, DMBA has been shown to inhibit both cellular and humoral immunity, as confirmed in mouse models. In terms of humoral immunity, DMBA significantly reduces antibody production; experiments indicated that DMBA-treated mice produced considerably fewer antibodies than their normal counterparts following vaccination( 40 , 41 ). Regarding cellular immunity, the cytotoxic T cell function in DMBA-treated mice was markedly diminished, with abnormal immune cell activation and reduced cytokine secretion, indicating a direct toxic effect of DMBA on immune cells. The immunosuppressive effect of DMBA correlates with its dosage; as the dose increases, the inhibitory effects on T cell proliferation, antibody production, and cytotoxic T cell function also intensify, demonstrating a dose-dependent relationship. Future research should further investigate the impact of DMBA on the body's immune mechanisms and provide new strategies for the prevention and treatment of related diseases. The relationship between CCL19 and rheumatoid arthritis CCL19, also known as macrophage inflammatory protein-3β (MIP-3β), is a member of the CC chemokine family and plays a significant role in immune regulation and various biological processes. This factor is crucial for the regulation and tissue localization of immune cells, particularly in maintaining the structure and function of lymphoid tissue. Research indicates that lymph node stromal cells (LN SCs) shape the microenvironment of lymph nodes by establishing a concentration gradient of CCL19 and CCL21, thereby guiding immune cells to migrate to specific areas( 42 ). Furthermore, additional studies have found that during immune responses, the expression of CCL19 is closely associated with the activity of specific CD4 + T cell subsets, particularly CD62Llow CD4 + T cells( 43 ). The involvement of CCL19 in numerous physiological and pathological processes positions it as a focal point for clinical research and the development of diagnostic tools. Monitoring changes in CCL19 levels can serve not only to assess the activity of immune-related diseases, such as autoimmune disorders and lymphoid tumors, but also to evaluate and monitor treatment responses and disease progression. In summary, CCL19, as a key molecule in immune regulation and signaling, plays a multifaceted role in immune response, anti-tumor defense, and central nervous system function. A deeper understanding of CCL19 and its associated signaling pathways reveals its complex biological mechanisms and extensive physiological effects, underscoring its significant role in immune diseases. RA, an autoimmune disease, is associated with CCL19, a finding supported by multiple studies. CCL19, a B-cell chemokine, correlates with a decrease in memory B cells in the blood of RA patients and influences clinical responses to rituximab treatment( 44 ). It significantly increases the expression of IL-1β and TNF-α in synoviocytes, while also promoting IL-1β expression in peripheral blood mononuclear cells (PBMCs)( 45 ). These cytokines, recognized as important pro-inflammatory mediators, activate a variety of immune cells, including synovial cells and macrophages, thereby triggering joint inflammation and tissue damage, which contributes to the onset and progression of RA. The interaction between CCL19 and its receptor CCR7 enhances its biological effects, potentially by promoting IL-1β expression and subsequently upregulating CCR7 function through IL-1β. The study also indicates that CCL19 regulates B cell recruitment in the RA synovium and is closely linked to joint and synovial destruction( 46 ). In the early stages of RA, CCL19 expression is stimulated by TNF-α and LTα1β2, resulting in significant alterations in the lymph node microenvironment( 47 ). Compared to osteoarthritis, CCL19 is expressed at higher levels in RA patients and plays a crucial role in promoting osteoclast migration and resorption( 48 ). Clinical studies have demonstrated that CCL19 levels serve as disease markers and prognostic indicators of RA. ELISA testing reveals that CCL19 is highly expressed in RA patients, and after treatment with disease-modifying antirheumatic drugs (DMARDs), CCL19 levels significantly decrease( 49 ). In summary, the elevated expression of CCL19 in RA, its interaction with inflammatory factors, and its correlation with clinical indicators underscore its key role in the pathogenesis of RA. These findings offer new insights into immunological research and potential treatment strategies for RA. Nitropyrene (1-NITROPYRENE) is a polycyclic aromatic hydrocarbon known for its strong carcinogenic and mutagenic properties. In response to external stimuli, the body's macrophages, natural killer cells, and various other immune cells play crucial roles. Our findings indicate that CCL19 exhibits a high binding energy with nitropyrene. Furthermore, 1-Nitropyridine may exert immune effects by engaging in a series of cellular pathways. Previous studies have demonstrated that 1-Nitropyridine can significantly induce the expression of Cyp1a1 protein, which is vital for metabolic processes( 50 ). This induction may substantially impact immune cell function and the inflammatory response. Additionally, nitropyridines are likely to play significant roles in inflammation, infection, and injury responses( 51 ). 1-Nitropyridine may trigger chronic inflammatory reactions and influence immune function, which is closely associated with the development of chronic inflammation, autoimmune diseases, cancers, and other health issues. It may contribute to disease progression by promoting the release of inflammatory factors and exacerbating the immune system's overreaction. Moreover, the study found that nitropyrene is linked to cellular senescence, as mice exposed to nitropyrene exhibited increased telomere damage and cell senescence in lung and alveolar epithelial cells( 52 ). In summary, the relationship between nitropyridine and the immune system is complex, involving various aspects of both innate and adaptive immunity. It significantly affects the immune system by modulating macrophage function, inducing specific immune responses, and regulating intracellular signaling pathways. This study, while conducting rigorous data analysis and utilizing a series of GWAS data, inevitably has some limitations.This study primarily focuses on individuals of European ancestry; therefore, the applicability of its conclusions to other ethnic groups requires further in-depth analysis. This necessity arises from the differences in genetic structure, living environments, and other factors among various ethnic groups, which may influence the generalizability of the research findings. Additionally, during the initial data collection and sorting phase, the meta-analysis of differentially expressed genes in plasma proteins may utilize various data sources, including microarrays and batch RNA sequencing, each with differing sample sizes. Such variability can lead to discrepancies in results, as the differing data sources may employ distinct collection and analysis methods, potentially interfering with the accurate identification of differentially expressed genes. Furthermore, the expression of quantitative trait loci is subject to change as the disease progresses, influenced by variations in cell types. Expression quantitative trait loci derived from bulk RNA sequencing face significant limitations in elucidating key molecular mechanisms associated with diseases. Specifically, there are notable differences in the mechanisms of plasma protein expression in vivo versus in vitro; thus, in vitro data cannot be assumed to comprehensively represent all plasma protein functions. Additionally, the alterations in various cell types throughout disease progression may affect the identification of critical molecular mechanisms. Moreover, limited sample sizes and insufficiently balanced groupings may introduce bias. While high thresholds and multiple corrections strengthen the rigor of the analysis, they may also obscure true associations that lack statistical significance in smaller samples.Finally, minor effects of genetic variation may diminish statistical power and heighten the risk of false positives. Furthermore, the pathogenesis of the disease is intricate, involving genetics, environmental factors, and numerous unknown elements. There remains a critical need for large-scale, multi-center, and well-designed studies to address these gaps. Only through such research can we accurately elucidate the relationships among various factors and diseases, thereby providing a robust scientific foundation for prevention and treatment strategies. Conclusion Through a series of analyses, including MR, SMR analysis, co-localization analysis, and linkage disequilibrium score regression (LDSC), this study identified significant correlations between F2, ATP5IF1, CCL19, CX3CL1, HDGF, MXRA8, TNFRSF14, and the onset and progression of RA. Additionally, FCGR3A, ADAM15, CCL19, CX3CL1, NFKBIE, and TNFRSF14 demonstrated significant associations with the onset and development of seropositive RA. Notably, CCL19 and TNFRSF14 emerged as positively correlated genes of genetic significance. Declarations Ethics approval and consent to participate For this study, all our GWAS data were derived from published statistical sources, thus negating the need for additional ethical approval. Consent for publication All authors unanimously agreed to submit this research achievement for publication. Clinical trial number Not applicable. Availability of data and material All types of data involved in this study have clear sources for easy access. The complete GWAS information for UK Biobank plasma pQTLs can be downloaded directly from s3://ukbiobank.opendata.sagebase.org/. The GWAS data for Icelandic plasma proteins is available in the literature titled "Large-scale integration of the plasma proteome with genetics and disease," which can be accessed at https://www.nature.com/articles/s41588-021-00978-w#Sec36. Complete GWAS data for RA, including seropositive and seronegative RA, as well as data for conditions such as ulcerative colitis, AS, MS, and PsA, can be downloaded from https://www.finngen.fi/en. Additionally, the complete GWAS data for juvenile RA can be obtained from https://www.nature.com/articles/s41588-021-00931-x. Other relevant data can be sourced from original literature and respective websites. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding Not applicable. Authors' contributions W.Y. contributed to the conception and design of this study. Z.H.L. and M.C. provided the conceptualization ideas and analysis and supervised the whole research process. W.Y. conducted the data analysis. W.Y. authored the first draft, then W.Y., C.L.L. and M.C. revised and refined the manuscript. W.Y. and M.C. carried out the visualization analysis of the research results. All authors reviewed and approved the final manuscript. Acknowledgements We would like to express our gratitude to the European Bioinformatics Center, the GWAS Catalog database, the FinnGen database, the British Biobank database, the IEU OpenGWAS database, the eqtlgen database, the GTEx database, the YANG LAB database, the Human Protein Atlas database, the STRING database, and the DSigDB database for providing shared data. We also extend our thanks to all data providers mentioned in this article and to the anonymous reviewers for their constructive comments. Authors' information Wei Yang correspondence address：College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130117, Jilin, China. Email： [email protected] Chenlin Liu correspondence address：College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130117, Jilin, China. Email： [email protected] *Zhenhua Li2 correspondence address：The Affiliated Hospital of Changchun University of Chinese Medicine, Changchun 130117, Jilin, China. 3.School of Traditional Chinese Medicine, Email： [email protected] *Miao Cui correspondence address：School of Traditional Chinese Medicine, Capital Medical University, No.10, Xitoutiao, You'anmenwai, Fengtai District, 100069, Beijing Beijing, China. Email： [email protected] References Waldner H. The role of innate immune responses in autoimmune disease development. Autoimmun Rev. 2009;8(5):400-4. https://dx.doi.org/10.1016/j.autrev.2008.12.019 Cross M, Smith E, Hoy D, Carmona L, Wolfe F, Vos T, et al. The global burden of rheumatoid arthritis: estimates from the global burden of disease 2010 study. Ann Rheum Dis. 2014;73(7):1316-22. https://dx.doi.org/10.1136/annrheumdis-2013-204627 Myasoedova E, Crowson CS, Kremers HM, Therneau TM, Gabriel SE. 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Int Immunopharmacol. 2024;142(Pt A):113056. https://dx.doi.org/10.1016/j.intimp.2024.113056 Kurreeman FA, Stahl EA, Okada Y, Liao K, Diogo D, Raychaudhuri S, et al. Use of a multiethnic approach to identify rheumatoid- arthritis-susceptibility loci, 1p36 and 17q12. Am J Hum Genet. 2012;90(3):524-32. https://dx.doi.org/10.1016/j.ajhg.2012.01.010 Li Y, Begovich AB. Unraveling the genetics of complex diseases: susceptibility genes for rheumatoid arthritis and psoriasis. Semin Immunol. 2009;21(6):318-27. https://dx.doi.org/10.1016/j.smim.2009.04.002 Nasti TH, Yusuf N, Sherwani MA, Athar M, Timares L, Elmets CA. Regulatory T Cells Play an Important Role in the Prevention of Murine Melanocytic Nevi and Melanomas. Cancer Prev Res (Phila). 2021;14(2):165-74. https://dx.doi.org/10.1158/1940-6207.Capr-20-0360 Gao J, Lauer FT, Dunaway S, Burchiel SW. Cytochrome P450 1B1 is required for 7,12-dimethylbenz(a)-anthracene (DMBA) induced spleen cell immunotoxicity. Toxicol Sci. 2005;86(1):68-74. https://dx.doi.org/10.1093/toxsci/kfi176 Ladics GS, Kawabata TT, White KL, Jr. Suppression of the in vitro humoral immune response of mouse splenocytes by 7,12-dimethylbenz[a]anthracene metabolites and inhibition of immunosuppression by alpha-naphthoflavone. Toxicol Appl Pharmacol. 1991;110(1):31-44. https://dx.doi.org/10.1016/0041-008x(91)90287-o Saxena V, Li L, Paluskievicz C, Kasinath V, Bean A, Abdi R, et al. Role of lymph node stroma and microenvironment in T cell tolerance. Immunol Rev. 2019;292(1):9-23. https://dx.doi.org/10.1111/imr.12799 Kagamu H, Kitano S, Yamaguchi O, Yoshimura K, Horimoto K, Kitazawa M, et al. CD4(+) T-cell Immunity in the Peripheral Blood Correlates with Response to Anti-PD-1 Therapy. Cancer Immunol Res. 2020;8(3):334-44. https://dx.doi.org/10.1158/2326-6066.Cir-19-0574 Sellam J, Rouanet S, Hendel-Chavez H, Miceli-Richard C, Combe B, Sibilia J, et al. 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Arthritis Res Ther. 2018;20(1):35. https://dx.doi.org/10.1186/s13075-018-1529-8 Lee J, Park C, Kim HJ, Lee YD, Lee ZH, Song YW, et al. Stimulation of osteoclast migration and bone resorption by C-C chemokine ligands 19 and 21. Exp Mol Med. 2017;49(7):e358. https://dx.doi.org/10.1038/emm.2017.100 Shi LJ, Li JH, Hu FL, Li M, Zhang J, Li JT, et al. [Clinical significance of serum C-C chemokine ligand 19 levels in patients with rheumatoid arthritis]. Beijing Da Xue Xue Bao Yi Xue Ban. 2016;48(4):667-71. Chu WC, Hong WF, Huang MC, Chen FY, Lin SC, Liao PJ, et al. 1-Nitropyrene stabilizes the mRNA of cytochrome P450 1a1, a carcinogen-metabolizing enzyme, via the Akt pathway. Chem Res Toxicol. 2009;22(12):1938-47. https://dx.doi.org/10.1021/tx900241g Tsai PK, Chen SP, Huang-Liu R, Chen CJ, Chen WY, Ng YY, et al. Proinflammatory Responses of 1-Nitropyrene against RAW264.7 Macrophages through Akt Phosphorylation and NF-κB Pathways. Toxics. 2021;9(11). https://dx.doi.org/10.3390/toxics9110276 Li SR, Kang NN, Wang RR, Li MD, Chen LH, Zhou P, et al. ALKBH5 SUMOylation-mediated FBXW7 m6A modification regulates alveolar cells senescence during 1-nitropyrene-induced pulmonary fibrosis. J Hazard Mater. 2024;468:133704. https://dx.doi.org/10.1016/j.jhazmat.2024.133704 Figures Figures 1 to 58 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.xlsx Figures.pptx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5510112","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":428407798,"identity":"6b1f8339-df37-4a41-b70f-5298aa296acc","order_by":0,"name":"Wei Yang","email":"","orcid":"","institution":"Changchun University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Yang","suffix":""},{"id":428407799,"identity":"3d2cd8b4-d712-445f-8f06-3f5c6e68c355","order_by":1,"name":"chenlin liu","email":"","orcid":"","institution":"Changchun University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"chenlin","middleName":"","lastName":"liu","suffix":""},{"id":428407800,"identity":"3ee8dba8-2783-44cd-8df7-8a6136810680","order_by":2,"name":"Zhenhua Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACNvb2gw8SDP7L2R9vbHyQUFFDWAsfz5lkgw8VzMYMZw4fNnhw5hhhLXISCWaSM84wJzbcSEuTfNjCTITDJBLSpHnb2IwZZ+SYVSQ2sDHwt3cn4NfC8/CwNW8bjxwzzxuzG4k7ZBgkzpzdgF8Le0Libd42CWM29hygljNsDAYSuQS0MCQYAB1mkNjDkGNWkNjGTIQWjgQjoPcTEmdwpKUxEKcFEsgHjA14Dh+WSDhzjIegX+TbwVF5QM6AvbHx44+KGjn+9l78WjAAD2nKR8EoGAWjYBRgBQA8HkwErTrY3gAAAABJRU5ErkJggg==","orcid":"","institution":"Changchun University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Zhenhua","middleName":"","lastName":"Li","suffix":""},{"id":428407801,"identity":"5fb41d72-1d07-4341-9928-9c20da76dc6a","order_by":3,"name":"Miao Cui","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Miao","middleName":"","lastName":"Cui","suffix":""}],"badges":[],"createdAt":"2024-11-23 13:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5510112/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5510112/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78501827,"identity":"7948c1fe-6745-4e30-9b01-a17f9dcf87d0","added_by":"auto","created_at":"2025-03-14 06:52:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":930209,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5510112/v1/846c4c70-9bf0-4db6-869d-68040b79d245.pdf"},{"id":78501142,"identity":"2dda256b-6661-43e3-a377-0247ce1699ae","added_by":"auto","created_at":"2025-03-14 06:36:29","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12211302,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5510112/v1/3e66029cbf4bcaac81460154.xlsx"},{"id":78501170,"identity":"e026a07d-0d9b-4de1-a4e3-ae04438d3ebc","added_by":"auto","created_at":"2025-03-14 06:36:32","extension":"pptx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":197183640,"visible":true,"origin":"","legend":"","description":"","filename":"Figures.pptx","url":"https://assets-eu.researchsquare.com/files/rs-5510112/v1/8fc2defc3a0553e32f91e93e.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eIntegrative Analysis of Genetic, Proteomic, and Transcriptomic Data Reveals Novel Therapeutic Targets for Rheumatoid Arthritis\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eRheumatoid arthritis (RA) is a prevalent autoimmune disease characterized by progressive joint damage and various extra-articular manifestations that can lead to permanent disability and often affect multiple organs throughout the body. The United States Coordinating Committee on Autoimmune Diseases reported in 2009 that approximately 20\u0026nbsp;million Americans suffer from autoimmune diseases(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). According to the 2010 Global Burden of Disease (GBD) data, the global prevalence of RA is approximately 0.24%(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), with the highest incidence rates observed in the United States and Nordic countries, ranging from 0.5\u0026ndash;1%(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The 2017 GBD data indicates an increase in global prevalence to 0.27%. Notably, North America exhibits the highest prevalence of RA at 0.38%, followed closely by Western Europe at 0.35%(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Additionally, World Health Organization (WHO) data from 2019 reports that 18\u0026nbsp;million people worldwide were affected by RA, with women being the predominantly affected demographic(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The variations in incidence across different countries, along with evidence of population migration, suggest that the pathogenesis of RA is highly complex and may involve multiple factors, including genetics, diet, immunity, and environmental influences.\u003c/p\u003e \u003cp\u003eThe treatment and prevention of RA represents a complex medical challenge. Although the introduction of novel therapies, including tumor necrosis factor (TNF) antagonists, interleukin-17 inhibitors, interleukin-1 antagonists, B cell-depleting agents, and other biological agents, has been shown to alleviate symptoms and enhance functional outcomes, significant obstacles remain in the management of this condition. One major concern is the side effects associated with these medications, which can include increased neurological complications(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), a heightened risk of infections(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), and potential damage to liver and kidney functions(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Furthermore, prolonged use of immunosuppressants can compromise the immune system(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Another challenge is the variability in treatment response(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), some patients experience limited symptom relief, and their conditions may even deteriorate. This variability is influenced by individual differences, disease heterogeneity, and other factors. To improve the efficacy of RA treatment, it is essential to continue exploring new therapeutic targets. Genome-wide association studies (GWAS) can identify single nucleotide polymorphisms (SNPs) linked to RA risk. However, the findings from GWAS must be integrated with comprehensive analyses to pinpoint causative genes and facilitate drug development. Without this integration, it will be challenging to accurately identify disease-causing genes or streamline the drug development process. This difficulty arises because the associated loci identified through GWAS may reside in intergenic regions, or their mechanisms of action on gene expression and function remain poorly understood. Therefore, further in-depth research is necessary to clarify the intrinsic relationship between these loci and the pathogenesis of RA.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a methodological approach that employs genetic variations as instrumental variables (IVs) to elucidate causal relationships between exposure factors and disease outcomes(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Recent technological advancements, particularly in aptamer and immunoassay platforms such as SomaScan and Olink, are enhancing MR analyses by facilitating the integration of genome-wide association study (GWAS) data with pooled results from pQTL studies, thereby revealing novel therapeutic targets. This approach holds significant promise, especially as protein - Quantitative Trait Loci (pQTLs) situated within drug-action gene regions are considered surrogate biomarkers, effectively reflecting gene expression levels during prolonged exposure states. In this context, we undertook a comprehensive, genome-wide druggability-based MR study aimed at identifying potential therapeutic strategies for RA. The study commenced with the collection of data on druggable genes, followed by the screening of genes associated with blood pQTLs. We subsequently analyzed the GWAS data for these genes in relation to RA using a two-sample MR analysis approach to pinpoint genes with strong associations to the disease. To bolster the robustness of our findings and further investigate the relationships between potential therapeutic targets and their phenotypes, we conducted single-gene Mendelian randomization studies (SMR) and Bayesian colocalization analyses. Additionally, we employed the LDSC analysis method to explore the genetic correlation between significant genes and complex diseases, performed phenotype-wide association (pheWAS) studies on noteworthy genes, and examined their expression across various tissues.We slao constructed a protein-protein interaction network for significant genes, conducted Gene Ontology (GO) enrichment analysis, and performed KEGG pathway analysis, along with drug prediction and molecular docking simulations. This comprehensive series of analyses offers valuable insights and guidance for the development of more efficient and targeted treatments for RA, which is anticipated to advance progress in this field.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eResearch design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 illustrates the analytical process employed in this study. We established an analytical framework that began with the integration of a plasma proteome-wide association study (PWAS) to derive data from two extensive plasma protein quantitative trait loci (pQTL) datasets. This was achieved through a two-sample Mendelian randomization (MR) analysis to identify potential positive proteins associated with RA. Subsequently, we conducted sensitivity analyses and reproducibility tests to thoroughly investigate the functional roles of these candidate plasma protein targets in the context of RA. We validated the identified coding gene loci shared by plasma proteins and RA through single-gene Mendelian randomization (SMR) analysis and Bayesian colocalization analysis. Concurrently, we employed the linkage disequilibrium score regression (LDSC) method to explore the genetic correlations between the identified positive genes and RA. For the significant positive genes identified, we performed a phenotype-wide association study (PheWAS) utilizing all genome-wide association studies (GWAS) from the latest version 11 data of the FinnGen database, examining their expression across various tissues to comprehensively investigate their potential functions. Additionally, we explored the underlying biological mechanisms of the putative protein targets through PPI analysis, Gene Ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. We also conducted drug prediction and molecular docking studies on the positive genes, providing a scientific basis for the development of more effective and targeted therapeutic drugs. Finally, we classified and evaluated the evidence from this study based on the results of the MR, SMR, colocalization analysis, and LDSC analysis, integrating findings from previous studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sources for plasma proteomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn a comprehensive study, Egil Ferkingstad and colleagues utilized the SomaScan platform to investigate a cohort of 35,559 Icelanders, identifying 28,191 genetic associations with 4,907 proteins, all of which fell below their predetermined significance threshold(13). The majority of data for this research was sourced from the Icelandic Cancer Project (ICP) and the genetics program at deCODE Genetics in Reykjavik, contributing 52% and 48% of the total participants, respectively. The researchers employed a recursive conditional analysis method to pinpoint the most significant variation within each genomic region (\u0026plusmn;1Mb), designating this as the primary indicator of the plasma protein quantitative trait locus (n = 18,084), while other variations were categorized as secondary indicators (n = 10,107). The study yielded impressive results, successfully replicating findings from previous research, including an 83% replication rate of the SomaScan-based plasma protein quantitative trait loci (pQTL) in the INTERVAL study and a 64% replication of the Olink-based plasma protein quantitative trait loci (pQTL) in the SCALLOP consortium. Detailed genome-wide association study (GWAS) data have been made publicly available at (https://www.nature.com/articles/s41588-021-00978-w#Sec36). It is important to note that all participants in this study were of European ancestry.\u003c/p\u003e\n\u003cp\u003eIn October 2023, the UK Biobank released a genome-wide association study (GWAS) dataset focused on plasma proteins(14). The research team utilized the Olink platform to analyze 2,923 proteins as part of the UK Biobank Pharmaceutical Proteomics Project (UKB-PPP), resulting in the identification of 23,588 preliminary genetic associations. All P-values were below the significance threshold, and these associations were located within a \u0026plusmn;1 Mb interval, with linkage disequilibrium (LD) r\u0026sup2; values of less than 0.8. The study successfully validated 84% of the known protein quantitative trait loci (pQTL) identified in the antibody study, as well as 38% of the pQTL found in the aptamer study. The complete GWAS dataset is accessible via the following link: s3://ukbiobank.opendata.sagebase.org/. It is important to note that all participants in this study were of European ancestry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sources for rheumatoid arthritis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GWAS data for RA originates from the 11th version of the GWAS dataset released in the FinnGen database on June 24, 2024. This dataset includes data on 453,626 individuals, comprising 4,589 patients with RA and 449,037 healthy controls. Additionally, the GWAS data for seropositive RA encompasses 448,721 individuals, which includes 5,426 cases of seropositive RA and 443,295 healthy controls. Furthermore, the GWAS data for seronegative RA involves 453,733 individuals, consisting of 7,314 seropositive RA patients and 446,419 healthy controls. The complete GWAS dataset can be accessed and downloaded via the following link: [FinnGen Database](https://www.finngen.fi/en). The GWAS data for juvenile RA is derived from the genetic association map released on September 30, 2021, which covers 409,217 individuals, including 216 juvenile RA patients and 409,001 healthy controls(15). This complete dataset is also available for download at [Nature](https://www.nature.com/articles/s41588-021-00931-x). All samples included in this study are of European ancestry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMendelian randomization (MR) analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMR analysis is predicated on three fundamental assumptions that ensure the validity of instrumental variables in causal inference(16). First, the exposure factor must be directly related to the genetic variation. Second, the genetic variation must not be associated with any confounding factors that could obscure the relationship between the exposure and the outcome. Third, the effect of the genetic variation on the outcome should be transmitted solely through the exposure factor. This study utilized the \u0026quot;TwoSampleMR\u0026quot; software package (version 0.6.8) in R to conduct the MR analysis. We employed pQTLs from the pharmacogenome as exposure data, establishing a significance threshold at P \u0026lt; 5\u0026times;10^-8. The significance threshold for both correlations was determined based on a P value of less than 0.05 for SNPs located within \u0026plusmn;10,000 kb of the transcription start site (TSS) of each gene(17). SNPs were screened using a linkage disequilibrium coefficient (r\u0026sup2;) criterion of less than 0.001, based on European samples from the 1000 Genomes Project. Phenotypes associated with the IVs were identified using the R package \u0026quot;phenoscanner\u0026quot; (version 1.0). We excluded SNPs that were directly associated with RA, as well as SNPs for traits directly linked to the disease. The screened SNP data were harmonized and subsequently subjected to MR analysis. For analyses involving a single SNP, we employed the Wald ratio method(18); in cases where multiple SNPs were available, we applied the random effects inverse variance weighting (IVW) method(19). We assessed heterogeneity among the individual causal effects of SNPs using Cochran\u0026apos;s Q test and evaluated pleiotropy through the MR Egger intercept. Finally, we conducted false discovery rate (FDR) and Bonferroni corrections on the resulting P values, with a significance threshold set at 0.05.To control for linkage disequilibrium, we clustered cis-pQTLs using the \u0026quot;clump_data\u0026quot; function with parameters set to clump_kb = 10,000 and clump_r2 = 0.001. To assess the potential for weak instrumental variable bias, we calculated the F statistic, which quantifies the strength of the instrumental variable. The formula employed for this calculation is: F = R\u0026sup2;(NK-1)/[K(1-R\u0026sup2;)], where R\u0026sup2; represents the estimated exposure variance explained by the instrumental variable, N denotes the sample size, and K indicates the number of IVs. If the F statistic is found to be less than 10, the corresponding SNP is classified as a weak instrumental variable and is subsequently excluded from the analysis to mitigate bias arising from weak instruments(20, 21).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-gene SMR analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the process of validating plasma protein targets, we employed the SMR(22) and HEIDI methods(23) developed by Yang Jian\u0026apos;s research group at West Lake University. These methodologies were utilized to assess the relationship between the expression of relevant protein-coding genes in pQTLGen blood samples and the risk of RA. The SMR analysis identified a single pQTL SNP that exhibited a strong correlation with the target gene region, which was utilized as an instrumental variable. The default P value for selecting the most correlated pQTL was established at 5 \u0026times; 10⁻⁸. Furthermore, the SMR tool integrates the Heterogeneity in Dependency Tool (HEIDI) test to determine whether the association between gene expression and the outcome arises from linkage, rather than from the influence of SNPs on disease through the regulation of gene expression. A P value of less than 0.01 in the HEIDI test suggests that the association is attributable to linkage. Subsequently, we utilized the merged cis-eQTL and mQTL summary data as IVs to re-conduct the primary analysis. The main outcome of the study is presented as the change in disease odds ratio (OR) for each standard deviation (SD) increase in gene expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBayesian colocalization analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to investigate whether multiple genetic associations indicate a single causal variant located within the same genomic region. To achieve this objective, we employed colocalization analysis to assess whether the associations between positive proteins and gene loci identified through MR analysis are attributable to the same causal variant in the context of RA. This analysis utilizes a Bayesian model to infer the relationships among traits by calculating the posterior probabilities (PPH) associated with five hypotheses(24): (1) H0: There is no association between RA and any trait; (2) H1: RA is solely related to trait 1; (3) H2: RA is solely related to trait 2; (4) H3: RA is associated with both traits, but the associations arise from different causal variants; (5) H4: RA and the two traits are related and stem from the same causal variant. For the analysis, we implemented the \u0026quot;coloc.abf\u0026quot; algorithm with its default parameter settings. Specifically, the prior probability p1 that the SNP is associated with trait 1 was set to 1\u0026times;10⁻⁴, the prior probability p2 that the SNP is associated with trait 2 was also set to 1\u0026times;10⁻⁴, and the prior probability p12 for the correlation between both traits was set to 1\u0026times;10⁻⁵. Our criteria for analysis are as follows: if PPH4 (the posterior probability of hypothesis H4) exceeds 0.75, we classify the association between plasma proteins and RA, as well as its subtypes, as significant colocalization; if PPH4 exceeds 0.5 or if the combined relationship of PPH3 and PPH4 exceeds 0.7, we consider moderate colocalization to be present.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLDSC analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we employed the Linkage Disequilibrium Score Regression (LDSC) analysis method to investigate the potential genetic association between positive genes and RA(25). The LDSC tool leverages genetic linkage disequilibrium (LD) to evaluate the strength of association with complex traits by estimating the LD score for each single nucleotide polymorphism (SNP). The scores range from -1 to 1, where -1 represents a complete negative genetic association and 1 denotes a complete positive genetic association. Furthermore, we performed a comprehensive analysis of the interaction between test statistics and linkage disequilibrium using the LDSC method to assess the potential influence of polygenic effects or biases on the statistical outcomes. Notably, LDSC effectively mitigates the issue of sample overlap during data processing, thereby enabling accurate and thorough evaluations of genetic correlations using genome-wide association study (GWAS) data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhenogroup-wide association analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe identified SNPs associated with significant druggable genes in blood samples through Bayesian colocalization analysis and compared these findings with the latest version 11 data from the FinnGen database (https://www.finngen.fi/en). Subsequently, we initiated a PheWAS to investigate the associations between positive druggable gene-related SNP variants and a diverse array of phenotypes(26).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExpression status of genes in different tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInvestigating the expression levels of positively regulated genes in specific human tissues may elucidate their potential mechanisms as therapeutic targets for RA. To achieve this, we employed the Human Protein Atlas (https://www.proteinatlas.org), a comprehensive resource that offers detailed insights into the mRNA and protein levels of genes across human tissues(27). The atlas includes protein expression data from 44 normal human tissues, encompassing 76% of human genes, which amounts to a total of 15,323 genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein-protein interaction network construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo visualize the interactions of genetically encoded proteins that may be significant for drug development, we utilized the PPI Networks tool. We selected the STRING database (https://string-db.org/) as our data source and established a confidence score threshold of 0.4 to filter valid interactions(28). Throughout this process, we maintained the default parameters of the STRING database to ensure the consistency and reliability of the analytical results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO enrichment analysis and KEGG pathway analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo enhance our understanding of the functional properties and biological connections of the pre-screened potential drug target genes, we employed the \u0026quot;clusterProfiler\u0026quot; package (version 4.12.6) in R software to conduct GO(29) and KEGG enrichment analyses(30). Our GO analysis encompasses three fundamental dimensions: biological process (BP), molecular function (MF), and cellular composition (CC). The objective is to elucidate the activity patterns and functional roles of these genes within biological systems and the specific cellular environments they inhabit. Furthermore, KEGG pathway analysis offers detailed insights into the metabolic pathways and signaling networks associated with these genes, which is essential for a comprehensive understanding of their biological roles and potential mechanisms of drug action. Through these analyses, we systematically delineated the functional networks of these genes within cells, thereby providing a robust biological foundation for drug development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrug candidate prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DSigDB (http://dsigdb.tanlab.org/DSigDBv1.0/) is a comprehensive database comprising 22,527 gene sets and 17,389 drug compounds, which are associated with 19,531 genes(31). In our study, we employed SMR analysis and Bayesian co-localization analysis to identify positive proteins linked to significant druggable genes associated with RA. We subsequently conducted mapping analyses with compounds from the DSigDB database to predict potential drug candidates and assess the pharmacological activity of the target genes. This approach enables us to leverage the gene-compound information within the DSigDB database to investigate prospective drugs related to these druggable genes, thereby enhancing our understanding of the pharmacological properties of these targets. Ultimately, this provides a robust data foundation for drug development and research into disease treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing a comprehensive analysis of the DSigDB database, we proceeded with the molecular docking process. This step aims to accurately assess the binding energy and interaction profiles of candidate drugs with their corresponding targets. Through rigorous screening and analysis of the docking results, we identified ligands exhibiting higher binding affinity and favorable interaction characteristics. Protein structure data were sourced from the Protein Data Bank (PDB, http://www.rcsb.org/). In instances where specific protein structure data were unavailable, we utilized an online tool developed by Yang Jianyi\u0026apos;s research group at Shandong University (https://yanglab.qd.sdu.edu.cn/) to construct the PDB format of the missing data. Drug structure data were acquired from the U.S. National Library of Medicine Chemical Substances Database (https://pubchem.ncbi.nlm.nih.gov/), where the required data was downloaded in SDF format(32). Subsequently, we employed the OpenBabel 3.1.1 software tool (https://github.com) to convert the data into PDB format. During the molecular docking phase, we selected key drugs with significant p-values along with the proteins encoded by their respective target genes, utilizing the computerized protein-ligand docking software AutoDock 4.2.6 (http://autodock.scripps.edu/) for the docking procedure. The binding energy was then analyzed and calculated. To provide a visual representation of the docking results, we employed PyMol 3.0.5 software (https://www.pymol.org/).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eMendelian randomization analysis results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe initially conducted a two-sample MR analysis. The results of the MR analysis concerning plasma proteins and the four distinct types of RA are presented in Appendix Tables S1-4.Figures 2-5 shows volcano plots of MR analysis of plasma proteins with four different types of RA.In our MR analysis of Icelandic plasma pQTLs and RA, we identified a total of 137 positive plasma proteins. In the MR analysis of Icelandic plasma pQTLs and seropositive RA, we found 150 positive plasma proteins. For seronegative RA, the MR analysis revealed 95 positive plasma proteins, while the analysis of juvenile RA identified 69 positive plasma proteins. In the UK Biobank MR analysis of plasma pQTLs and RA, we detected 156 positive plasma proteins. The analysis of seropositive RA in the UK Biobank revealed 167 positive plasma proteins. In contrast, the MR analysis of UK Biobank plasma pQTLs and seronegative RA identified 106 positive plasma proteins. Similarly, the analysis of juvenile RA in the UK Biobank also found 106 positive plasma proteins. Overall, we identified 81 positive plasma proteins.Figures 6-9 present circular heatmaps of plasma proteins and the positive results of four RA MR assays. A comparison of the positive MR results across the two datasets reveals that, in RA, the following proteins are common to both datasets: INSL5, SERPINB1, USP8, RELT, MSR1, ENPP6, GRAP2, CRYBB1, ST13, UROD, TGFBR3, SELE, MAPKAPK2, NSFL1C, CCL19, F2, CD86, IL1RN, FCRL1, and GFRAL. In seropositive RA, the common positive proteins identified include ENO2, GLB1, ANGPTL1, ARHGAP25, MSR1, GRAP2, RNASET2, ST13, BDNF, IL10RB, IL1R1, TGFBR3, SELE, FCRL3, CCL19, DBNL, MAPK13, F2, IL1RN, C3, TIMP4, SERPINA12, CD72, and CXCL9. For seronegative RA, the common positive proteins include PCOLCE, SMOC1, CRYBB1, ST13, RBP5, TNFRSF1A, KLK8, CHIT1, MAPKAPK2, NSFL1C, F2, SERPINF1, MAN2B2, FKBP7, and DNAJA4. In juvenile RA, the common positive proteins identified are STX8, CD59, MATN3, SERPINA3, CLEC1B, AFM, CD55, and SLAMF6. When comparing the different types of RA, we found that ST13 and F2 are common positive proteins across RA, seropositive RA, and seronegative RA. Additionally, MSR1, GRAP2, CRYBB1, MAPKAPK2, NSFL1C, and IL1RN are co-positive proteins found in both RA and seropositive RA.\u003c/p\u003e\n\u003cp\u003eAfter applying FDR correction, we identified several significant positive proteins in the MR analysis of plasma pQTLs related to RA in Iceland and the UK Biobank. Specifically, in the MR analysis for both populations, PPA2, JUND, AGER, F2, and PMEL emerged as significant positive proteins. Additionally, the following proteins were found to be significantly positive: AIF1, ARG2, ATP5IF1, CCL19, CDSN, CEP43, MXRA8, PADI2, RPA2, SLC16A1, TNF, and TNFRSF14. In the MR analysis focusing on plasma pQTLs and seropositive RA in Iceland, TGFBR3, FCGR3B, TIMP4, and PMEL were identified as significant positive proteins. For the UK Biobank\u0026apos;s MR analysis of plasma pQTLs and seropositive RA, the proteins AIF1, APOBR, ATP6V1G2, BCL2L15, C1QTNF6, CCL19, CD40, CDSN, CEP43, CX3CL1, FCGR2B, FCRL1, IL6R, MXRA8, TGFBR3, TNF, and TNFRSF14 were significantly positive. Lastly, in the MR analysis of plasma pQTLs associated with seronegative RA in the UK Biobank, AIF1, CEP43, and TNF were identified as significant positive proteins.\u003c/p\u003e\n\u003cp\u003eAfter applying the Bonferroni correction, AIF1, CCL19, CDSN, CEP43, and TNF emerged as significant positive proteins in the MR analysis of plasma pQTLs associated with RA in the UK Biobank. Furthermore, in the MR analysis of plasma pQTLs related to seropositive RA, AIF1, ATP6V1G2, BCL2L15, CCL19, CDSN, CEP43, IL6R, and TNF were identified as significantly positive proteins. In contrast, the MR analysis of plasma pQTLs concerning seronegative RA highlighted AIF1 and TNF as the only significantly positive proteins.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-gene SMR analysis results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our analysis of the SMR concerning Icelandic plasma pQTLs and RA, we identified a total of 28 positively associated genes. These genes include ACADVL, AGER, AP2A2, ARPC1B, B3GALT6, CBL, CFL2, CHI3L2, CRAT, FCRL1, GHR, HIBCH, JUND, KLKB1, LRP4, NMB, OAF, PDE4A, PMM1, PPID, RELT, SERPINB1, ST13, TAGLN2, TCEA2, TNFAIP3, UROD, and USP8.In our analysis of plasma pQTLs and serum-positive RA in Iceland, we identified a total of 34 positive genes, which include ACADVL, AGER, AP2A2, C5, CD14, CD48, CD72, CHI3L2, CYTL1, FCRL3, GHR, HIBCH, IL15RA, ISOC1, LRP4, MDH1, OAF, PDE4A, PMM1, POMGNT2, PPID, PSMB4, QPCTL, RNASET2, ROBO1, SERPINB1, SH3BGRL2, ST13, TAGLN2, TCEA2, TNFAIP3, TPI1, ULK3, and ZG16B.In our SMR analysis of plasma pQTLs and seronegative RA in Iceland, we identified a total of 21 positive genes, including AP2A2, B3GNT8, C4BPA, CBL, CHI3L2, CRAT, DLK2, ERBB3, G3BP1, KLKB1, LRP4, MAPKAPK2, MTHFD1, NDE1, OAF, PPID, SARS2, SNUPN, ST13, TAGLN2, and TCEA2.In the SMR analysis of Icelandic plasma pQTLs associated with juvenile RA, we identified a total of 15 positively correlated genes: ABLIM3, BCL10, BPNT1, CCS, CD59, HEXIM2, JUND, PTPN7, SLAMF6, SPOCK2, STX8, TBCE, TNFSF8, TXNDC12, and UBE2F.In the SMR analysis of plasma pQTLs and RA within the UK Biobank, we identified a total of 38 positive genes, which include ACOT13, ADAM15, AIF1, APOBR, ATP6V1G2, ATXN2L, BCL2L15, CD6, CD72, CHCHD6, DPP4, EPHB6, FCGR2B, FCRL1, HCG22, HDGF, IL6R, LSP1, PADI2, PARK7, PFKFB2, RABEP1, RARRES2, RELT, RNASET2, RPA2, SEMA4D, SERPINB1, SLC16A1, ST13, STX4, TEF, TGFB2, TNF, TNFRSF4, TNFRSF14, UROD, and USP8.In the SMR analysis of plasma pQTLs and serum-positive RA within the UK Biobank, we identified a total of 37 positive genes, which include ADAM15, APOBR, ATP6V1G2, BCL2L15, CCL5, CD6, CD72, DOK2, DTNB, FCGR2B, FCRL1, FCRL2, FCRL3, FDX1, FKBPL, IL6R, IMMT, KIAA0319, LSP1, PADI2, PARK7, PBXIP1, PFKFB2, PMVK, RABEP1, RNASET2, RPA2, SH2B3, SLC16A1, SPAG1, ST13, STX4, TEF, TGFB2, TNF, TNFRSF14, and TOR1AIP1.In the SMR analysis of plasma pQTLs and seronegative RA within the UK Biobank, we identified a total of 21 positive genes: AIF1, APOBR, BAG3, BCL2L15, CASP10, CD300E, FCRL1, FCRL3, GZMB, LSP1, LY75, MAPKAPK2, PDCD1, PFKFB2, PKD1, RNASET2, SEMA4D, SERPINB1, SHISA5, ST13, and TNF.In the UK Biobank plasma pQTLs and SMR analysis of juvenile RA, we identified a total of 12 positively associated genes: AIF1, ASAH2, BCAM, CD59, COQ7, HS6ST1, PFKFB2, SLAMF6, SORBS1, SPINK8, STX4, and STX8.Appendix Table S5-8 presents the results of the single-gene SMR analysis of plasma proteins across four distinct types of RA, while Figures 10-13 illustrates the forest plot depicting the positive outcomes of the single-gene SMR analysis. Upon comparing the positive SMR results from the two datasets, we identified several common positive genes in RA, namely FCRL1, RELT, SERPINB1, ST13, UROD, and USP8. In the case of seropositive RA, CD72, FCRL3, RNASET2, and ST13 emerged as common positive genes across both datasets. For seronegative RA, MAPKAPK2 and ST13 were identified as common positive genes in both datasets. Lastly, in juvenile RA, CD59, SLAMF6, and STX8 were found to be common positive genes across the datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBayesian colocalization analysis results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our previous MR analysis of Icelandic plasma pQTLs associated with RA and its three subtypes, we identified several positive genes through Bayesian colocalization analysis. Notably, F2 (PPH4 = 0.7678) was found to be significantly positively colocalized with RA. Additionally, FCGR3A (PPH4 = 0.9822) emerged as a significantly positively colocalized gene specifically in seropositive RA. Other genes, including AGER, ALDH2, C3, CCL19, DEF6, GRAMD1C, HIBCH, MLN, MTHFD1, PDE4A, and TNFSF14, exhibited moderate colocalization in RA. In the context of seropositive RA, AGER, CCL19, CCL22, F2, FCGR3B, HIBCH, IL15RA, LRP4, PDE4A, and TIMP4 were identified as moderately colocalized genes. Furthermore, ALDH2 and FCRLB were found to be moderately colocalized in seronegative RA.\u003c/p\u003e\n\u003cp\u003eIn the previous MR analysis of plasma pQTLs and RA within the UK Biobank, we identified several positive genes through Bayesian co-localization analysis. Notably, ATP5IF1 (PPH4 = 0.9298), CCL19 (PPH4 = 0.9762), CX3CL1 (PPH4 = 0.9463), HDGF (PPH4 = 0.8426), MXRA8 (PPH4 = 0.7889), and TNFRSF14 (PPH4 = 0.8016) emerged as significant positive co-localized genes associated with RA. Additionally, in the context of seropositive RA, we found ADAM15 (PPH4 = 0.9721), CCL19 (PPH4 = 0.9766), CX3CL1 (PPH4 = 0.9837), NFKBIE (PPH4 = 0.7643), and TNFRSF14 (PPH4 = 0.9552) to be significant positive co-localized genes. Furthermore, we identified a group of moderately co-localized genes in RA, which includes AIF1, ATP6V1G2, CCL21, CD72, CDSN, CEP43, COMMD1, CPTP, HCG22, ICAM3, MYDGF, PHLDB1, and TNF. In seropositive RA, the moderately co-localized genes are AIF1, ATP6V1G2, CCL21, CD72, CDSN, CEP43, F2, FCGR2B, FKBPL, ICAM3, KIAA0319, MXRA8, PHLDB1, SCGN, SH2B3, TIMP4, and TNF. In the seronegative category, AIF1, CDSN, CEP43, COMMD1, and TNF were identified as moderately co-localized genes. The results of the Bayesian co-localization analysis are presented in Appendix Tables S9-12, while Figures 14-27 illustrates the scatter plot of significant positive genes from the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLDSC analysis results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the genetic correlates of complex diseases and intricate gene signatures, we conducted a Bayesian colocalization analysis of significantly associated genes with RA, including seropositive RA, seronegative RA, and juvenile RA. Linkage Disequilibrium Score Regression (LDSC) analysis was performed, revealing significant genetic correlations between CCL19 (h2_p=2.37E-07) and RA (h2_p=1.14E-05), with a notable positive genetic correlation (rg = 0.4553, rg_se = 0.1085, rg_p = 0.0000). Similarly, the genetic correlations between TNFRSF14 (h2_p=7.34E-14) and RA were also significant, indicating a positive genetic correlation (rg = 0.1546, rg_se = 0.0694, rg_p = 0.0258). Furthermore, the genetic correlations between CCL19 (h2_p=2.37E-07) and seropositive RA (h2_p=9.32E-05) were significant, demonstrating a strong positive genetic correlation (rg = 0.5660, rg_se = 0.1149, rg_p = 0.0000). The genetic correlations between TNFRSF14 (h2_p=7.34E-14) and seropositive RA (h2_p=9.32E-05) were also significant, with a positive correlation noted (rg = 0.1878, rg_se = 0.0734, rg_p = 0.0105). The results of the LDSC analysis are detailed in Appendix Table S13.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults of whole-phenome group association analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a phenotype-wide association analysis between Bayesian co-localized significantly positive genes and the latest version 11 GWAS data released by the Finngen database. The results indicated that the F2 gene was associated with 172 diseases or phenotypes. Following FDR correction, we identified that the F2 gene is associated with venous, lymphatic, and lymph node diseases, including deep vein thrombosis of the lower extremities, pulmonary embolism, other forms of embolism and thrombosis, venous thromboembolism, non-infectious enteritis and colitis, RA, menorrhagia, and frequently occurring menstruation, which is significantly related to irregular menstruation. The ATP5IF1 gene was found to be associated with 203 diseases or phenotypes. After FDR correction, we determined that ATP5IF1 is strongly related to autoimmune diseases, hypertension, and RA. The CCL19 gene showed correlation with 195 diseases or phenotypes, and after FDR correction, we found that CCL19 is significantly correlated with RA and seropositive RA. The CX3CL1 gene was associated with 98 diseases or phenotypes, and after FDR correction, we observed that CX3CL1 is highly associated with systemic connective tissue diseases. Lastly, the HDGF gene was related to 120 diseases or phenotypes, and following FDR correction, we found that HDGF is significantly associated with cranial nerve diseases and hypertensive heart disease, among other conditions.The gene MXRA8 is associated with 230 diseases or phenotypes. Following false discovery rate (FDR) correction, we found that MXRA8 is significantly linked to allergic rhinitis, benign lipoma, Crohn\u0026apos;s disease, thyroid poisoning with diffuse goiter, blepharoptosis, and various eyelid diseases. Additionally, it shows strong associations with hypothyroidism, chronic sinusitis, nasal polyps, inflammatory bowel disease, ulcerative colitis, polyarthritis, RA, and pollen allergies. Similarly, the gene TNFRSF14 is associated with 203 diseases or phenotypes. After FDR correction, we identified that TNFRSF14 is significantly related to eosinophilic asthma, autoimmune diseases, body mass index, follicular lymphoma, primary lymphatic and hematopoietic malignancies, nasal polyps, dermatitis, eczema, and RA. The gene FCGR3A is associated with 87 diseases or phenotypes, while ADAM15 is correlated with 126 diseases or phenotypes. After FDR correction, we found that ADAM15 is highly correlated with benign tumors of the parotid gland, benign tumors of the major salivary gland, benign salivary gland tumors, and Sj\u0026ouml;gren\u0026apos;s syndrome. The gene NFKBIE is related to 121 diseases or phenotypes, and after FDR correction, it was found to be highly related to skin atrophic diseases and RA. Appendix Table S14 presents the MR-PheWas analysis results for significantly positive genes, and Figure 28 illustrates the Manhattan plot of the MR-PheWas analysis for these genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExpression status of genes in different tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe utilized the Human Protein Atlas to identify differences in the expression of significantly positive genes across human tissues. Previous studies have demonstrated that CCL19 is predominantly expressed in lymph nodes, with notable expression also observed in bone marrow, lymphoid tissues, tonsils, thymus, and adipose tissue. Conversely, TNFRSF14 is primarily expressed in the duodenum, while also showing significant expression in the small intestine, fallopian tube, lymph nodes, and liver. Figure 29 illustrates the chromosomal locations of the significantly positive genes identified in our analysis, whereas Figures 30 and 31 depict the tissue expression profiles of these genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein-protein interaction network construction results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe summarized the results of the SMR analysis and co-localization analysis of plasma proteins in relation to RA, including seropositive RA, seronegative RA, and juvenile RA, along with 153 drug target genes identified through STRING database analysis, to construct a PPI network. This network comprises 151 nodes and 289 edges representing the protein interaction pathways. The PPI network is illustrated in Appendix Table S15 and Figures 32-33.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO enrichment analysis and KEGG pathway analysis results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our investigation of the characteristics of 153 potential drug targets identified through SMR analysis and co-localization analysis concerning the association between plasma proteins and RA, we employed the GO framework for a systematic analysis. The results indicated that these targets are extensively distributed across 353 biological pathways and are primarily involved in several critical biological processes (BP). Notably, they were significantly associated with the positive regulation of cytokine production (GO:0001819), chemotaxis (GO:0006935), tropism (GO:0042330), and leukocyte migration (GO:0050900). Furthermore, at the molecular function (MF) level, these targets displayed specific pathways, including cytokine receptor binding (GO:0005126), cytokine activity (GO:0005125), and G protein-coupled receptor binding (GO:0001664), among others. In terms of cellular component (CC) localization, they are predominantly found on the exterior of the plasma membrane (GO:0009897), within membrane rafts (GO:0045121), and in membrane microdomains (GO:0098857). The results of the GO enrichment analysis are presented in Appendix Table S16 and Figure 34.\u003c/p\u003e\n\u003cp\u003eTo further elucidate the potential link between these drug targets and the treatment of RA, we conducted a KEGG pathway analysis. The results indicated that these targets were significantly enriched in specific core pathways, including cytokine-cytokine receptor interaction (hsa04060), viral protein interaction with cytokines and cytokine receptors (hsa04061), and tuberculosis (hsa05152). Appendix Table S17 and Figure 35 present the results of the KEGG pathway analysis, while Figure 36 illustrates the significantly correlated pathway diagrams derived from this analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCandidate drug prediction outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo predict potentially effective intervention drugs, we utilized DSigDB to generate a list of the top ten candidate drugs based on their P values and adjusted P values. The results indicated that methotrexate (CTD 00006299), Chongsulide (CTD 00005665), nitroglycerin (CTD 00006039), antimycin A (CTD 00005427), and fluoride (CTD 00005982) emerged as the five most significant drugs. Specifically, methotrexate showed a correlation with the genes IL15RA, ACADVL, CCL21, LSP1, F2, TNF, C3, C5, FCGR3B, CCL5, MAPKAPK2, CD48, CCL19, FCGR2B, KLKB1, PPID, and HIBCH. Chongsulide correlated with the genes C3, C5, TNF, and KLKB1; nitroglycerin was associated with the genes C3, ALDH2, CASP10, and TNF; antimycin A correlated with the genes CCL5, CD14, TNF, and IL6R; and fluoride was linked to the genes CCL21, TNFRSF14, TNFSF8, CCL19, and TNF. Appendix Table S18 presents the prediction results of drug candidates for significantly positive genes, while Appendix Table S19 displays the prediction results of drug candidates for RA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular docking results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we utilized positive genes identified through Bayesian co-localization as significantly positive target genes and employed AutoDock 4.2.6 software to analyze the top five candidate drugs. The candidate drugs derived from the significant positive gene analysis were associated with their respective binding sites and interactions between the proteins encoded by the corresponding genes. We calculated the binding energy for each interaction and successfully obtained effective docking results involving nine genes and drugs. Among all the docking results, the combination of ADAM15 with camptothecin exhibited the lowest binding energy value of -10.0 kcal/mol, while the pairing of TNFRSF14 with 7,12-dimethylbenzanthracene also demonstrated a binding energy value of -10.0 kcal/mol. Additionally, the combination of NFKBIE with Trichogenin showed a binding energy value of -8.8 kcal/mol, MXRA8 with 2,3,7,8-tetrachlorodibenzo-p-dioxin had a binding energy of -7.7 kcal/mol, and the combination of HDGF with troglitazone displayed a binding energy value of -7.5 kcal/mol. Furthermore, the pairing of F2 with hydrocortisone succinate resulted in a binding energy value of -7.2 kcal/mol, indicating the stability of its binding efficiency. Appendix Table S20 presents the binding energy scores from the molecular docking analysis, while Figures 37-58 illustrate the molecular docking diagrams corresponding to the higher binding energies.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eRheumatoid arthritis (RA) is a chronic autoimmune disease that affects multiple joints in the body. In this condition, an abnormal immune response leads to inflammation of the joint synovium, resulting in joint swelling and pain. Over time, this inflammation may also damage the cartilage and bone, contributing to joint dysfunction. Despite the availability of various treatments, patients still face challenges related to individual differences, uncertainty regarding treatment efficacy, side effects, and economic burdens. Looking ahead, advancements in genomics and biomarker research are anticipated to yield more precise treatment options for patients with RA. Our in-depth analysis has identified several genes, including FCGR3A, ADAM15, CCL19, CX3CL1, NFKBIE, TNFRSF14, F2, ATP5IF1, HDGF, and MXRA8, as potential key targets for RA treatment. Notably, the genes TNFRSF14 and CCL19, due to their high genetic correlation, may play significant roles in the pathogenesis of RA and its subtypes, making them promising areas for future research and therapeutic focus.\u003c/p\u003e\n\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe relationship between TNFRSF14 and rheumatoid arthritis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe gene encoding tumor necrosis factor receptor superfamily member 14 (TNFRSF14), also known as HVEM, encodes a protein that plays a crucial role in immune responses. This protein is essential for the activation and proliferation of lymphocytes. Studies have demonstrated that HVEM effectively regulates T cell immune responses by promoting inflammatory responses and transmitting inhibitory signals(\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e). The function of HVEM extends beyond serving as a receptor for LIGHT (lymphotoxin-like molecule) and lymphotoxin-alpha; it also interacts with immunoglobulin superfamily members BTLA and CD160, highlighting its versatile role in immune regulation. HVEM can bind to multiple ligands in various conformations, forming complex signaling networks that jointly regulate inflammatory and inhibitory responses. Abnormal expression of TNFRSF14 has been observed in approximately 40% of follicular lymphoma (FL) patients. HVEM inhibits T cell activation by interacting with receptors on both B cells and T cells, which is crucial for maintaining immune balance and preventing immune overreactions(\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e). Furthermore, the influence of HVEM extends beyond T cells and B cells to encompass a broader range of immune regulatory mechanisms. For instance, the interaction of HVEM with BTLA represents a novel link between immunoglobulin domains and TNFR family members, inhibiting T and B cell activation, proliferation, and cytokine production. As a transmembrane protein, HVEM contains multiple extracellular cysteine-rich domains (CRDs) that are essential for its function, enabling HVEM to bind to various ligands in different conformations and form functionally diverse and interconnected signaling pathways. These pathways collaboratively regulate inflammatory and inhibitory responses, thereby playing a pivotal role in the immune response.In summary, TNFRSF14 (HVEM) plays a crucial role in immune regulation. It participates in the regulation of T cell and B cell activation and proliferation, and also influences inflammatory responses and immunity through interactions with other immune molecules. These functions underscore the significant research value of TNFRSF14 in immunological studies and its potential clinical applications.\u003c/p\u003e\n \u003cp\u003eRA is a disease characterized by autoimmune features. The abnormal activation of B cells and T cells plays a critical role in the susceptibility to and development of the disease(\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e). A related study indicates that the role of B cells in RA extends beyond the production of autoantibodies; they also activate T cells through antigen presentation and are directly involved in the production of cytokines, which are integral to the inflammatory response. These processes are closely linked to the onset and progression of RA. Concurrently, T cells are also pivotal in RA, participating in its pathological processes by releasing pro-inflammatory cytokines or directly causing joint damage. The function of TNFRSF14 in regulating immune responses is likely related to the pathogenesis of RA, particularly in the activation of T and B cells and their immune responses. A study reveals that both HVEM and BTLA exhibit characteristic expression and distribution in the synovium of RA, suggesting that their signaling may influence the disease\u0026apos;s pathogenesis(\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e). Furthermore, a bioinformatics study identified TNFRSF14-MMEL1 at the 1p36 locus and IKZF3-ORMDL3-GSDMB at the 17q12 locus as genes most likely associated with RA(\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e). Genetic case-control studies also indicate that MMEL1/TNFRSF14 is significant in genetic susceptibility to RA(\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e). These results underscore the importance of TNFRSF14 in RA. Overall, these findings contribute valuable insights into the immunological basis of RA and may inform future therapeutic strategies targeting these immune cells and their interactions.\u003c/p\u003e\n \u003cp\u003eWe found that 7,12-dimethylbenzo[a]anthracene (DMBA) exhibits a strong binding affinity for TNFRSF14. As a potent carcinogen, DMBA not only directly damages genes but also regulates cellular proliferation, thereby promoting tumor growth. This process involves complex immune responses, including the proliferation of regulatory T cells, humoral immune responses, and cellular immune responses. One study demonstrated that DMBA can induce an increase in regulatory T cells. As the dosage of DMBA increases, the immune response weakens, and the proliferation of Treg cells leads to the establishment of an immunosuppressive state(\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e). Furthermore, DMBA has been shown to inhibit both cellular and humoral immunity, as confirmed in mouse models. In terms of humoral immunity, DMBA significantly reduces antibody production; experiments indicated that DMBA-treated mice produced considerably fewer antibodies than their normal counterparts following vaccination(\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e). Regarding cellular immunity, the cytotoxic T cell function in DMBA-treated mice was markedly diminished, with abnormal immune cell activation and reduced cytokine secretion, indicating a direct toxic effect of DMBA on immune cells. The immunosuppressive effect of DMBA correlates with its dosage; as the dose increases, the inhibitory effects on T cell proliferation, antibody production, and cytotoxic T cell function also intensify, demonstrating a dose-dependent relationship. Future research should further investigate the impact of DMBA on the body\u0026apos;s immune mechanisms and provide new strategies for the prevention and treatment of related diseases.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe relationship between CCL19 and rheumatoid arthritis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eCCL19, also known as macrophage inflammatory protein-3\u0026beta; (MIP-3\u0026beta;), is a member of the CC chemokine family and plays a significant role in immune regulation and various biological processes. This factor is crucial for the regulation and tissue localization of immune cells, particularly in maintaining the structure and function of lymphoid tissue. Research indicates that lymph node stromal cells (LN SCs) shape the microenvironment of lymph nodes by establishing a concentration gradient of CCL19 and CCL21, thereby guiding immune cells to migrate to specific areas(\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e). Furthermore, additional studies have found that during immune responses, the expression of CCL19 is closely associated with the activity of specific CD4\u0026thinsp;+\u0026thinsp;T cell subsets, particularly CD62Llow CD4\u0026thinsp;+\u0026thinsp;T cells(\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e). The involvement of CCL19 in numerous physiological and pathological processes positions it as a focal point for clinical research and the development of diagnostic tools. Monitoring changes in CCL19 levels can serve not only to assess the activity of immune-related diseases, such as autoimmune disorders and lymphoid tumors, but also to evaluate and monitor treatment responses and disease progression. In summary, CCL19, as a key molecule in immune regulation and signaling, plays a multifaceted role in immune response, anti-tumor defense, and central nervous system function. A deeper understanding of CCL19 and its associated signaling pathways reveals its complex biological mechanisms and extensive physiological effects, underscoring its significant role in immune diseases.\u003c/p\u003e\n \u003cp\u003eRA, an autoimmune disease, is associated with CCL19, a finding supported by multiple studies. CCL19, a B-cell chemokine, correlates with a decrease in memory B cells in the blood of RA patients and influences clinical responses to rituximab treatment(\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e). It significantly increases the expression of IL-1\u0026beta; and TNF-\u0026alpha; in synoviocytes, while also promoting IL-1\u0026beta; expression in peripheral blood mononuclear cells (PBMCs)(\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e). These cytokines, recognized as important pro-inflammatory mediators, activate a variety of immune cells, including synovial cells and macrophages, thereby triggering joint inflammation and tissue damage, which contributes to the onset and progression of RA. The interaction between CCL19 and its receptor CCR7 enhances its biological effects, potentially by promoting IL-1\u0026beta; expression and subsequently upregulating CCR7 function through IL-1\u0026beta;. The study also indicates that CCL19 regulates B cell recruitment in the RA synovium and is closely linked to joint and synovial destruction(\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e). In the early stages of RA, CCL19 expression is stimulated by TNF-\u0026alpha; and LT\u0026alpha;1\u0026beta;2, resulting in significant alterations in the lymph node microenvironment(\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e). Compared to osteoarthritis, CCL19 is expressed at higher levels in RA patients and plays a crucial role in promoting osteoclast migration and resorption(\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e). Clinical studies have demonstrated that CCL19 levels serve as disease markers and prognostic indicators of RA. ELISA testing reveals that CCL19 is highly expressed in RA patients, and after treatment with disease-modifying antirheumatic drugs (DMARDs), CCL19 levels significantly decrease(\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e). In summary, the elevated expression of CCL19 in RA, its interaction with inflammatory factors, and its correlation with clinical indicators underscore its key role in the pathogenesis of RA. These findings offer new insights into immunological research and potential treatment strategies for RA.\u003c/p\u003e\n \u003cp\u003eNitropyrene (1-NITROPYRENE) is a polycyclic aromatic hydrocarbon known for its strong carcinogenic and mutagenic properties. In response to external stimuli, the body\u0026apos;s macrophages, natural killer cells, and various other immune cells play crucial roles. Our findings indicate that CCL19 exhibits a high binding energy with nitropyrene. Furthermore, 1-Nitropyridine may exert immune effects by engaging in a series of cellular pathways. Previous studies have demonstrated that 1-Nitropyridine can significantly induce the expression of Cyp1a1 protein, which is vital for metabolic processes(\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e). This induction may substantially impact immune cell function and the inflammatory response. Additionally, nitropyridines are likely to play significant roles in inflammation, infection, and injury responses(\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e). 1-Nitropyridine may trigger chronic inflammatory reactions and influence immune function, which is closely associated with the development of chronic inflammation, autoimmune diseases, cancers, and other health issues. It may contribute to disease progression by promoting the release of inflammatory factors and exacerbating the immune system\u0026apos;s overreaction. Moreover, the study found that nitropyrene is linked to cellular senescence, as mice exposed to nitropyrene exhibited increased telomere damage and cell senescence in lung and alveolar epithelial cells(\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e). In summary, the relationship between nitropyridine and the immune system is complex, involving various aspects of both innate and adaptive immunity. It significantly affects the immune system by modulating macrophage function, inducing specific immune responses, and regulating intracellular signaling pathways.\u003c/p\u003e\n \u003cp\u003eThis study, while conducting rigorous data analysis and utilizing a series of GWAS data, inevitably has some limitations.This study primarily focuses on individuals of European ancestry; therefore, the applicability of its conclusions to other ethnic groups requires further in-depth analysis. This necessity arises from the differences in genetic structure, living environments, and other factors among various ethnic groups, which may influence the generalizability of the research findings. Additionally, during the initial data collection and sorting phase, the meta-analysis of differentially expressed genes in plasma proteins may utilize various data sources, including microarrays and batch RNA sequencing, each with differing sample sizes. Such variability can lead to discrepancies in results, as the differing data sources may employ distinct collection and analysis methods, potentially interfering with the accurate identification of differentially expressed genes. Furthermore, the expression of quantitative trait loci is subject to change as the disease progresses, influenced by variations in cell types. Expression quantitative trait loci derived from bulk RNA sequencing face significant limitations in elucidating key molecular mechanisms associated with diseases. Specifically, there are notable differences in the mechanisms of plasma protein expression in vivo versus in vitro; thus, in vitro data cannot be assumed to comprehensively represent all plasma protein functions. Additionally, the alterations in various cell types throughout disease progression may affect the identification of critical molecular mechanisms. Moreover, limited sample sizes and insufficiently balanced groupings may introduce bias. While high thresholds and multiple corrections strengthen the rigor of the analysis, they may also obscure true associations that lack statistical significance in smaller samples.Finally, minor effects of genetic variation may diminish statistical power and heighten the risk of false positives. Furthermore, the pathogenesis of the disease is intricate, involving genetics, environmental factors, and numerous unknown elements. There remains a critical need for large-scale, multi-center, and well-designed studies to address these gaps. Only through such research can we accurately elucidate the relationships among various factors and diseases, thereby providing a robust scientific foundation for prevention and treatment strategies.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThrough a series of analyses, including MR, SMR analysis, co-localization analysis, and linkage disequilibrium score regression (LDSC), this study identified significant correlations between F2, ATP5IF1, CCL19, CX3CL1, HDGF, MXRA8, TNFRSF14, and the onset and progression of RA. Additionally, FCGR3A, ADAM15, CCL19, CX3CL1, NFKBIE, and TNFRSF14 demonstrated significant associations with the onset and development of seropositive RA. Notably, CCL19 and TNFRSF14 emerged as positively correlated genes of genetic significance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor this study, all our GWAS data were derived from published statistical sources, thus negating the need for additional ethical approval.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors unanimously agreed to submit this research achievement for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll types of data involved in this study have clear sources for easy access. The complete GWAS information for UK Biobank plasma pQTLs can be downloaded directly from s3://ukbiobank.opendata.sagebase.org/. The GWAS data for Icelandic plasma proteins is available in the literature titled \u0026quot;Large-scale integration of the plasma proteome with genetics and disease,\u0026quot; which can be accessed at https://www.nature.com/articles/s41588-021-00978-w#Sec36. Complete GWAS data for RA, including seropositive and seronegative RA, as well as data for conditions such as ulcerative colitis, AS, MS, and PsA, can be downloaded from https://www.finngen.fi/en. Additionally, the complete GWAS data for juvenile RA can be obtained from https://www.nature.com/articles/s41588-021-00931-x. Other relevant data can be sourced from original literature and respective websites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW.Y. contributed to the conception and design of this study. Z.H.L. and M.C. provided the conceptualization ideas and analysis and supervised the whole research process. W.Y. conducted the data analysis. W.Y. authored the first draft, then W.Y., C.L.L. and M.C. revised and refined the manuscript. W.Y. and M.C. carried out the visualization analysis of the research results. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to the European Bioinformatics Center, the GWAS Catalog database, the FinnGen database, the British Biobank database, the IEU OpenGWAS database, the eqtlgen database, the GTEx database, the YANG LAB database, the Human Protein Atlas database, the STRING database, and the DSigDB database for providing shared data. We also extend our thanks to all data providers mentioned in this article and to the anonymous reviewers for their constructive comments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWei Yang\u003c/p\u003e\n\u003cp\u003ecorrespondence address：College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130117, Jilin, China.\u003c/p\u003e\n\u003cp\u003eEmail：[email protected]\u003c/p\u003e\n\u003cp\u003eChenlin Liu\u003c/p\u003e\n\u003cp\u003ecorrespondence address：College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130117, Jilin, China.\u003c/p\u003e\n\u003cp\u003eEmail：[email protected]\u003c/p\u003e\n\u003cp\u003e*Zhenhua Li2\u003c/p\u003e\n\u003cp\u003ecorrespondence address：The Affiliated Hospital of Changchun University of Chinese Medicine, Changchun 130117, Jilin, China. 3.School of Traditional Chinese Medicine,\u003c/p\u003e\n\u003cp\u003eEmail：[email protected]\u003c/p\u003e\n\u003cp\u003e*Miao Cui\u003c/p\u003e\n\u003cp\u003ecorrespondence address：School of Traditional Chinese Medicine, Capital Medical University, No.10, Xitoutiao, You\u0026apos;anmenwai, Fengtai District, 100069, Beijing Beijing, China.\u003c/p\u003e\n\u003cp\u003eEmail：[email protected]\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWaldner H. 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Beijing Da Xue Xue Bao Yi Xue Ban. 2016;48(4):667-71.\u003c/li\u003e\n\u003cli\u003eChu WC, Hong WF, Huang MC, Chen FY, Lin SC, Liao PJ, et al. 1-Nitropyrene stabilizes the mRNA of cytochrome P450 1a1, a carcinogen-metabolizing enzyme, via the Akt pathway. Chem Res Toxicol. 2009;22(12):1938-47. https://dx.doi.org/10.1021/tx900241g\u003c/li\u003e\n\u003cli\u003eTsai PK, Chen SP, Huang-Liu R, Chen CJ, Chen WY, Ng YY, et al. Proinflammatory Responses of 1-Nitropyrene against RAW264.7 Macrophages through Akt Phosphorylation and NF-\u0026kappa;B Pathways. Toxics. 2021;9(11). https://dx.doi.org/10.3390/toxics9110276\u003c/li\u003e\n\u003cli\u003eLi SR, Kang NN, Wang RR, Li MD, Chen LH, Zhou P, et al. ALKBH5 SUMOylation-mediated FBXW7 m6A modification regulates alveolar cells senescence during 1-nitropyrene-induced pulmonary fibrosis. J Hazard Mater. 2024;468:133704. https://dx.doi.org/10.1016/j.jhazmat.2024.133704\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Figures","content":"\u003cp\u003eFigures 1 to 58 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"rheumatoid arthritis, Mendelian randomization, druggable target genes, plasma proteins, genetic correlation","lastPublishedDoi":"10.21203/rs.3.rs-5510112/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5510112/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCurrently, the treatment and prevention of rheumatoid arthritis (RA) face significant challenges. In the pursuit of new therapeutic avenues, Mendelian randomization (MR) analysis has emerged as a crucial research method. Building on this, we conducted a comprehensive genome-wide analysis of MR of drug targets to identify potential therapeutic intervention points for RA.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethodS\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn this study, we constructed a comprehensive analytical framework aimed at identifying and validating potential biomarkers for RA. The framework begins with a two-sample MR study utilizing two large plasma protein datasets. Building upon this foundation, we conducted an in-depth exploration of the identified positive proteins using the summary data-based Mendelian randomization (SMR) method, combined with Bayesian co-localization analysis of coding genes. This approach allowed us to reveal RA multi-omics biomarkers, and we employed the LDSC analysis method to investigate the genetic correlation between the identified genes and complex diseases. Additionally, a phenome-wide association study (PheWAS) was performed on the positive genes mapped by the identified proteins, alongside an exploration of their expression in various tissues. Subsequently, we expanded our analysis to include protein-protein interaction (PPI) network analysis, gene ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Finally, we conducted drug prediction and molecular docking studies. The purpose of these comprehensive analytical methods is to thoroughly investigate the biological functions and mechanisms of action of these biomarkers, thereby providing a scientific basis for the development of more effective and targeted therapeutic drugs. Our findings encompass RA and its multiple subtypes, including seropositive RA, seronegative RA, and juvenile RA.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study presents a multidimensional analysis of plasma proteins in relation to RA and its subtypes. In the MR analysis of Icelandic plasma protein - Quantitative Trait Loci(pQTLs) associated with RA, the findings revealed 137, 150, 95, and 69 positive associations for RA, seropositive RA, seronegative RA, and juvenile RA, respectively. Additionally, the MR analysis of plasma pQTLs from the UK Biobank database identified 156, 167, 106, and 81 positive plasma proteins for the same conditions. After applying false discovery rate (FDR) correction, the MR analysis of plasma pQTLs and RA in Iceland identified PPA2, JUND, AGER, F2, and PMEL as significantly positive proteins. In the MR analysis of plasma pQTLs and RA within the UK Biobank database, the significantly positive proteins included AIF1, ARG2, ATP5IF1, CCL19, CDSN, CEP43, MXRA8, PADI2, RPA2, SLC16A1, TNF, and TNFRSF14. For the MR analysis of plasma pQTLs and seropositive RA in Iceland, TGFBR3, FCGR3B, TIMP4, and PMEL were identified as significantly positive proteins. In the UK Biobank MR analysis of plasma pQTLs and seropositive RA, the following proteins were significantly positive: AIF1, APOBR, ATP6V1G2, BCL2L15, C1QTNF6, CCL19, CD40, CDSN, CEP43, CX3CL1, FCGR2B, FCRL1, IL6R, MXRA8, TGFBR3, TNF, and TNFRSF14. The MR analysis of plasma pQTLs and seronegative RA in the UK Biobank identified AIF1, CEP43, and TNF as significantly positive proteins. Following Bonferroni correction, the MR analysis of UK Biobank plasma pQTLs and RA highlighted AIF1, CCL19, CDSN, CEP43, and TNF as significantly positive proteins. For seropositive RA in the UK Biobank, AIF1, ATP6V1G2, BCL2L15, CCL19, CDSN, CEP43, IL6R, and TNF were identified as significantly positive proteins. Lastly, the MR analysis of plasma pQTLs and seronegative RA in the UK Biobank confirmed AIF1 and TNF as significantly positive proteins.In the context of single-gene SMR analysis, the examination of Icelandic plasma pQTLs in relation to RA\u0026mdash;specifically seropositive RA, seronegative RA, and juvenile RA\u0026mdash;identified 28, 34, 21, and 15 positive plasma associations, respectively. For proteins, MR analysis of UK Biobank plasma pQTLs revealed 38, 37, 21, and 12 positive plasma proteins, respectively. Building on the findings from the previous two-sample MR analysis, Bayesian co-localization was subsequently performed. Among the Icelandic plasma pQTLs, F2 emerged as a significantly positive gene associated with RA. In the UK Biobank plasma pQTLs, the genes ATP5IF1, CCL19, CX3CL1, HDGF, MXRA8, and TNFRSF14 were identified as significantly positive. LDSC analysis demonstrated a significant positive genetic correlation between CCL19 and both RA and seropositive RA, as well as a significant positive genetic correlation between TNFRSF14 and both RA and seropositive RA. These results suggest that FCGR3A, ADAM15, CCL19, CX3CL1, NFKBIE, TNFRSF14, F2, ATP5IF1, HDGF, and MXRA8 may serve as key therapeutic targets for RA. Notably, TNFRSF14 and CCL19 warrant further investigation as important genes for understanding the pathogenesis and potential therapeutic strategies for RA and its subtypes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThrough a comprehensive analysis of plasma proteomic and transcriptomic data, we successfully identified key therapeutic targets for RA and its three clinical subtypes. Specifically, we identified FCGR3A, ADAM15, CCL19, CX3CL1, NFKBIE, TNFRSF14, F2, ATP5IF1, HDGF, and MXRA8 as potential therapeutic targets for RA. By integrating genetic relatedness scores, we further elucidated the significance of these findings. Notably, TNFRSF14 and CCL19 emerged as critical genes warranting in-depth exploration regarding the pathogenesis of RA and its subtypes, as well as their potential as therapeutic targets. These results provide a scientific basis for the development of new immunotherapy approaches, combination treatment regimens, or targeted intervention strategies, and are anticipated to advance research progress in the treatment of RA.\u003c/p\u003e","manuscriptTitle":"Integrative Analysis of Genetic, Proteomic, and Transcriptomic Data Reveals Novel Therapeutic Targets for Rheumatoid Arthritis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-14 06:36:24","doi":"10.21203/rs.3.rs-5510112/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":"35a0cead-8261-41ac-9dfc-abbf0a89b162","owner":[],"postedDate":"March 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45663577,"name":"Biological sciences/Genetics"},{"id":45663578,"name":"Biological sciences/Immunology"},{"id":45663579,"name":"Health sciences/Diseases"}],"tags":[],"updatedAt":"2025-03-14T06:36:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-14 06:36:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5510112","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5510112","identity":"rs-5510112","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}